Showing 1–50 of 111 results for author: Tan, S

Geometry-Encoded Multireceiver Fluorometry Enables Full-Range Nonlinear Quantification under the Inner Filter Effect

Authors: Shuiyi Tan, Nai-Quan Zhu, Olivier J. F. Martin, Yuchao Fu

Abstract: The inner filter effect (IFE) transforms the nominally linear fluorescence-concentration relationship into a geometry-dependent and often nonmonotonic response, resulting in reduced sensitivity, concentration ambiguity, and inaccurate underestimation at high optical densities. Here, we introduce a spatially encoded multireceiver fluorometric strategy that does not eliminate or correct the IFE, but… ▽ More The inner filter effect (IFE) transforms the nominally linear fluorescence-concentration relationship into a geometry-dependent and often nonmonotonic response, resulting in reduced sensitivity, concentration ambiguity, and inaccurate underestimation at high optical densities. Here, we introduce a spatially encoded multireceiver fluorometric strategy that does not eliminate or correct the IFE, but instead harnesses the spatial fluorescence attenuation induced by IFE as an additional quantitative encoding dimension. Fluorescence generated along the excitation axis is integrated over independently positioned receiver windows, and concentration is recovered by nonlinear optimization of the joint fluorescent intensity vector. Tryptophan was selected as a biomedically relevant model fluorophore to validate the proposed strategy. Single-window calibration exhibited vanishing-gradient boundary and two-valued concentration inversions, whereas two spatially separated receiver windows restored global identifiability across the full concentration range. Screening of 35 two-receiver geometries identified the minimum-uncertainty configuration, achieving an average 95% error half-width of mean E_95= 0.884 mg/L, and the maximum-sensitivity configuration, reaching a noise-normalized response sensitivity of mean S_N=3.832 a.u./(mg/L). By converting spatial attenuation into a multidimensional concentration coordinate, this approach extends quantitative fluorescence analysis without dilution, a separate absorbance measurement, or piecewise calibration and provides a general metrology framework for fluorescence metrology even under strong IFE conditions. △ Less

Submitted 19 August, 2026; originally announced August 2026.

Comments: 13 pages, 5 figures

Poverty Mapping: Data, Models and Applications

Authors: Suoyi Tan, Mengning Wang, Yixiu Kong, Huimin Bai, Jianguo Liu, Dirk Brockmann, Yicheng Zhang, Xin Lu

Abstract: Poverty mapping is increasingly important for monitoring Sustainable Development Goal 1 (SDG 1) of the United Nations 2030 Agenda, which aims to end poverty in all its forms everywhere. Yet timely and fine-resolution poverty estimation remains difficult because conventional census- and survey-based approaches are costly, infrequent, and often sparse precisely where deprivation is most severe. As p… ▽ More Poverty mapping is increasingly important for monitoring Sustainable Development Goal 1 (SDG 1) of the United Nations 2030 Agenda, which aims to end poverty in all its forms everywhere. Yet timely and fine-resolution poverty estimation remains difficult because conventional census- and survey-based approaches are costly, infrequent, and often sparse precisely where deprivation is most severe. As poverty emerges from complex socioeconomic systems shaped by human mobility, social interactions, infrastructure, and economic activities, emerging computational methods and nontraditional data sources have created new opportunities for poverty estimation and mapping. At the intersection of statistical physics, complex systems science, and data science, these approaches enable poverty estimation at finer spatial and temporal resolutions. This review summarizes the main concepts of poverty and the principal frameworks used to measure it, and examines recent advances on poverty estimation and mapping using satellite imagery, mobile phone data, social media data, and multisource data fusion. The review also discusses persistent challenges related to representativeness, transferability across regions, interpretability, and uncertainty quantification. Finally, the review clarifies both the analytical promise and the practical limits of contemporary poverty mapping. △ Less

Submitted 31 July, 2026; originally announced July 2026.

Machine Learning-Driven Design of Mixed-Pitch Grating Couplers for Co-Packaged Optics Applications

Authors: Yu Dian Lim, Yun Da Chua, Wai Cheung Ma, Yeow Kheng Lim, Chuan Seng Tan

Abstract: A mixed-pitch grating coupler which can couple a wide range of wavelengths is preferred in its application in co-packaged optics (CPO). However, the design and optimization of such grating coupler is complex. In this work, we developed software with integrated deep neural network (DNN) model to automatically design the mixed-pitch grating coupler from user-specified peak wavelengths and full-width… ▽ More A mixed-pitch grating coupler which can couple a wide range of wavelengths is preferred in its application in co-packaged optics (CPO). However, the design and optimization of such grating coupler is complex. In this work, we developed software with integrated deep neural network (DNN) model to automatically design the mixed-pitch grating coupler from user-specified peak wavelengths and full-width half-maximum (FWHM) values. We first trained the DNN model with 10,000 rows of grating parameters-power spectrum datasets, where the power spectrum was simulated using finite-difference time domain (FDTD) technique. Upon training, we tested the model using ~1,000 different combinations of peak wavelengths and FWHM values. Among the combinations, 822 attempts have <15% error, while 351 attempts have <5% error when comparing the user-specified and FDTD-verified spectrum. Meanwhile, comparing the user-specified and FDTD-verified peak wavelengths, 844 attempts have peak wavelengths with absolute error (AE) < 2 nm. For FWHMs, 738 attempts have FWHM values with AE < 10 nm. We have also developed a graphical-user interface (GUI) to ease the usage of this software. △ Less

Submitted 16 July, 2026; originally announced July 2026.

Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor

Authors: Pauline Mathiot, Elio Garnaoui, Axel-Ugo Leriche, Evan Philip, Boris Albrecht, Clémence Briosne-Fréjaville, Lorenzo Cardarelli, Antoine Cornillot, Gwennolé Cournez, Luc Couturier, Julius De Hond, Rebecca El Koussaifi, Thomas Eritzpokoff, Florian Fasola, Antonio Andrea Gentile, Casper Gyurik, Clotilde Hamot, Loïc Henriet, Gaétan Hercé, Michael Kaicher, Lucas Lassablière, François-Marie Le Régent, Edgar Leroux, Yohann Machu, Hadriel Mamann , et al. (15 additional authors not shown)

Abstract: We report on a performance comparison between physical and logical computations on a prototypical machine-learning application: solving differential equations using quantum kernel methods. The algorithm is implemented on an atom-based logical quantum processor, both at the physical and logical levels. We show that the kernel estimated from the logical implementation performs better than its physic… ▽ More We report on a performance comparison between physical and logical computations on a prototypical machine-learning application: solving differential equations using quantum kernel methods. The algorithm is implemented on an atom-based logical quantum processor, both at the physical and logical levels. We show that the kernel estimated from the logical implementation performs better than its physical counterpart on relevant metrics. We observe how such performance improvement can be traced back to specific noise-induced errors detected by the chosen encoding. We apply the computed quantum kernel to the task of solving differential equations, confirming how the superior performance of a logical quantum kernel is retained also at an end-to-end applicative level. Our findings show that experimental validation of end-to-end protocols can already highlight the positive impact of fault-tolerant implementations despite their higher quantum resource count, and guide application-informed architectural choices. △ Less

Submitted 20 May, 2026; originally announced May 2026.

Comments: PM, EG, AL and EP contributed equally

THEMol dataset: Torsion, Hessian, and Energy of Molecules

Authors: Jiashu Liang, Tianze Zheng, Yu Xia, Xingyuan Xu, Xu Han, Zhi Wang, Siyuan Liu, Ailun Wang, Yu Liu, Shiqian Tan, Dongfei Liu, Zhichen Pu, Yuanheng Wang, Qiming Sun, Xiaojie Wu, Wen Yan

Abstract: We present THEMol (Torsion, Hessian, Energy of Molecules), a massive open-source collection of quantum mechanical properties tailored for closed-shell organic molecules, with up to 50 heavy atoms. THEMol includes a Hessian subset with more than 3 million relaxed geometries with Hessian matrices, a TorsionScan subset with nearly 100 million constrained relaxed geometries with energies and forces, a… ▽ More We present THEMol (Torsion, Hessian, Energy of Molecules), a massive open-source collection of quantum mechanical properties tailored for closed-shell organic molecules, with up to 50 heavy atoms. THEMol includes a Hessian subset with more than 3 million relaxed geometries with Hessian matrices, a TorsionScan subset with nearly 100 million constrained relaxed geometries with energies and forces, and relaxation-trajectory subsets (HessianRelax and TorsionScanRelax) that together comprise about 3 billion DFT calculations. The chemical space sampling is comprehensive, spanning twelve essential elements and diverse molecular architectures relevant to drug discovery, electrolytes, ionic liquids, and beyond. The dataset also features exhaustive conformational sampling through the TorsionScan and TorsionScanRelax subsets, including comprehensive in-ring and non-ring torsional scans. Furthermore, it contains an extensive library of Hessian matrices, computed at relaxed geometries, to capture critical second-derivative information of the potential energy landscape. Additionally, we supply electron density-derived atomic multipoles computed via the Minimal Basis Iterative Stockholder partition scheme. Organized into five distinct subsets (Hessian, TorsionScan, HessianRelax, TorsionScanRelax, and MBIS), the data encompasses optimized geometries, relaxation trajectories, and derived molecular properties. We anticipate that this massive and diverse dataset will significantly empower the development of highly accurate and transferable molecular potentials. △ Less

Submitted 14 May, 2026; originally announced May 2026.

