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Increased resistance to photooxidation in Dion-Jacobson lead halide perovskites -- implication for perovskite device stability
Authors:
Zhilin Ren,
Juraj Ovčar,
Tik Lun Leung,
Yanling He,
Yin Li,
Dongyang Li,
Xinshun Qin,
Hongbo Mo,
Zhengtian Yuan,
Jueming Bing,
Martin P. Bucknall,
Luca Grisanti,
Muhammad Umair Ali,
Peng Bai,
Tao Zhu,
Ali Ashger Syed,
Jingyang Lin,
Jingbo Wang,
Abdul-Khaleed,
Wenting Sun,
Gangyue Li,
Gang Li,
Alan Man Ching Ng,
Anita W. Y. Ho-Baillie,
Ivor Lončarić
, et al. (2 additional authors not shown)
Abstract:
2D metal halide perovskites have enabled significant stability improvements in perovskite devices, particularly in resistance to moisture. However, some 2D perovskites are even more susceptible to photooxidation compared to 3D perovskites. This is particularly true for more commonly investigated Ruddlesden-Popper (RP) perovskites that exhibit increased susceptibility to photoinduced degradation co…
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2D metal halide perovskites have enabled significant stability improvements in perovskite devices, particularly in resistance to moisture. However, some 2D perovskites are even more susceptible to photooxidation compared to 3D perovskites. This is particularly true for more commonly investigated Ruddlesden-Popper (RP) perovskites that exhibit increased susceptibility to photoinduced degradation compared to Dion-Jacobson (DJ) perovskites. Comparisons between different RP and DJ perovskites reveal that this phenomenon cannot be explained by commonly proposed differences in superoxide ion generation, interlayer distance and lattice structural rigidity differences. Instead, the resistance to photooxidation of DJ perovskites can be attributed to decreased likelihood of double deprotonation events (compared to single deprotonation events in RP perovskites) required for the loss of organic cations and the perovskite decomposition. Consequently, DJ perovskites are less susceptible to oxidative degradation (both photo- and electrochemically induced), which leads to improved operational stability of solar cells based on these materials.
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Submitted 19 September, 2024;
originally announced September 2024.
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3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations
Authors:
Bryan Gopal,
Ryan W. Han,
Gautham Raghupathi,
Andrew Y. Ng,
Geoffrey H. Tison,
Pranav Rajpurkar
Abstract:
We propose 3KG, a physiologically-inspired contrastive learning approach that generates views using 3D augmentations of the 12-lead electrocardiogram. We evaluate representation quality by fine-tuning a linear layer for the downstream task of 23-class diagnosis on the PhysioNet 2020 challenge training data and find that 3KG achieves a $9.1\%$ increase in mean AUC over the best self-supervised base…
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We propose 3KG, a physiologically-inspired contrastive learning approach that generates views using 3D augmentations of the 12-lead electrocardiogram. We evaluate representation quality by fine-tuning a linear layer for the downstream task of 23-class diagnosis on the PhysioNet 2020 challenge training data and find that 3KG achieves a $9.1\%$ increase in mean AUC over the best self-supervised baseline when trained on $1\%$ of labeled data. Our empirical analysis shows that combining spatial and temporal augmentations produces the strongest representations. In addition, we investigate the effect of this physiologically-inspired pretraining on downstream performance on different disease subgroups and find that 3KG makes the greatest gains for conduction and rhythm abnormalities. Our method allows for flexibility in incorporating other self-supervised strategies and highlights the potential for similar modality-specific augmentations for other biomedical signals.
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Submitted 20 September, 2021; v1 submitted 21 April, 2021;
originally announced June 2021.
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Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning
Authors:
Maxime Voisin,
Yichen Shen,
Alireza Aliamiri,
Anand Avati,
Awni Hannun,
Andrew Ng
Abstract:
We develop an algorithm that accurately detects Atrial Fibrillation (AF) episodes from photoplethysmograms (PPG) recorded in ambulatory free-living conditions. We collect and annotate a dataset containing more than 4000 hours of PPG recorded from a wrist-worn device. Using a 50-layer convolutional neural network, we achieve a test AUC of 95% and show robustness to motion artifacts inherent to PPG…
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We develop an algorithm that accurately detects Atrial Fibrillation (AF) episodes from photoplethysmograms (PPG) recorded in ambulatory free-living conditions. We collect and annotate a dataset containing more than 4000 hours of PPG recorded from a wrist-worn device. Using a 50-layer convolutional neural network, we achieve a test AUC of 95% and show robustness to motion artifacts inherent to PPG signals. Continuous and accurate detection of AF from PPG has the potential to transform consumer wearable devices into clinically useful medical monitoring tools.
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Submitted 27 November, 2018; v1 submitted 12 November, 2018;
originally announced November 2018.
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Ab initio model of optical properties of two-temperature warm dense matter
Authors:
Bastian Holst,
Vanina Recoules,
Stephane Mazevet,
Marc Torrent,
Andrew Ng,
Zhijiang Chen,
Sean E. Kirkwood,
V. Sametoglu,
M. Reid,
Ying Y. Tsui
Abstract:
We present a model to describe thermophysical and optical properties of two-temperature systems consisted of heated electrons and cold ions in a solid lattice that occur during ultrafast heating experiments. Our model is based on ab initio simulations within the framework of density functional theory. The optical properties are obtained by evaluating the Kubo-Greenwood formula. By applying the mat…
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We present a model to describe thermophysical and optical properties of two-temperature systems consisted of heated electrons and cold ions in a solid lattice that occur during ultrafast heating experiments. Our model is based on ab initio simulations within the framework of density functional theory. The optical properties are obtained by evaluating the Kubo-Greenwood formula. By applying the material parameters of our ab initio model to a two-temperature model we are able to describe the temperature relaxation process of femtosecond-laser-heated gold and its optical properties within the same theoretical framework. Recent time-resolved measurements of optical properties of ultrafast heated gold revealed the dynamics of the interaction between femtosecond laser pulses and solid state matter. Different scenarios obtained from simulations of our study are compared with experimental data [Chen, Holst, Kirkwood, Sametoglu, Reid, Tsui, Recoules, and Ng, Phys. Rev. Lett. 110, 135001 (2013)].