UWB-Fat: Non-Intrusive Body Fat Measurement Using Commodity Ultra-Wideband Radar

Authors: Haotang Li, Yili Ren, Zhenyu Qi, Sen He, Kebin Peng, Sheng Tan, Bo Liu, Jiyue Zhao, Zi Wang

Abstract: Body fat percentage and its spatial distribution are clinically important health indicators. However, existing measurement methods often impose a tradeoff between accuracy and accessibility. Clinical-grade techniques, such as Dual-Energy X-ray Absorptiometry (DEXA) and hydrostatic weighing, provide accurate measurements but require specialized equipment and trained operators, making them difficult… ▽ More Body fat percentage and its spatial distribution are clinically important health indicators. However, existing measurement methods often impose a tradeoff between accuracy and accessibility. Clinical-grade techniques, such as Dual-Energy X-ray Absorptiometry (DEXA) and hydrostatic weighing, provide accurate measurements but require specialized equipment and trained operators, making them difficult to access and unsuitable for everyday use. In contrast, consumer-level methods, such as Bioelectrical Impedance Analysis (BIA) smart scales and skinfold calipers, are more accessible but typically provide only coarse-grained estimates, are prone to user error, or require intrusive physical contact. In this work, we present UWB-Fat, the first system that leverages commodity ultra-wideband (UWB) radar to enable non-intrusive, accessible, and accurate caliper-equivalent skinfold thickness estimation, serving as a convenient replacement for the skinfold caliper. UWB-Fat collects UWB signal at specified body sites non-intrusively without operator assistance. It extracts body-composition-related features from UWB signals by exploiting dielectric contrasts among skin, fat, and muscle tissues. Then, it uses a physics-inspired model to estimate site-specific skinfold thickness. We evaluate UWB-Fat on 15 participants, achieving a root mean square error of 0.63~mm for pooled-site subcutaneous fat thickness. These results highlight the potential of UWB-Fat to support low-cost, self-administered, and everyday body fat monitoring. △ Less

Submitted 8 May, 2026; originally announced May 2026.

Probing Coronal Activity Using Radio Signals Based on the 2021 superior conjunction of Mars: the Downlink Data from Tianwen-1

Authors: Yu-Chen Liu, De-Qing Kong, Song Tan, Zi-Han Zhao, Zan Wang, Dong-Hao Liu, Xin-Ying Zhu, Yan Su, Hong-Bo Zhang

Abstract: During the first superior conjunction of the Tianwen-1 Mars probe in October 2021, its downlink signal received by the Wuqing 70-m radio telescope passed within 4.53 solar radii of the Sun. The signal was significantly perturbed by the solar wind, providing a mechanism to probe coronal activity. We analyze the Doppler frequency scintillation spectrum of the solar wind within 10 solar radii to deri… ▽ More During the first superior conjunction of the Tianwen-1 Mars probe in October 2021, its downlink signal received by the Wuqing 70-m radio telescope passed within 4.53 solar radii of the Sun. The signal was significantly perturbed by the solar wind, providing a mechanism to probe coronal activity. We analyze the Doppler frequency scintillation spectrum of the solar wind within 10 solar radii to derive a characteristic frequency scintillation parameter. Statistical analysis indicates this parameter increases as the signal path approaches the Sun, with notable anomalies observed on October 5, 13, and 15. Comparisons with SOHO and SDO data reveal strong spatio-temporal correlations between these scintillation anomalies and coronal activity. We demonstrate that this parameter effectively identifies solar phenomena, including coronal streamers, high-speed solar wind, and coronal mass ejections (CMEs). Quantitative analysis confirms a distinct temporal correlation and delay between frequency scintillation and solar wind speed changes, validating the feasibility of spatially localizing solar activity. △ Less

Submitted 15 April, 2026; originally announced April 2026.

SJET: An Interactive Solar Jet Extraction Tool

Authors: Song Tan, Alexander Warmuth, Frédéric Schuller, Yuandeng Shen, Yue Fang, Jake A. J. Mitchell, Zedong Liu

Abstract: Solar jets are dynamic collimated plasma flows in the solar atmosphere that play crucial roles in coronal heating and solar wind acceleration. Their complex and diverse morphologies pose significant challenges for developing universal algorithms for automatic identification and extraction, particularly for on-disk jets affected by projection effects and background contamination. We present SJET, a… ▽ More Solar jets are dynamic collimated plasma flows in the solar atmosphere that play crucial roles in coronal heating and solar wind acceleration. Their complex and diverse morphologies pose significant challenges for developing universal algorithms for automatic identification and extraction, particularly for on-disk jets affected by projection effects and background contamination. We present SJET, an interactive tool for solar jet feature extraction using multiple algorithms developed in Python that integrates five thresholding algorithms with morphological operations. SJET implements a novel method for identifying start and end points based on circular regions that objectively determines jet propagation direction by exploiting morphological asymmetry, combined with modeling the axis using quadratic Bézier curves for accurate extraction of geometric parameters including length, width, curvature, and deflection angles. Validation analyses using Solar Orbiter/EUI high-resolution image and SDO/AIA observations demonstrate SJET's effectiveness across different observational conditions, with good agreement compared to traditional analysis methods, though the tool's accuracy remains dependent on user-defined threshold parameters and region of interest selection. SJET provides a solution to method inconsistency in solar jet research through standardized processing workflows, establishing a technical foundation for large-sample statistical studies. △ Less

Submitted 15 April, 2026; originally announced April 2026.

Comments: Accepted for publication in RAS Techniques and Instruments. Source code available at https://github.com/songsolarphysics/SJET

Defect-free arrays at the thousand-atom scale in a 4-K cryogenic environment

Authors: Desiree Lim, Hadriel Mamann, Grégoire Pichard, Lilian Bourachot, Arvid Lindberg, Clotilde Hamot, Hugo Le Bars, Florian Fasola, Siddhy Tan, Gwennolé Cournez, Sylvain Dutartre, Thierry Cartry, Sylvain Lemettre, Richard Hostein, Julien Paris, Franck Ferreyrol, Andréa Collardey, Adrien Signoles, Thierry Lahaye, Corentin Monmeyran, Bruno Ximenez

Abstract: We report on a cryogenic platform at 4 K incorporating high numerical aperture optics for the generation of large-scale tweezers arrays, and compatible with Rydberg-state manipulation. We achieve trapping lifetimes of around 5000 s, significantly extending the available experimental time for the preparation of large-scale arrays. By combining two trapping lasers at different wavelengths and by min… ▽ More We report on a cryogenic platform at 4 K incorporating high numerical aperture optics for the generation of large-scale tweezers arrays, and compatible with Rydberg-state manipulation. We achieve trapping lifetimes of around 5000 s, significantly extending the available experimental time for the preparation of large-scale arrays. By combining two trapping lasers at different wavelengths and by minimizing other atom losses during the rearrangement and imaging processes, we demonstrate the preparation of defect-free arrays with up to 1024 atoms. Our cryogenic design opens exciting prospects for analog and digital quantum computing. △ Less

Submitted 8 April, 2026; originally announced April 2026.

Comments: 8 pages, 5 figures

Uncertainty quantification in neural network-based glucose prediction for diabetes

Authors: Hai Siong Tan, Rafe McBeth

Abstract: In this work, we investigate uncertainty-aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 diabetes. We consider three families of sequence models based on LSTM, GRU, and Transformer architectures, with uncertainty quantification enabled by either Monte Carlo dropout or through evidential output layers compatible with Deep Evidential Regre… ▽ More In this work, we investigate uncertainty-aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 diabetes. We consider three families of sequence models based on LSTM, GRU, and Transformer architectures, with uncertainty quantification enabled by either Monte Carlo dropout or through evidential output layers compatible with Deep Evidential Regression. Using the HUPA-UCM diabetes dataset for validation, we find that Transformer-based models equipped with evidential output heads provide the most effective uncertainty-aware framework, achieving consistently higher predictive accuracies and better-calibrated uncertainty estimates whose magnitudes significantly correlate with prediction errors. We further evaluate the clinical risk of each model using the recently proposed Diabetes Technology Society error grid, with risk categories defined by international expert consensus. Our results demonstrate the value of integrating principled uncertainty quantification into real-time machine-learning-based blood glucose prediction systems. △ Less

Submitted 27 March, 2026; v1 submitted 5 March, 2026; originally announced March 2026.

Comments: 20 pages, 7 figures; v2: minor revisions with PR-AUC curves included in result analysis. Code available at https://github.com/HaiSiong-Tan/Uncertainty_aware_glucose_prediction

Single-Emitter Spectra from an Ensemble

Authors: Jonah R. Horowitz, Oliver J. Tye, Oliver M. Nix, Shaun Tan, Hogeun Chang, Ji Hyun Min, Taehyung Kim, Moungi G. Bawendi

Abstract: The heterogeneity in nanoscale emitters hinders efforts to understand their basic photophysics and limits their use in practical applications. Existing methods have difficulty accurately characterizing single-emitter spectra and optical heterogeneity on a statistical scale. Here, we introduce SPICEE (SPectrally Imbalanced Correlations from Ensemble Emission), a spectrally filtered photon-correlati… ▽ More The heterogeneity in nanoscale emitters hinders efforts to understand their basic photophysics and limits their use in practical applications. Existing methods have difficulty accurately characterizing single-emitter spectra and optical heterogeneity on a statistical scale. Here, we introduce SPICEE (SPectrally Imbalanced Correlations from Ensemble Emission), a spectrally filtered photon-correlation technique that recovers single-particle emission lineshapes from an ensemble sample. Analytical derivations, numerical modeling, and experiments on a solution ensemble of emitters validate the technique. We apply SPICEE to blue-emitting ZnSeTe semiconductor nanocrystals relevant to display applications and find that the low color purity in the ensemble spectrum is primarily caused by a small subpopulation of nanocrystals with a distinct emission mechanism. This work demonstrates that SPICEE is a powerful high-throughput tool for accurately characterizing the single-emitter properties of nanoscale systems. △ Less

Submitted 11 February, 2026; v1 submitted 2 February, 2026; originally announced February 2026.

Comments: 10 pages in main text, 4 figures. JRH and OJT contributed equally to this work

Three-body scattering area of identical bosons in two dimensions

Authors: Junjie Liang, Hongye Yu, Shina Tan

Abstract: We study the wave function $φ^{(3)}$ of three identical bosons scattering at zero energy, zero total momentum, and zero orbital angular momentum in two dimensions, interacting via short-range potentials with a finite two-body scattering length $a$. We derive asymptotic expansions of $φ^{(3)}$ in two regimes: the 111-expansion, where all three pairwise distances are large, and the 21-expansion, whe… ▽ More We study the wave function $φ^{(3)}$ of three identical bosons scattering at zero energy, zero total momentum, and zero orbital angular momentum in two dimensions, interacting via short-range potentials with a finite two-body scattering length $a$. We derive asymptotic expansions of $φ^{(3)}$ in two regimes: the 111-expansion, where all three pairwise distances are large, and the 21-expansion, where one particle is far from the other two. In the 111-expansion, the leading term grows as $\ln^3(B/a)$ at large hyperradius $B=\sqrt{(s_1^2+s_2^2+s_3^2)/2}$. At order $B^{-2}\ln^{-3}(B/a)$, we identify a three-body parameter $D$ with dimension of length squared, which we term the three-body scattering area. This quantity should be contrasted with the three-body scattering area previously studied for infinite or vanishing two-body scattering length. If the two-body interaction is attractive and supports bound states, $D$ acquires a negative imaginary part, and we derive its relation to the probability amplitudes for the production of two-body bound states in three-body collisions. Under weak modifications of the interaction potentials, we derive the corresponding shift of $D$ in terms of $φ^{(3)}$ and the changes of the two-body and three-body potentials. We also study the effects of $D$ and $φ^{(3)}$ on three-body and many-body physics, including the three-body ground-state energy in a large periodic volume, the many-body energy and the three-body correlation function of the dilute two-dimensional Bose gas, and the three-body recombination rates of two-dimensional ultracold atomic Bose gases. △ Less

Submitted 28 January, 2026; originally announced January 2026.

Comments: 18 pages, 2 figures

Ab initio quantum embedding at finite temperature with density matrix embedding theory

Authors: Laurence Giordano, Y. Stanley Tan, Zhi-Hao Cui, Chong Sun

Abstract: We present a finite-temperature extension of density matrix embedding theory (FT-DMET) for realistic crystalline systems. We describe a practical framework for constructing extended bath orbitals, solving the embedding problem, and performing DMET self-consistency at finite temperature. To reduce computational cost, we introduce strategies based on mutual-information-guided bath truncation, contro… ▽ More We present a finite-temperature extension of density matrix embedding theory (FT-DMET) for realistic crystalline systems. We describe a practical framework for constructing extended bath orbitals, solving the embedding problem, and performing DMET self-consistency at finite temperature. To reduce computational cost, we introduce strategies based on mutual-information-guided bath truncation, controlled treatment of the thermal electron number without explicit optimization, and the use of low-temperature impurity solvers and one-shot FT-DMET in the low-temperature regime. We apply this approach to periodic hydrogen chains and square lattices to characterize their finite-temperature phases. We observe the Pomeranchuk-like effect in one dimension and enhanced stability of long-range order in two dimensions. △ Less

Submitted 10 July, 2026; v1 submitted 4 January, 2026; originally announced January 2026.

Entropy-Driven Sensor Deployment and Source Detection in Hypergraphs

Authors: Qiao Ke, Chengjun Zhang, Chuang Liu, Mingxia Jing, Suoyi Tan, Xiu-Xiu Zhan

Abstract: Identifying the diffusion source in complex networks is critical for understanding and controlling epidemic spread. In realistic settings, full observation of node states is rarely available, making sensor-based source detection a practical alternative. However, existing sensor-based methods are often confined to simple networks, failing to capture the higher-order group dynamics of real-world spr… ▽ More Identifying the diffusion source in complex networks is critical for understanding and controlling epidemic spread. In realistic settings, full observation of node states is rarely available, making sensor-based source detection a practical alternative. However, existing sensor-based methods are often confined to simple networks, failing to capture the higher-order group dynamics of real-world spreading process. By deploying a limited number of sensors to monitor the diffusion process, one can infer the origin from partial observations. Yet, determining optimal sensor placement is challenging, i.e., poor deployment leads to redundant or noisy data, while optimal placement must balance coverage diversity and information value under limited resources. To address these challenges, we propose a dedicated framework termed Sensor-based Source Detection in Hypergraphs (SSDH). Specifically, we introduce a novel entropy-driven sensor deployment strategy that effectively captures critical early-stage diffusion signals by maximizing information gain under limited resources. Furthermore, we develop a source localization algorithm that quantifies propagation uncertainty through a newly defined path uncertainty-based score. By integrating this score with topological distance, SSDH enables accurate and robust source identification. Extensive experiments on both synthetic and empirical hypergraphs demonstrate that SSDH consistently outperforms competing algorithms by 5%--30% across different sensor ratios, final spreading ratios, and infection probabilities. These results validate the effectiveness of SSDH and highlight its superior capability to tackle source localization in complex systems characterized by higher-order interactions. △ Less

Submitted 30 November, 2025; originally announced December 2025.

AI-Designed Photonics Gratings with Experimental Verification

Authors: Yu Dian Lim, Chuan Seng Tan

Abstract: Artificial Intelligence (AI) software based on transformer model is developed to automatically design gratings for possible integrations in ion traps to perform optical addressing on ions. From the user-defined (x,z) coordinates and full-width half-maximum (FWHM) values, the AI software can automatically generate the Graphic Design System (GDS) layout of the grating that shoots light towards the p… ▽ More Artificial Intelligence (AI) software based on transformer model is developed to automatically design gratings for possible integrations in ion traps to perform optical addressing on ions. From the user-defined (x,z) coordinates and full-width half-maximum (FWHM) values, the AI software can automatically generate the Graphic Design System (GDS) layout of the grating that shoots light towards the pre-defined (x,z) coordinates with built-in finite-difference time-domain (FDTD) simulation for performance verification. Based on the FDTD verification, AI-design gratings produced grating-to-free-space light that shoots towards the provided (x,z) target with < 2 micron deviations. For most attempts, the FWHM of FDTD simulation has < 2 micron deviations from the user-defined FWHM. The AI-designed gratings were successfully taped out and capable of producing output light for possible optical addressing of trapped ions. △ Less

Submitted 26 November, 2025; v1 submitted 25 November, 2025; originally announced November 2025.

Predicting Healthcare Provider Engagement in SMS Campaigns

Authors: Daanish Aleem Qureshi, Rafay Chaudhary, Kok Seng Tan, Or Maoz, Scott Burian, Michael Gelber, Phillip Hoon Kang, Alan George Labouseur

Abstract: As digital communication grows in importance when connecting with healthcare providers, traditional behavioral and content message features are imbued with renewed significance. If one is to meaningfully connect with them, it is crucial to understand what drives them to engage and respond. In this study, the authors analyzed several million text messages sent through the Impiricus platform to lear… ▽ More As digital communication grows in importance when connecting with healthcare providers, traditional behavioral and content message features are imbued with renewed significance. If one is to meaningfully connect with them, it is crucial to understand what drives them to engage and respond. In this study, the authors analyzed several million text messages sent through the Impiricus platform to learn which factors influenced whether or not a doctor clicked on a link in a message. Several key insights came to light through the use of logistic regression, random forest, and neural network models, the details of which the authors discuss in this paper. △ Less

Submitted 20 November, 2025; originally announced November 2025.

Evidential Physics-Informed Neural Networks for Scientific Discovery

Authors: Hai Siong Tan, Kuancheng Wang, Rafe McBeth

Abstract: We present the fundamental theory and implementation guidelines underlying Evidential Physics-Informed Neural Network (E-PINN) -- a novel class of uncertainty-aware PINN. It leverages the marginal distribution loss function of evidential deep learning for estimating uncertainty of outputs, and infers unknown parameters of the PDE via a learned posterior distribution. Validating our model on two il… ▽ More We present the fundamental theory and implementation guidelines underlying Evidential Physics-Informed Neural Network (E-PINN) -- a novel class of uncertainty-aware PINN. It leverages the marginal distribution loss function of evidential deep learning for estimating uncertainty of outputs, and infers unknown parameters of the PDE via a learned posterior distribution. Validating our model on two illustrative case studies -- the 1D Poisson equation with a Gaussian source and the 2D Fisher-KPP equation, we found that E-PINN generated empirical coverage probabilities that were calibrated significantly better than Bayesian PINN and Deep Ensemble methods. To demonstrate real-world applicability, we also present a brief case study on applying E-PINN to analyze clinical glucose-insulin datasets that have featured in medical research on diabetes pathophysiology. △ Less

Submitted 7 December, 2025; v1 submitted 17 September, 2025; originally announced September 2025.

Comments: v3: minor revisions. To appear in TAAI 2025. Code available at https://github.com/HaiSiong-Tan/E-PINN

Oxygen vacancy formation in ZnSeTe blue quantum dot light-emitting diodes

Authors: Shaun Tan, Sujin Park, Seung-Gu Choi, Oliver J. Tye, Ruiqi Zhang, Jonah R. Horowitz, Heejae Chung, Vladimir Bulović, Jeonghun Kwak, Jin-Wook Lee, Taehyung Kim, Moungi G. Bawendi

Abstract: Recent advancements have led to the development of bright and heavy metal-free blue-emitting quantum dot light-emitting diodes (QLEDs). However, consensus understanding of their distinct photophysical and electroluminescent dynamics remains elusive. This work correlates the chemical and electronic changes occurring in a QLED during operation using depth-resolved and operando techniques. The result… ▽ More Recent advancements have led to the development of bright and heavy metal-free blue-emitting quantum dot light-emitting diodes (QLEDs). However, consensus understanding of their distinct photophysical and electroluminescent dynamics remains elusive. This work correlates the chemical and electronic changes occurring in a QLED during operation using depth-resolved and operando techniques. The results indicate that oxygen vacancy forms in the ZnMgO layer during operation, with important implications on the charge injection and electrochemical dynamics. Taken together, the results suggest a causal relationship between oxygen vacancy formation and operational degradation of the blue-emitting ZnSeTe-based QLEDs. △ Less

Submitted 15 September, 2025; originally announced September 2025.