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Submitted 12 February, 2018;
originally announced February 2018.
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MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
Authors:
Pranav Rajpurkar,
Jeremy Irvin,
Aarti Bagul,
Daisy Ding,
Tony Duan,
Hershel Mehta,
Brandon Yang,
Kaylie Zhu,
Dillon Laird,
Robyn L. Ball,
Curtis Langlotz,
Katie Shpanskaya,
Matthew P. Lungren,
Andrew Y. Ng
Abstract:
We introduce MURA, a large dataset of musculoskeletal radiographs containing 40,561 images from 14,863 studies, where each study is manually labeled by radiologists as either normal or abnormal. To evaluate models robustly and to get an estimate of radiologist performance, we collect additional labels from six board-certified Stanford radiologists on the test set, consisting of 207 musculoskeletal…
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We introduce MURA, a large dataset of musculoskeletal radiographs containing 40,561 images from 14,863 studies, where each study is manually labeled by radiologists as either normal or abnormal. To evaluate models robustly and to get an estimate of radiologist performance, we collect additional labels from six board-certified Stanford radiologists on the test set, consisting of 207 musculoskeletal studies. On this test set, the majority vote of a group of three radiologists serves as gold standard. We train a 169-layer DenseNet baseline model to detect and localize abnormalities. Our model achieves an AUROC of 0.929, with an operating point of 0.815 sensitivity and 0.887 specificity. We compare our model and radiologists on the Cohen's kappa statistic, which expresses the agreement of our model and of each radiologist with the gold standard. Model performance is comparable to the best radiologist performance in detecting abnormalities on finger and wrist studies. However, model performance is lower than best radiologist performance in detecting abnormalities on elbow, forearm, hand, humerus, and shoulder studies. We believe that the task is a good challenge for future research. To encourage advances, we have made our dataset freely available at https://stanfordmlgroup.github.io/competitions/mura .
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Submitted 22 May, 2018; v1 submitted 11 December, 2017;
originally announced December 2017.
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Label-free bacteria detection using evanescent mode of a suspended core terahertz fiber
Authors:
Anna Mazhorova,
Andrey Markov,
Andy Ng,
Raja Chinnappan,
Mohammed Zourob,
Maksim Skorobogatiy
Abstract:
We propose for the first time an E. coli bacteria sensor based on the evanescent field of the fundamental mode of a suspended-core terahertz fiber. The sensor is capable of E. coli detection at concentrations in the range of 104-109 cfu/ml. The polyethylene fiber features a 150 μm core suspended by three deeply sub-wavelength bridges in the center of a 5.1 mm-diameter cladding tube. The fiber core…
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We propose for the first time an E. coli bacteria sensor based on the evanescent field of the fundamental mode of a suspended-core terahertz fiber. The sensor is capable of E. coli detection at concentrations in the range of 104-109 cfu/ml. The polyethylene fiber features a 150 μm core suspended by three deeply sub-wavelength bridges in the center of a 5.1 mm-diameter cladding tube. The fiber core is biofunctionalized with T4 bacteriophages which bind and eventually destroy (lyse) their bacterial target. Using environmental SEM we demonstrate that E. coli is first captured by the phages on the fiber surface. After 25 minutes, most of the bacteria is infected by phages and then destroyed with ~1μm-size fragments remaining bound to the fiber surface. The bacteria-binding and subsequent lysis unambiguously correlate with a strong increase of the fiber absorption. This signal allows the detection and quantification of bacteria concentration. Presented bacteria detection method is label-free and it does not rely on the presence of any bacterial "fingerprint" features in the THz spectrum.
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Submitted 28 November, 2011;
originally announced November 2011.
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Idealized Slab Plasma approach for the study of Warm Dense Matter
Authors:
A. Ng,
T. Ao,
F. Perrot,
M. W. C. Dharma-wardana,
M. E. Foord
Abstract:
Recently, warm dense matter (WDM) has emerged as an interdisciplinary field that draws increasing interest in plasma physics, condensed matter physics, high pressure science, astrophysics, inertial confinement fusion, as well as materials science under extreme conditions. To allow the study of well-defined WDM states, we have introduced the concept of idealized-slab plasmas that can be realized…
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Recently, warm dense matter (WDM) has emerged as an interdisciplinary field that draws increasing interest in plasma physics, condensed matter physics, high pressure science, astrophysics, inertial confinement fusion, as well as materials science under extreme conditions. To allow the study of well-defined WDM states, we have introduced the concept of idealized-slab plasmas that can be realized in the laboratory via (i) the isochoric heating of a solid and (ii) the propagation of a shock wave in a solid. The application of this concept provides new means for probing the dynamic conductivity, equation of state, ionization and opacity. These approaches are presented here using results derived from first-principles (density-functional type) theory, Thomas-Fermi type theory, and numerical simulations.
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Submitted 9 May, 2005;
originally announced May 2005.