Morphological and Chemical Changes in Cd-free Colloidal QD-LEDs During Operation

Authors: Ruiqi Zhang, Jamie Geng, Shaun Tan, Shreyas Srinivasan, Taehyung Kim, Mayuran Saravanapavanantham, Kwang-Hee Lim, Mike Dillender, Heejae Chung, Thienan Nguyen, Karen Yang, Yongli Lu, Taegon Kim, Moungi G. Bawendi, Vladimir Bulovic

Abstract: Heavy metal-free quantum-dot light-emitting devices (QD-LEDs) have demonstrated remarkable brightness, saturated color, and high efficiencies across a broad spectral range. However, in contrast to organic LEDs (OLEDs), QD-LED operational lifetimes remain limited, with the underlying degradation mechanisms not fully understood. In the present study, we show that InP/ZnSe/ZnS (red-emitting) and ZnTe… ▽ More Heavy metal-free quantum-dot light-emitting devices (QD-LEDs) have demonstrated remarkable brightness, saturated color, and high efficiencies across a broad spectral range. However, in contrast to organic LEDs (OLEDs), QD-LED operational lifetimes remain limited, with the underlying degradation mechanisms not fully understood. In the present study, we show that InP/ZnSe/ZnS (red-emitting) and ZnTeSe/ZnSe/ZnS (blue-emitting) cadmium-free colloidal QD-LEDs undergo nanoscale morphological changes during operation. Specifically,interparticle coarsening and layer thinning are observed in the electron transport layer (ETL) consisting of ZnMgO nanoparticles (NPs), in the QD emissive layer, and in the organic hole transport layer. This is accompanied by the generation and diffusion of compositional oxygen- and hydrogen-radicals throughout the device, with oxygen accumulating at the electrode/ETL interfance. Moreover, in situ transmission electron microscopy reveals the electron beam exposure, in the presence of hydrogen radicals, accelerates ZnMgO NPs coarsening. To mitigate these degradation pathway, we show that acrylate-based resin-encapsulation treatment stabilize the ETL/QD layers by suppressing the radical formation and halting morphology changes. This approach achieves dramatic stability enhancements, exhibits an 8-fold and 5000-fold lifetime improvement on InP/ZnSe/ZnS and ZnTeSe/ZnSe/ZnS QD-LEDs, respectively. Our findings establish the causal relationships between the morphological degradation, interlayer radical dynamics, and state-of-the-art QD-LEDs instability, providing new insights into a scalable encapsulation treatment that enables efficient and long-lived Cd-free QD-LEDs. △ Less

Submitted 15 September, 2025; originally announced September 2025.

Comments: 34 pages, 5 figures

Solar Orbiter reveals persistent magnetic reconnection in medium-scale filament eruptions

Authors: Song Tan, Alexander Warmuth, Frédéric Schuller, Yuandeng Shen, Daniel F. Ryan, Daniele Calchetti, Johann Hirzberger, Takayoshi Oba, Artem Ulyanov, Gherardo Valori

Abstract: Solar filament eruptions play a key role in driving space weather, yet their fine-scale evolution remains poorly understood due to observational limitations. Using unprecedented high-resolution observations from Solar Orbiter's Extreme Ultraviolet Imager (105 km/pixel) and Polarimetric and Helioseismic Imager, we reveal persistent magnetic reconnection events in a failed filament eruption. We iden… ▽ More Solar filament eruptions play a key role in driving space weather, yet their fine-scale evolution remains poorly understood due to observational limitations. Using unprecedented high-resolution observations from Solar Orbiter's Extreme Ultraviolet Imager (105 km/pixel) and Polarimetric and Helioseismic Imager, we reveal persistent magnetic reconnection events in a failed filament eruption. We identify magnetic reconnections between the filament and surrounding magnetic field structures, with both frequency and type far exceeding previous observations. These reconnections significantly affect the filament stability and eruption dynamics, leading to sequential coronal jets and failed eruptions. We propose a 'persistent magnetic cutting' concept, highlighting how persistent small-scale magnetic reconnections cumulatively affect filament stability during its evolution. △ Less

Submitted 4 September, 2025; originally announced September 2025.

Comments: Accepted for publication in A&A

Extremely diverse coronal jets accompanying an erupting filament captured by Solar Orbiter

Authors: Song Tan, Alexander Warmuth, Frédéric Schuller, Yuandeng Shen, Jake A. J. Mitchell, Fanpeng Shi

Abstract: Solar jets are collimated plasma ejections driven by magnetic reconnection, which play a critical role in the energy release and mass transport in the solar atmosphere. Using Solar Orbiter's Extreme Ultraviolet Imager (EUI) with its unprecedented spatiotemporal resolution, we report the discovery of nine transient coronal jets associated with a filament eruption on September 30, 2024. These jets,… ▽ More Solar jets are collimated plasma ejections driven by magnetic reconnection, which play a critical role in the energy release and mass transport in the solar atmosphere. Using Solar Orbiter's Extreme Ultraviolet Imager (EUI) with its unprecedented spatiotemporal resolution, we report the discovery of nine transient coronal jets associated with a filament eruption on September 30, 2024. These jets, with a median lifetime of only 22 seconds, have significantly shorter timescales than previously observed coronal jets. They exhibit diverse morphologies and properties, evolving through three distinct phases of the filament eruption: initiation, rise, and peak. The spatial and temporal distribution of the jets suggests they are driven by dynamic magnetic reconnection between the erupting filament and overlying magnetic fields. These jets represent a distinct class of phenomena different from traditional mini-filament-driven jets, being directly associated with large-scale filament eruption processes. This study reveals a previously unrecognised class of highly transient jets, highlighting the complexity of reconnection-driven processes during filament eruptions and underscoring the importance of high-resolution observations in uncovering fundamental plasma dynamics in the solar atmosphere. △ Less

Submitted 4 September, 2025; originally announced September 2025.

Comments: Accepted for publication in A&A

Human Mobility in Epidemic Modeling

Authors: Xin Lu, Jiawei Feng, Shengjie Lai, Petter Holme, Shuo Liu, Zhanwei Du, Xiaoqian Yuan, Siqing Wang, Yunxuan Li, Xiaoyu Zhang, Yuan Bai, Xiaojun Duan, Wenjun Mei, Hongjie Yu, Suoyi Tan, Fredrik Liljeros

Abstract: Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to captu… ▽ More Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to capture the complex and heterogeneous nature of real-world human interactions. Recent advancements in data-driven methodologies and computational capabilities have unlocked the potential of integrating high-resolution human mobility data into epidemic modeling, significantly improving the accuracy, timeliness, and applicability of epidemic risk assessment, contact tracing, and intervention strategies. This review provides a comprehensive synthesis of the current landscape in human mobility-informed epidemic modeling. We explore diverse sources and representations of human mobility data, and then examine the behavioral and structural roles of mobility and contact in shaping disease transmission dynamics. Furthermore, the review spans a wide range of epidemic modeling approaches, ranging from classical compartmental models to network-based, agent-based, and machine learning models. And we also discuss how mobility integration enhances risk management and response strategies during epidemics. By synthesizing these insights, the review can serve as a foundational resource for researchers and practitioners, bridging the gap between epidemiological theory and the dynamic complexities of human interaction while charting clear directions for future research. △ Less

Submitted 1 November, 2025; v1 submitted 30 July, 2025; originally announced July 2025.

Comments: 79 pages, 13 figures, 5 tables

MSC Class: 91Cxx; 37M05; 91F99 ACM Class: J.3; J.4; K.4.1

Three-boson scattering hypervolume for a nonzero orbital angular momentum

Authors: Pui In Ip, Shina Tan

Abstract: We analyze the zero energy collision of three identical bosons in the same internal state with total orbital angular momentum $L=2$, assuming short range interactions. By solving the Schrödinger equation asymptotically, we derive two expansions of the wave function when three bosons are far apart or a pair of bosons and the third boson are far apart. The scattering hypervolume $D$ is defined for t… ▽ More We analyze the zero energy collision of three identical bosons in the same internal state with total orbital angular momentum $L=2$, assuming short range interactions. By solving the Schrödinger equation asymptotically, we derive two expansions of the wave function when three bosons are far apart or a pair of bosons and the third boson are far apart. The scattering hypervolume $D$ is defined for this collision. Unlike the scattering hypervolume defined by one of us in 2008, whose dimension is length to the fourth power, the dimension of $D$ studied in the present paper is length to the eighth power. We then derive the expression of $D$ when the interaction potentials are weak, using the Born's expansion. We also calculate the energy shift of such three bosons with three different momenta $\hbar \mathbf{k_{1}}$, $\hbar\mathbf{k_{2}}$ and $\hbar\mathbf{k_{3}}$ in a large periodic box. The obtained energy shift depends on $D^{(0)}/Ω^{2}$ and $D/Ω^{2}$, where $D^{(0)}$ is the three-body scattering hypervolume defined for the three-body $L=0$ collision and $Ω$ is the volume of the periodic box. We also calculate the contribution of $D$ to the three-body T-matrix element for low-energy collisions. We then calculate the shift of the energy and the three-body recombination rate due to $D^{(0)}$ and $D$ in the dilute homogeneous Bose gas. The contribution to the three-body recombination rate constant from $D$ is proportional to $T^2$ if the temperature $T$ is much larger than the quantum degeneracy temperature but still much lower than the temperature scale at which the thermal de Broglie wave length becomes comparable to the physical range of interaction. △ Less

Submitted 12 January, 2026; v1 submitted 28 July, 2025; originally announced July 2025.

Laser Amplification in $e^{-}$-$μ^{-}$-ion Plasmas

Authors: Y. Chen, R. Ou, H. Wang, S. J. Chen, Y. X. Zhong, Y. G. Chen, S. Tan, Y. X. Li, C. Y. Zheng, Z. J. Liu, L. H. Cao, M. M. Zhang, D. P. Feng, W. J. Zuo, C. Z. Xiao

Abstract: We investigate laser amplification in $e^{-}$-$μ^{-}$-ion plasmas, where negative muons partially replace electrons. Theoretical results reveal a hybrid plasma wave, called $μ$-wave that exhibits ion-acoustic behavior in long-wavelength regime and Langmuir-like behavior in short-wavelength regime. Besides, the Landau damping of $μ$-wave is smaller than that of Langmuir wave. Particle-in-cell (PIC)… ▽ More We investigate laser amplification in $e^{-}$-$μ^{-}$-ion plasmas, where negative muons partially replace electrons. Theoretical results reveal a hybrid plasma wave, called $μ$-wave that exhibits ion-acoustic behavior in long-wavelength regime and Langmuir-like behavior in short-wavelength regime. Besides, the Landau damping of $μ$-wave is smaller than that of Langmuir wave. Particle-in-cell (PIC) simulations confirm the theoretical results of instabilities in$e^{-}$-$μ^{-}$-ion plasmas. The $μ$-wave enables efficient laser amplification by suppressing pump-driven spontaneous instabilities through enhanced Landau damping of Langmuir waves. Compared to Raman amplification, $μ$-wave amplification can maintain the Gaussian waveform of the seed laser, avoiding pulse splitting. Compared to strongcoupling Brillouin amplification, $μ$-wave amplification exhibits weaker filamentation instability. Our theoretical model can be generalized to other plasma systems containing two species of negatively charged particles, such as two-temperature electron plasmas and negative-ion plasma. These findings establish $e^{-}$-$μ^{-}$-ion plasma as a promising medium for advanced laser amplification schemes. △ Less

Submitted 6 October, 2025; v1 submitted 6 July, 2025; originally announced July 2025.

Comments: 9 pages, 6 figures

Taylor dispersion of bubble swarms rising in quiescent liquid

Authors: Guangyuan Huang, Hendrik Hessenkemper, Shiyong Tan, Rui Ni, Anna-E. Sommer, Andrew D. Bragg, Tian Ma

Abstract: We study the dispersion of bubble swarms rising in initially quiescent water using 3D Lagrangian tracking of deformable bubbles and tracer particles in an octagonal bubble column. First, we compare the dispersion inside bubble swarms with that for single-bubble cases and find that the horizontal mean squared displacement (MSD) in the swarm cases exhibits oscillations around the asymptotic scaling… ▽ More We study the dispersion of bubble swarms rising in initially quiescent water using 3D Lagrangian tracking of deformable bubbles and tracer particles in an octagonal bubble column. First, we compare the dispersion inside bubble swarms with that for single-bubble cases and find that the horizontal mean squared displacement (MSD) in the swarm cases exhibits oscillations around the asymptotic scaling predicted for a diffusive regime. This occurs due to wake-induced bubble motion, however, the oscillatory behaviour is heavily damped compared to the single-bubble cases due to the presence of bubble-induced turbulence (BIT) and bubble-bubble interactions in the swarm. The vertical MSD in bubble swarms is nearly an order of magnitude faster than the single-bubble cases, due to the much higher vertical fluctuating bubble velocities in the swarms. We also investigate tracer dispersion in BIT and find that concerning the time to transition away from the ballistic regime, larger bubbles with a higher gas void fraction transition earlier than tracers, consistent with Mathai et al. (\textit{Phys. Rev. Lett.} 121, 054501, 2018). However, for bubble swarms with smaller bubbles and a lower gas void fraction, they transition at the same time. This differing behavior is due to the turbulence being more well-mixed for the larger bubble case, whereas for the smaller bubble case the tracer dispersion is highly dependent on the wake fluctuations generated by the oscillating motion of nearby bubbles. △ Less

Submitted 16 May, 2025; originally announced May 2025.

Journal ref: J. Fluid Mech. 1014 (2025) R1

Kolmogorov scaling in bubble-induced turbulence

Authors: Tian Ma, Shiyong Tan, Rui Ni, Hendrik Hessenkemper, Andrew D. Bragg

Abstract: Experiments using 3D Lagrangian tracking are used to investigate Kolmogorov scaling below the bubble size in bubble-induced turbulence (BIT). Second and third order structure functions reveal approximate Kolmogorov scaling for homogeneous bubble swarms. A new scaling for the kinetic energy dissipation rate is derived and shown to be in excellent agreement with the data. Using this we predict the s… ▽ More Experiments using 3D Lagrangian tracking are used to investigate Kolmogorov scaling below the bubble size in bubble-induced turbulence (BIT). Second and third order structure functions reveal approximate Kolmogorov scaling for homogeneous bubble swarms. A new scaling for the kinetic energy dissipation rate is derived and shown to be in excellent agreement with the data. Using this we predict the scale separation below the bubble size as a function of the parameters and find that a large inertial range is not possible in BIT since bubbles of the required size would quickly break down. △ Less

Submitted 12 May, 2025; originally announced May 2025.

Comments: Accepted for publication in Physical Review Letters

Physics-informed Transformer Model for the Design of Wavelength-filtering Ring Resonator

Authors: Yu Dian Lim, Feng Shuo Wan, Ren Jie Wan, Chuan Seng Tan

Abstract: We have developed a physics-informed transformer model to suggest design parameters in wavelength-filtering ring resonator, that suit a given pair of resonant wavelengths with <6 nm errors. The model provides a versatile method for rapid and accurate design of resonators corresponding to various resonant wavelengths. We have developed a physics-informed transformer model to suggest design parameters in wavelength-filtering ring resonator, that suit a given pair of resonant wavelengths with <6 nm errors. The model provides a versatile method for rapid and accurate design of resonators corresponding to various resonant wavelengths. △ Less

Submitted 23 April, 2025; originally announced April 2025.

DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning

Authors: Tom Dooney, Harsh Narola, Stefano Bromuri, R. Lyana Curier, Chris Van Den Broeck, Sarah Caudill, Daniel Stanley Tan

Abstract: Gravitational wave (GW) detectors, such as LIGO, Virgo, and KAGRA, detect faint signals from distant astrophysical events. However, their high sensitivity also makes them susceptible to background noise, which can obscure these signals. This noise often includes transient artifacts called 'glitches', that can mimic genuine astrophysical signals or mask their true characteristics. In this study, we… ▽ More Gravitational wave (GW) detectors, such as LIGO, Virgo, and KAGRA, detect faint signals from distant astrophysical events. However, their high sensitivity also makes them susceptible to background noise, which can obscure these signals. This noise often includes transient artifacts called 'glitches', that can mimic genuine astrophysical signals or mask their true characteristics. In this study, we present DeepExtractor, a deep learning framework that is designed to reconstruct signals and glitches with power exceeding interferometer noise, regardless of their source. We design DeepExtractor to model the inherent noise distribution of GW detectors, following conventional assumptions that the noise is Gaussian and stationary over short time scales. It operates by predicting and subtracting the noise component of the data, retaining only the clean reconstruction of signal or glitch. We focus on applications related to glitches and validate DeepExtractor's effectiveness through three experiments: (1) reconstructing simulated glitches injected into simulated detector noise, (2) comparing its performance with the state-of-the-art BayesWave algorithm, and (3) analyzing real data from the Gravity Spy dataset to demonstrate effective glitch subtraction from LIGO strain data. We further demonstrate its potential by reconstructing three real GW events from LIGO's third observing run, without being trained on GW waveforms. Our proposed model achieves a median mismatch of only 0.9% for simulated glitches, outperforming several deep learning baselines. Additionally, DeepExtractor surpasses BayesWave in glitch recovery, offering a dramatic computational speedup by reconstructing one glitch sample in approximately 0.1 seconds on a CPU, compared to BayesWave's processing time of approximately one hour per glitch. △ Less

Submitted 11 June, 2025; v1 submitted 30 January, 2025; originally announced January 2025.

Comments: 24 pages, 17 figures, 3 tables

Journal ref: Phys. Rev. D 112, 044022 (2025)

Evidential Physics-Informed Neural Networks

Authors: Hai Siong Tan, Kuancheng Wang, Rafe McBeth

Abstract: We present a novel class of Physics-Informed Neural Networks that is formulated based on the principles of Evidential Deep Learning, where the model incorporates uncertainty quantification by learning parameters of a higher-order distribution. The dependent and trainable variables of the PDE residual loss and data-fitting loss terms are recast as functions of the hyperparameters of an evidential p… ▽ More We present a novel class of Physics-Informed Neural Networks that is formulated based on the principles of Evidential Deep Learning, where the model incorporates uncertainty quantification by learning parameters of a higher-order distribution. The dependent and trainable variables of the PDE residual loss and data-fitting loss terms are recast as functions of the hyperparameters of an evidential prior distribution. Our model is equipped with an information-theoretic regularizer that contains the Kullback-Leibler divergence between two inverse-gamma distributions characterizing predictive uncertainty. Relative to Bayesian-Physics-Informed-Neural-Networks, our framework appeared to exhibit higher sensitivity to data noise, preserve boundary conditions more faithfully and yield empirical coverage probabilities closer to nominal ones. Toward examining its relevance for data mining in scientific discoveries, we demonstrate how to apply our model to inverse problems involving 1D and 2D nonlinear differential equations. △ Less

Submitted 27 January, 2025; originally announced January 2025.

Comments: Accepted for International Conference on Scientific Computing and Machine Learning (SCML) 2025

Dissertation Machine Learning in Materials Science -- A case study in Carbon Nanotube field effect transistors

Authors: Shulin Tan

Abstract: In this thesis, I explored the use of several machine learning techniques, including neural networks, simulation-based inference, and generative flow networks, on predicting CNTFETs performance, probing the conductivity properties of CNT network, and generating CNTFETs processing information for target performance. In this thesis, I explored the use of several machine learning techniques, including neural networks, simulation-based inference, and generative flow networks, on predicting CNTFETs performance, probing the conductivity properties of CNT network, and generating CNTFETs processing information for target performance. △ Less

Submitted 18 January, 2025; originally announced January 2025.

Comments: PhD thesis

The Quantum Internet (Technical Version)

Authors: Peter P. Rohde, Zixin Huang, Yingkai Ouyang, He-Liang Huang, Zu-En Su, Simon Devitt, Rohit Ramakrishnan, Atul Mantri, Si-Hui Tan, Nana Liu, Scott Harrison, Chandrashekar Radhakrishnan, Gavin K. Brennen, Ben Q. Baragiola, Jonathan P. Dowling, Tim Byrnes, William J. Munro

Abstract: Following the emergence of quantum computing, the subsequent quantum revolution will be that of interconnecting individual quantum computers at global level. In the same way that classical computers only realised their full potential with the emergence of the internet, a fully realised quantum internet is the next stage of evolution for quantum computation. This work examines in detail how the qua… ▽ More Following the emergence of quantum computing, the subsequent quantum revolution will be that of interconnecting individual quantum computers at global level. In the same way that classical computers only realised their full potential with the emergence of the internet, a fully realised quantum internet is the next stage of evolution for quantum computation. This work examines in detail how the quantum internet would evolve in practice, focusing not only on the technology itself but also on the implications it will have economically and politically. We present both original ideas, as well as an extensive review of relevant and related background material. This work begins with a description of classical networks before introducing the key concepts behind quantum networks, such as quantum internet protocols, quantum cryptography, and cloud quantum computing. The work is divided into technical sections (requiring only a basic knowledge of the notation of quantum mechanics), for those interested in mathematical details, as well as non-technical sections for those seeking a more general understanding. We target this work very broadly at quantum and classical computer scientists, classical computer systems, software and network engineers, physicists, economists, artists, musicians, and those just generally curious about the future of quantum technologies and what they might bring to humanity. △ Less

Submitted 22 January, 2025; v1 submitted 21 January, 2025; originally announced January 2025.

Comments: 370 pages, comments are welcome; note that apart from a few sections, most of this project has been written before 2021

Three-body scattering hypervolume of two-component fermions in three dimensions

Authors: Jiansen Zhang, Zipeng Wang, Shina Tan

Abstract: We study the zero-energy collision of three fermions, two of which are in the spin-down ($\downarrow$) state and one of which is in the spin-up ($\uparrow$) state. Assuming that the two-body and the three-body interactions have a finite range, we find a parameter, $D$, called the three-body scattering hypervolume. We study the three-body wave function asymptotically when three fermions are far apa… ▽ More We study the zero-energy collision of three fermions, two of which are in the spin-down ($\downarrow$) state and one of which is in the spin-up ($\uparrow$) state. Assuming that the two-body and the three-body interactions have a finite range, we find a parameter, $D$, called the three-body scattering hypervolume. We study the three-body wave function asymptotically when three fermions are far apart or one spin-$\uparrow$ (spin-$\downarrow$) fermion and one pair, formed by the other two fermions, are far apart, and derive three asymptotic expansions of the wave function. The three-body scattering hypervolume $D$ appears in the coefficients of such expansions at the order of $B^{-5}$, where $B=\sqrt{(s_1^2+s_2^2+s_3^2)/2}$ is the hyperradius of the triangle formed by the three fermions (we assume that the three fermions have the same mass), and $s_1,s_2,s_3$ are the sides of the triangle. We compute the $T$-matrix element for three such fermions colliding at low energy in terms of $D$ in the absence of two-body interactions. When the interactions are weak, we calculate $D$ approximately using the Born expansion. We also analyze the energy shift of three two-component fermions in a large periodic cube due to $D$ and generalize this result to the many-fermion system. $D$ also determines the three-body recombination rates in two-component Fermi gases, and we calculate the three-body recombination rates in terms of $D$ and the density and temperature of the gas. △ Less

Submitted 14 March, 2025; v1 submitted 9 January, 2025; originally announced January 2025.

Comments: 21 pages, 2 figures

Predicting Organic-Inorganic Halide Perovskite Photovoltaic Performance from Optical Properties of Constituent Films through Machine Learning

Authors: Ruiqi Zhang, Brandon Motes, Shaun Tan, Yongli Lu, Meng-Chen Shih, Yilun Hao, Karen Yang, Shreyas Srinivasan, Moungi G. Bawendi, Vladimir Bulovic

Abstract: We demonstrate a machine learning (ML) approach that accurately predicts the current-voltage behavior of 3D/2D-structured (FAMA)Pb(IBr)3/OABr hybrid organic-inorganic halide perovskite (HOIP) solar cells under AM1.5 illumination. Our neural network algorithm is trained on measured responses from several hundred HOIP solar cells, using three simple optical measurements of constituent HOIP films as… ▽ More We demonstrate a machine learning (ML) approach that accurately predicts the current-voltage behavior of 3D/2D-structured (FAMA)Pb(IBr)3/OABr hybrid organic-inorganic halide perovskite (HOIP) solar cells under AM1.5 illumination. Our neural network algorithm is trained on measured responses from several hundred HOIP solar cells, using three simple optical measurements of constituent HOIP films as input: optical transmission spectrum, spectrally-resolved photoluminescence, and time-resolved photoluminescence, from which we predict the open-circuit voltage (Voc), short-circuit current (Jsc), and fill factors (FF) values of solar cells that contain the HOIP active layers. Determined average prediction accuracies for 95 % of the predicted Voc, Jsc, and FF values are 91%, 94% and 89%, respectively, with R2 coefficients of determination of 0.47, 0.77, and 0.58, respectively. Quantifying the connection between ML predictions and physical parameters extracted from the measured HOIP films optical properties, allows us to identify the most significant parameters influencing the prediction results. With separate ML-classifying algorithms, we identify degraded solar cells using the same optical input data, achieving over 90% classification accuracy through support vector machine, cross entropy loss, and artificial neural network algorithms. To our knowledge, the demonstrated regression and classification work is the first to use ML to predict device photovoltaic properties solely from the optical properties of constituent materials. △ Less

Submitted 6 December, 2024; originally announced December 2024.

Comments: 36 pages, 6 figures

Intermittency of bubble deformation in turbulence

Authors: Xu Xu, Yinghe Qi, Shijie Zhong, Shiyong Tan, Qianwen Wu, Rui Ni

Abstract: The deformation of finite-sized bubbles in intense turbulence exhibits complex geometries beyond simple spheroids as the bubbles exchange energy with the surrounding eddies across a wide range of scales. This study investigates deformation via the velocity of the most stretched tip of the deformed bubble in 3D, as the tip extension results from the compression of the rest of the interface by surro… ▽ More The deformation of finite-sized bubbles in intense turbulence exhibits complex geometries beyond simple spheroids as the bubbles exchange energy with the surrounding eddies across a wide range of scales. This study investigates deformation via the velocity of the most stretched tip of the deformed bubble in 3D, as the tip extension results from the compression of the rest of the interface by surrounding eddies. The results show that the power spectrum based on the tip velocity exhibits a scaling akin to that of the Lagrangian statistics of fluid elements, but decays with a distinct timescale and magnitude modulated by the Weber number based on the bubble size. This indicates that the interfacial energy is primarily siphoned from eddies of similar sizes as the bubble. Moreover, the tip velocity appears much more intermittent than the velocity increment, and its distribution near the extreme tails can be explained by the proposed model that accounts for the fact that small eddies with sufficient energy can contribute to extreme deformation. These findings provide a framework for understanding the energy transfer between deformable objects and multiscale eddies in intense turbulence. △ Less

Submitted 28 October, 2024; originally announced October 2024.

Journal ref: Phys. Rev. Lett. 133, 214001 (2024)

Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation

Authors: Hai Siong Tan, Kuancheng Wang, Rafe Mcbeth

Abstract: In this work, we examine the effectiveness of an uncertainty quantification framework known as Evidential Deep Learning applied in the context of biomedical image segmentation. This class of models involves assigning Dirichlet distributions as priors for segmentation labels, and enables a few distinct definitions of model uncertainties. Using the cardiac and prostate MRI images available in the Me… ▽ More In this work, we examine the effectiveness of an uncertainty quantification framework known as Evidential Deep Learning applied in the context of biomedical image segmentation. This class of models involves assigning Dirichlet distributions as priors for segmentation labels, and enables a few distinct definitions of model uncertainties. Using the cardiac and prostate MRI images available in the Medical Segmentation Decathlon for validation, we found that Evidential Deep Learning models with U-Net backbones generally yielded superior correlations between prediction errors and uncertainties relative to the conventional baseline equipped with Shannon entropy measure, Monte-Carlo Dropout and Deep Ensemble methods. We also examined these models' effectiveness in active learning, finding that relative to the standard Shannon entropy-based sampling, they yielded higher point-biserial uncertainty-error correlations while attaining similar performances in Dice-Sorensen coefficients. These superior features of EDL models render them well-suited for segmentation tasks that warrant a critical sensitivity in detecting large model errors. △ Less

Submitted 24 October, 2024; originally announced October 2024.

Comments: 15 pages

Journal ref: Published in Proceedings of TAAI 2024

Dense Suspension Inertial Microfluidic Particle Theory (DENSE-IMPACT) Model for Elucidating Outer Wall Focusing at High Cell Densities

Authors: Soon Wei Daniel Lim, Yong How Kee, Scott Nicholas Allan Smith, Shan Mei Tan, An Eng Lim, Yuansheng Yang, Shireen Goh

Abstract: Inertial microfluidics has been limited to dilute particle concentrations due to defocusing (spreading out) at high particle concentrations. We observe a counterintuitive shift of focusing to the outer curved wall under high concentration flow, which contradicts the existing particle focusing theory. We developed a multiphase model incorporating lift forces and particle-particle interactions to ex… ▽ More Inertial microfluidics has been limited to dilute particle concentrations due to defocusing (spreading out) at high particle concentrations. We observe a counterintuitive shift of focusing to the outer curved wall under high concentration flow, which contradicts the existing particle focusing theory. We developed a multiphase model incorporating lift forces and particle-particle interactions to explain this behaviour. Numerical simulations validated by experimental data reveal the shift is governed by the ratio of the lift force strength to that of particle interaction frequencies. △ Less

Submitted 14 November, 2024; v1 submitted 19 September, 2024; originally announced September 2024.

Recognizing Beam Profiles from Silicon Photonics Gratings using Transformer Model

Authors: Yu Dian Lim, Hong Yu Li, Simon Chun Kiat Goh, Xiangyu Wang, Peng Zhao, Chuan Seng Tan

Abstract: Over the past decade, there has been extensive work in developing integrated silicon photonics (SiPh) gratings for the optical addressing of trapped ion qubits in the ion trap quantum computing community. However, when viewing beam profiles from infrared (IR) cameras, it is often difficult to determine the corresponding heights where the beam profiles are located. In this work, we developed transf… ▽ More Over the past decade, there has been extensive work in developing integrated silicon photonics (SiPh) gratings for the optical addressing of trapped ion qubits in the ion trap quantum computing community. However, when viewing beam profiles from infrared (IR) cameras, it is often difficult to determine the corresponding heights where the beam profiles are located. In this work, we developed transformer models to recognize the corresponding height categories of beam profiles of light from SiPh gratings. The model is trained using two techniques: (1) input patches, and (2) input sequence. For model trained with input patches, the model achieved recognition accuracy of 0.938. Meanwhile, model trained with input sequence shows lower accuracy of 0.895. However, when repeating the model-training 150 cycles, model trained with input patches shows inconsistent accuracy ranges between 0.445 to 0.959, while model trained with input sequence exhibit higher accuracy values between 0.789 to 0.936. The obtained outcomes can be expanded to various applications, including auto-focusing of light beam and auto-adjustment of z-axis stage to acquire desired beam profiles. △ Less

Submitted 22 August, 2024; v1 submitted 19 August, 2024; originally announced August 2024.

Harnessing Zn-Volatility for Compositional Tuning in PtZn Nanoalloy Catalysts

Authors: Bingqing Yao, Chaokai Xu, Yaxin Tang, Yankun Du, Shengdong Tan, Sheng Dai, Guangfu Luo, Qian He

Abstract: Bimetallic nanoalloys have gained extensive attention due to their tunable properties and wide range of catalytic applications. However, achieving good compositional control in nanoalloy catalysts remains a formidable challenge. In this work, we demonstrate that heat treatment can be used to tune the composition of Pt-Zn nanoalloy catalysts, leveraging the volatile nature of zinc to enhance their… ▽ More Bimetallic nanoalloys have gained extensive attention due to their tunable properties and wide range of catalytic applications. However, achieving good compositional control in nanoalloy catalysts remains a formidable challenge. In this work, we demonstrate that heat treatment can be used to tune the composition of Pt-Zn nanoalloy catalysts, leveraging the volatile nature of zinc to enhance their performance in propane dehydrogenation. Through identical location (scanning) transmission electron microscopy (IL-(S)TEM) using an in-situ EM gas cell, as well as other complementary techniques, we observed that the zinc content of the Pt-Zn nanoalloy particles decreased over time of the heat treatment under hydrogen. The rate of change depends on the original composition of the particles, as well as the heat treatment conditions such as temperature and flow rate. Our experimental results and theoretical calculations suggest that Zn in the intermetallic phase might be more stable, providing an opportunity for precise tuning the nanoparticle compositions. This approach presents a viable strategy for developing better Pt-Zn catalysts for propane dehydrogenation. △ Less

Submitted 19 July, 2024; originally announced July 2024.

Foundry's perspective on laser and SOA module integration with silicon photonics

Authors: James Y. S. Tan, Shawn Xie Wu, Salih Yanikgonul, Chao Li, Patrick Guo-Qiang Lo

Abstract: Silicon photonic integrated circuit (PIC) builds on the demand for a low cost approach from established silicon-based manufacturing infrastructure traditionally built for electronics. Besides its natural abundance, silicon has desirable properties such as optically low loss (at certain critical wavelengths), and small form factor to enable high density scaled-up optical on-chip circuitry. However,… ▽ More Silicon photonic integrated circuit (PIC) builds on the demand for a low cost approach from established silicon-based manufacturing infrastructure traditionally built for electronics. Besides its natural abundance, silicon has desirable properties such as optically low loss (at certain critical wavelengths), and small form factor to enable high density scaled-up optical on-chip circuitry. However, given its indirect bandgap, the platform is typically integrated with other direct bandgap (e.g., III-V semiconductor) platforms for on-chip light source. An effective solution to integrating light source onto silicon photonics platform is integral to a practical scaled-up and full-fledged integrated photonics implementation. Here, we discuss the integration solutions, and present our foundry's perspective toward realizing it. △ Less

Submitted 20 February, 2024; originally announced May 2024.

Comments: 14 pages

Journal ref: IEEE J Lightwave Technol. vol. 42, no. 3, pp. 1062-1074, 2024

Deep Evidential Learning for Radiotherapy Dose Prediction

Authors: Hai Siong Tan, Kuancheng Wang, Rafe Mcbeth

Abstract: In this work, we present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction. Using medical images of the Open Knowledge-Based Planning Challenge dataset, we found that this model can be effectively harnessed to yield uncertainty estimates that inherited correlations with prediction errors upon completion of n… ▽ More In this work, we present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction. Using medical images of the Open Knowledge-Based Planning Challenge dataset, we found that this model can be effectively harnessed to yield uncertainty estimates that inherited correlations with prediction errors upon completion of network training. This was achieved only after reformulating the original loss function for a stable implementation. We found that (i)epistemic uncertainty was highly correlated with prediction errors, with various association indices comparable or stronger than those for Monte-Carlo Dropout and Deep Ensemble methods, (ii)the median error varied with uncertainty threshold much more linearly for epistemic uncertainty in Deep Evidential Learning relative to these other two conventional frameworks, indicative of a more uniformly calibrated sensitivity to model errors, (iii)relative to epistemic uncertainty, aleatoric uncertainty demonstrated a more significant shift in its distribution in response to Gaussian noise added to CT intensity, compatible with its interpretation as reflecting data noise. Collectively, our results suggest that Deep Evidential Learning is a promising approach that can endow deep-learning models in radiotherapy dose prediction with statistical robustness. Towards enhancing its clinical relevance, we demonstrate how we can use such a model to construct the predicted Dose-Volume-Histograms' confidence intervals. △ Less

Submitted 23 September, 2024; v1 submitted 25 April, 2024; originally announced April 2024.

Comments: 28 pages

Journal ref: Computers in Biology and Medicine, Vol. 182, Nov 2024, 109172

Broadband squeezed light field by magnetostriction in an opto-magnomechanical

Authors: Ke Di, Shuai Tan, Anyu Cheng, Yinxue Zhao, Yu Liu, Jiajia Du

Abstract: We present a novel mechanism for generating a wide bandwidth squeezed optical output field in an opto-magnomechanical system. In this system, the magnon (mechanical) mode in the yttrium-iron-garnet crystal is coupled to the microwave field (optical field) through magnetic dipole (radiation pressure) interaction. The magnetostrictive force induced by the yttrium-iron-garnet crystal causes a mechani… ▽ More We present a novel mechanism for generating a wide bandwidth squeezed optical output field in an opto-magnomechanical system. In this system, the magnon (mechanical) mode in the yttrium-iron-garnet crystal is coupled to the microwave field (optical field) through magnetic dipole (radiation pressure) interaction. The magnetostrictive force induced by the yttrium-iron-garnet crystal causes a mechanical displacement and creates a quadrature squeezed magnon mode. Eventually, this quadrature squeezed mechanical mode is transferred to the output optical field through state-swap interaction. Our results demonstrate the optimal parameter range for obtaining a stable squeezed optical output field with a wide bandwidth. Moreover, the squeezed light field exhibits strong robustness to environmental temperature. The new scheme we propose has potential applications in quantum precision measurements, quantum wireless networks, quantum radar, etc. △ Less

Submitted 7 February, 2024; originally announced February 2024.

Three-body scattering area for particles with infinite or zero scattering length in two dimensions

Authors: Junjie Liang, Shina Tan

Abstract: We derive the asymptotic expansions of the wave function of three particles having equal mass with finite-range interactions and infinite or zero two-dimensional scattering length colliding at zero energy and zero orbital angular momentum, from which a three-body parameter $D$ is defined. The dimension of $D$ is length squared, and we call $D$ three-body scattering area. We find that the ground st… ▽ More We derive the asymptotic expansions of the wave function of three particles having equal mass with finite-range interactions and infinite or zero two-dimensional scattering length colliding at zero energy and zero orbital angular momentum, from which a three-body parameter $D$ is defined. The dimension of $D$ is length squared, and we call $D$ three-body scattering area. We find that the ground state energy per particle of a zero-temperature dilute Bose gas with these interactions is approximately $\frac{\hbar^2 D }{6m}ρ^2$, where $ρ$ is the number density of the bosons, $m$ is the mass of each boson, and $\hbar$ is Planck's constant over $2π$. Such a Bose gas is stable at $D\geq 0$ in the thermodynamic limit, and metastable at $D<0$ in the harmonic trap if the number of bosons is less than $N_{cr}\approx 3.6413 \sqrt{\frac{\hbar}{mω|D|}}$, where $ω$ is the angular frequency of the harmonic trap. If the two-body interaction supports bound states, $D$ typically acquires a negative imaginary part, and we find the relation between this imaginary part and the amplitudes of the pair-boson production processes. We derive a formula for the three-body recombination rate constant of the many-boson system in terms of the imaginary part of $D$. △ Less

Submitted 28 April, 2024; v1 submitted 3 February, 2024; originally announced February 2024.

Breaking bubbles across multiple timescales in turbulence

Authors: Yinghe Qi, Xu Xu, Shiyong Tan, Shijie Zhong, Qianwen Wu, Rui Ni

Abstract: The familiar process of bubbles generated via breaking waves in the ocean is foundational to many natural and industrial applications. In this process, large pockets of entrained gas are successively fragmented by the ambient turbulence into smaller and smaller bubbles. The key question is how long it takes for the bubbles to reach terminal sizes for a given system. Despite decades of effort, the… ▽ More The familiar process of bubbles generated via breaking waves in the ocean is foundational to many natural and industrial applications. In this process, large pockets of entrained gas are successively fragmented by the ambient turbulence into smaller and smaller bubbles. The key question is how long it takes for the bubbles to reach terminal sizes for a given system. Despite decades of effort, the reported breakup time from multiple experiments differs significantly. Here, to reconcile those results, rather than focusing on one scale, we measure multiple timescales associated with the process through a unique experiment that resolves bubbles' local deformation and curvature. The results emphasize that the scale separation among various timescales is controlled by the Weber number, similar to how the Reynolds number determines the scale separation in single-phase turbulence, but shows a distinct transition at a critical Weber number. △ Less

Submitted 18 January, 2024; originally announced January 2024.

Journal ref: Journal of Fluid Mechanics. 2024;983:A24

Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation

Authors: H. S. Tan, Kuancheng Wang, Rafe Mcbeth

Abstract: In this work, we study various hybrid models of entropy-based and representativeness sampling techniques in the context of active learning in medical segmentation, in particular examining the role of UMAP (Uniform Manifold Approximation and Projection) as a technique for capturing representativeness. Although UMAP has been shown viable as a general purpose dimension reduction method in diverse are… ▽ More In this work, we study various hybrid models of entropy-based and representativeness sampling techniques in the context of active learning in medical segmentation, in particular examining the role of UMAP (Uniform Manifold Approximation and Projection) as a technique for capturing representativeness. Although UMAP has been shown viable as a general purpose dimension reduction method in diverse areas, its role in deep learning-based medical segmentation has yet been extensively explored. Using the cardiac and prostate datasets in the Medical Segmentation Decathlon for validation, we found that a novel hybrid combination of Entropy-UMAP sampling technique achieved a statistically significant Dice score advantage over the random baseline ($3.2 \%$ for cardiac, $4.5 \%$ for prostate), and attained the highest Dice coefficient among the spectrum of 10 distinct active learning methodologies we examined. This provides preliminary evidence that there is an interesting synergy between entropy-based and UMAP methods when the former precedes the latter in a hybrid model of active learning. △ Less

Submitted 27 May, 2024; v1 submitted 16 December, 2023; originally announced December 2023.

Comments: 25 pages, 6 figures

Journal ref: Computers in Biology and Medicine, vol. 176, June 2024, 108605

Fatigue behaviors and atomic-scale mechanisms in nanocrystalline gold thin film

Authors: Honglei Chen, Susheng Tan, Zhijie Wang

Abstract: The fatigue properties of 930 nm-thick Au films and 1 μm-thick Au film with a Ti interlayer are systematically investigated. The dominant damage behaviors of 930 nm-thick Au films under dynamic bending cyclic loading changed from extrusions to intergranular cracks with the decrease in strain ranges and the increase in cyclic cycles. The different fatigue behaviors are attributed to the process of… ▽ More The fatigue properties of 930 nm-thick Au films and 1 μm-thick Au film with a Ti interlayer are systematically investigated. The dominant damage behaviors of 930 nm-thick Au films under dynamic bending cyclic loading changed from extrusions to intergranular cracks with the decrease in strain ranges and the increase in cyclic cycles. The different fatigue behaviors are attributed to the process of edge dislocation annihilation and vacancy formation during cyclic deformation. Depositing 10 nm-thick Ti interlayers between the PI substrates and 1 μm-thick annealed Au films is effective to suppress strain localization and increase the rupture strain and the fatigue properties of thin Au films. This study shed lights on the fatigue mechanism and provide clues to design nanocomposites in the flexible displays in the practical application. △ Less

Submitted 12 November, 2023; originally announced November 2023.

Comments: 25 pages, 8 figures

Simultaneous Single Crystal Growth and Segregation of Ni-Rich Cathode Enabled by Nanoscale Phase Separation for Advanced Lithium-Ion Batteries

Authors: Yujing Bi, Yaobin Xu, Ran Yi, Dianying Liu, Peng Zuo, Jiangtao Hu, Qiuyan Li, Jing Wu, Chongmin Wang, Sha Tan, Enyuan Hu, Jingnan Li, Rebecca O Toole, Liu Luo, Xiaoguang Hao, Subramanian Venkatachalam, Job Rijssenbeek, Jie Xiao

Abstract: Synthesis of high-performance single crystal LiNi0.8Mn0.1Co0.1O2 (NMC811) in the absence of molten salt is challenging with no success yet. An innovative drop-in approach is discovered to synthesize single crystal NMC811 by controlling the morphology of transition metal hydroxide TM(OH)2 precursors followed by a simple decomposition step to form transition metal oxide (TMO) intermediates. Ni redis… ▽ More Synthesis of high-performance single crystal LiNi0.8Mn0.1Co0.1O2 (NMC811) in the absence of molten salt is challenging with no success yet. An innovative drop-in approach is discovered to synthesize single crystal NMC811 by controlling the morphology of transition metal hydroxide TM(OH)2 precursors followed by a simple decomposition step to form transition metal oxide (TMO) intermediates. Ni redistribution in TMO, as a result of the concurrent formation of mixed spinel and rock salt phases, helps deagglomerate the later formed NMC811 clusters of single crystals. As-prepared single crystal NMC811 is validated in a 2Ah pouch cell demonstrating 1000 stable cycling. The fundamentally new reaction mechanism of single crystal growth and segregation without molten salt provides a new direction towards cost-efficient manufacturing of single crystal NMC811 cathode for advanced lithium-based batteries. △ Less

Submitted 20 June, 2023; originally announced June 2023.

Comments: 13 pages, 5 figures

Turbulence calculation based on the extended Navier-Stokes equations

Authors: Shanwen Tan, Zhengui Li, Wangxu Li

Abstract: In this study, we propose a computational method for solving the turbulence problem of incompressible viscous Newtonian fluids based on the extended Navier-Stokes (N-S) equations. With some phenomenological observations and H. J. Kreuer's interpretation of the origin of viscosity, we make a hypothesis in the fluid flow that the shear process is accompanied by eddy formation. Considering the influe… ▽ More In this study, we propose a computational method for solving the turbulence problem of incompressible viscous Newtonian fluids based on the extended Navier-Stokes (N-S) equations. With some phenomenological observations and H. J. Kreuer's interpretation of the origin of viscosity, we make a hypothesis in the fluid flow that the shear process is accompanied by eddy formation. Considering the influence of eddy on convection and diffusion, the classical N-S equations have been improved to obtain the extended N-S equations. The extended equations are closed and the source of velocity fluctuations is explicitly considered as additional convection and diffusion. The extended equations are compatible with the classical N-S equations and are able to describe the laminar and turbulent flow in a unified way. In fluid flow simulations, the equations describing the mean flow quantities can be obtained directly from the extended N-S equations without any additional turbulence model. The flow over a cube placed in a channel was numerically investigated to verify the extended equation, these simulation results were in good agreement with the Large Eddy Simulation (LES) and experimental results. △ Less

Submitted 16 June, 2023; v1 submitted 26 May, 2023; originally announced May 2023.

Comments: 20 pages, 9 figures

Elucidating the Role of Prelithiation in Si-based Anodes for Interface Stabilization

Authors: Shuang Bai, Wurigumula Bao, Kun Qian, Bing Han, Weikang Li, Baharak Sayahpour, Bhagath Screenarayanan, Darren H. S. Tan, So-yeon Ham, Ying Shirley Meng

Abstract: Prelithiation as a facile and effective method to compensate the lithium inventory loss in the initial cycle has progressed considerably both on anode and cathode sides. However, much less research has been devoted to the prelithiation effect on the interface stabilization for long-term cycling of Si-based anodes. An in-depth quantitative analysis of the interface that form during the prelithiatio… ▽ More Prelithiation as a facile and effective method to compensate the lithium inventory loss in the initial cycle has progressed considerably both on anode and cathode sides. However, much less research has been devoted to the prelithiation effect on the interface stabilization for long-term cycling of Si-based anodes. An in-depth quantitative analysis of the interface that form during the prelithiation of SiO$_x$ is presented here and the results are compared with prelithiaton of Si anodes. Local structure probe combined with detailed electrochemical analysis reveals that a characteristic mosaic interface is formed on both prelithiated SiO$_x$ and Si anodes. This mosaic interface containing multiple lithium silicates phases, is fundamentally different from the solid electrolyte interface (SEI) formed without prelithiation. The ideal conductivity and mechanical properties of lithium silicates enable improved cycling stability of both prelithiated anodes. With a higher ratio of lithium silicates due to the oxygen participation, prelithiated SiO$_{1.3}$ anode improves the initial coulombic efficiency to 94% in full cell and delivers good cycling retention after hundreds cycles under lean electrolyte conditions. The insights provided in this work could be used to further optimize high Si loading based anode in future high energy density batteries. △ Less

Submitted 13 April, 2023; originally announced April 2023.

A new turbulence model based on scale decomposition

Authors: Shanwen Tan

Abstract: Based on the characteristics of the multi-scale and similarity at different scales in turbulent flow, we propose a scale decomposition for solving the turbulence problem of incompressible Newtonian fluid. The solution domain is decomposed into two-level scales, the large scale component represents mean flow and large scale eddies, and the small scale one represents the turbulent fluctuations. The… ▽ More Based on the characteristics of the multi-scale and similarity at different scales in turbulent flow, we propose a scale decomposition for solving the turbulence problem of incompressible Newtonian fluid. The solution domain is decomposed into two-level scales, the large scale component represents mean flow and large scale eddies, and the small scale one represents the turbulent fluctuations. The problem is solved in large scale by the equations of motion and the effect of the turbulent fluctuations on the mean flow is evaluated approximately by using equivalent eddy. Furthermore, the effect of equivalent eddy is decomposed into two parts including convective effect and diffusion effect, which is expressed as a function of mean quantities in large scale. The modified Naiver-Stokes equations are established, there ensures the closure of the equations in large scale. Finally, the modified Naiver-Stokes equations is verified by the numerical simulation. Flow around cylinder is numerically investigated and able to obtain flow behavior from low to high Reynolds numbers. A general-purpose turbulence model is established in this study, which is worthy of engineering application. △ Less

Submitted 3 February, 2023; originally announced February 2023.

Comments: 13 pages 9 figures

Foveated Thermal Computational Imaging in the Wild Using All-Silicon Meta-Optics

Authors: Vishwanath Saragadam, Zheyi Han, Vivek Boominathan, Luocheng Huang, Shiyu Tan, Johannes E. Fröch, Karl F. Böhringer, Richard G. Baraniuk, Arka Majumdar, Ashok Veeraraghavan

Abstract: Foveated imaging provides a better tradeoff between situational awareness (field of view) and resolution and is critical in long-wavelength infrared regimes because of the size, weight, power, and cost of thermal sensors. We demonstrate computational foveated imaging by exploiting the ability of a meta-optical frontend to discriminate between different polarization states and a computational backe… ▽ More Foveated imaging provides a better tradeoff between situational awareness (field of view) and resolution and is critical in long-wavelength infrared regimes because of the size, weight, power, and cost of thermal sensors. We demonstrate computational foveated imaging by exploiting the ability of a meta-optical frontend to discriminate between different polarization states and a computational backend to reconstruct the captured image/video. The frontend is a three-element optic: the first element which we call the "foveal" element is a metalens that focuses s-polarized light at a distance of $f_1$ without affecting the p-polarized light; the second element which we call the "perifoveal" element is another metalens that focuses p-polarized light at a distance of $f_2$ without affecting the s-polarized light. The third element is a freely rotating polarizer that dynamically changes the mixing ratios between the two polarization states. Both the foveal element (focal length = 150mm; diameter = 75mm), and the perifoveal element (focal length = 25mm; diameter = 25mm) were fabricated as polarization-sensitive, all-silicon, meta surfaces resulting in a large-aperture, 1:6 foveal expansion, thermal imaging capability. A computational backend then utilizes a deep image prior to separate the resultant multiplexed image or video into a foveated image consisting of a high-resolution center and a lower-resolution large field of view context. We build a first-of-its-kind prototype system and demonstrate 12 frames per second real-time, thermal, foveated image, and video capture in the wild. △ Less

Submitted 12 December, 2022; originally announced December 2022.