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Quantifying electrochemical losses in perovskite solar cells

Journal of materials Chemistry C 2023年第8期11卷 2911-2920页

作者： Tulus, None Wang, Junke Galagan, Yulia von Hauff, Elizabeth Physics of Energy Department of Physics & Astronomy Faculty of Sciences Vrije Universiteit Amsterdam Amsterdam1081 HV Netherlands Jakarta10340 Indonesia TNO - Solliance Eindhoven5656 AE Netherlands National Taiwan University Department of Materials Science and Engineering Taipei106 Taiwan Faculty of Electrical and Computer Engineering Technical University of Dresden Dresden Germany Dresden Germany

We quantify electrochemical losses in perovskite solar cells (PSCs) based on methylammonium lead triiodide (MAPbI3) films with impedance analysis. We focus on the characteristic signatures of impedance spectra taken from PSCs, in particular the negative capacitance hook widely observed in the low frequency regime. We elucidate the underlying physical origin for the negative capacitance by applying a generalized equivalent circuit model (ECM) for PSCs that accounts for fast electrical dynamics resulting in high frequency (HF) signatures due to electronic processes, and much slower electrochemical dynamics that result in low frequency (LF) signatures in the spectra. We observe relaxation times faster than 10−6 s in the HF regime that can be attributed to electrical dynamics, while relaxation times longer than 10−3 s in the LF regime that are consistent with electrochemical dynamics. The voltage-dependence and timescales of the electrochemical dynamics are consistent with MA+ and I− migration in the MAPbI3 absorber layer. At higher applied voltages, we observe a highly non-linear response from the PSC which is consistent with irreversible chemical changes in the MAPbI3 absorber. We demonstrate how ECM modelling combined with the analysis of ECM fit quality is a useful approach for in situ monitoring and quantitative diagnosis of loss mechanisms in PSC. © 2023 The Royal Society of Chemistry.

关键词： Relaxation time

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Frequency multiplexed emission from cavity-enhanced T centers

Frequency multiplexed emission from cavity-enhanced T center...

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Komza, Lukasz Zhang, Xueyue Tang, Yu-Lung Song, Hanbin Sipahigil, Alp Department of Physics University of California Berkeley BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Materials Science and Engineering University of California Berkeley BerkeleyCA94720 United States

To enable efficient s pin-photon interfaces with T centers, cavity-enhanced emission is essential. We build an integrated photonics platform demonstrating cavity-enhanced emission from multiple T centers across distin... 详细信息

ISBN: (纸本)9781957171395

关键词： Photonic integrated circuits

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MEMS-actuated terahertz metamaterials driven by phase-transition materials

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Frontiers of Optoelectronics 2024年第2期17卷 23-32页

作者： Zhixiang Huang Weipeng Wu Eric Herrmann Ke Ma Zizwe A.Chase Thomas A.Searles M.Benjamin Jungfleisch Xi Wang Department of Materials Science and Engineering College of EngineeringUniversity of DelawareNewarkDE 19716USA Department of Physics and Astronomy College of Arts and SciencesUniversity of DelawareNewarkDE 19716USA Department of Electrical and Computer Engineering College of EngineeringUniversity of Illinois ChicagoChicagoIL 60607USA

The non-ionizing and penetrative characteristics of terahertz(THz)radiation have recently led to its adoption across a variety of *** effectively utilize THz radiation,modulators with precise control are *** most recent THz modulators manipulate the amplitude,frequency,or phase of incident THz radiation,considerably less progress has been made toward THz polarization *** methods for polarization control suffer from high driving voltages,restricted modulation depth,and narrow band capabilities,which hinder device performance and broader ***,an ideal THz modulator that offers high modulation depth along with ease of processing and operation is *** this paper,we propose and realize a THz metamaterial comprised of microelectromechanical systems(MEMS)actuated by the phase-transition material vanadium dioxide(VO_(2)).Simulation and experimental results of the three-dimensional metamaterials show that by leveraging the unique phase-transition attributes of VO_(2),our THz polarization modulator offers notable advancements over existing designs,including broad operation spectrum,high modulation depth,ease of fabrication,ease of operation condition,and continuous modulation *** enhanced features make the system a viable candidate for a range of THz applications,including telecommunications,imaging,and radar systems.

关键词： Metamaterials MEMS THz VO_(2) Phase-transition material

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Automatic 2D Material Detection in Optical Images using Deep Learning Based computer Vision 4

Automatic 2D Material Detection in Optical Images using Deep...

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AI and Optical Data sciences IV 2023

作者： Ramezani, Fereshteh Parvez, Sheikh Fix, J. Pierce Battaglin, Arthur Whyte, Seamus Borys, Nicholas J. Whitaker, Bradley Electrical and Computer Engineering Department Montana State University Bozeman United States Department of Physics Montana State University Bozeman United States Materials Science Program Montana State University Bozeman United States Optical Technology Center Montana State University Bozeman United States

ISBN: (纸本)9781510659810

computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. In order to use these materials for research and product development, single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of 2D materials based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectorRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes ( © 2023 SPIE.

关键词： Object detection

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Understanding wide field-of-view flat lenses:an analytical solution[Invited]

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Chinese Optics Letters 2023年第2期21卷 111-118页

作者：杨帆安森松 Mikhail Y.Shalaginov 张华亮胡崛隽古田 Department of Materials Science&Engineering Massachusetts Institute of TechnologyCambridgeMassachusetts 02139USA Department of Electrical&Computer Engineering University of Massachusetts LowellLowellMassachusetts 01854USA Materials Research Laboratory Massachusetts Institute of TechnologyCambridgeMassachusetts 02139USA

Wide field-of-view(FOV)optics are widely used in various imaging,display,and sensing *** wide FOV optics rely on complicated lens assembly comprising multiple elements to suppress coma and other Seidel *** emergence of flat optics exemplified by metasurfaces and diffractive optical elements(DOEs)offers a promising route to expand the FOV without escalating complexity of optical *** date,design of large FOV flat lenses has been relying upon iterative numerical ***,we derive,for the first time,to the best of our knowledge,an analytical solution to enable computationally efficient design of flat lenses with an ultra-wide FOV approaching 180°.This analytical theory further provides critical insights into working principles and otherwise non-intuitive design trade-offs of wide FOV optics.

关键词： metasurface metalens field-of-view

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning 14

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Novianty, Astri Putro, Budi Laksono Komala Sari, Siska Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia Indonesia University of Education Department of Computer Science Education Bandung Indonesia Telkom University School of Applied System Diploma Program of Information System Bandung Indonesia

ISBN: (纸本)9798350335095

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice. © 2023 IEEE.

关键词： Support vector machines

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Integrated Platform for Spin-Photon Interfaces in Silicon

Integrated Platform for Spin-Photon Interfaces in Silicon

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Song, Hanbin Zhang, Xueyue Zhi, Yiyang Komza, Lukasz Sipahigil, Alp Department of Materials Science and Engineering University of California Berkeley BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California Berkeley BerkeleyCA94720 United States

We demonstrate optical initialization and readout of electron spins of a single T center in a silicon nanophotonic waveguide. We present progress for coherent control of the waveguide single spin for silicon spin-phot... 详细信息

ISBN: (纸本)9781957171395

关键词： Electron optics

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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electrical conductivity enhancement in yttrium iron garnet: Effects of Ca substitution and Fe deficiency

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Journal of Alloys and Compounds 2025年 1032卷

作者： Kang, Young-Min Defferriere, Thomas Shin, Hyeon-Su Gross, Miela J. Lee, Gyu Rac Tuller, Harry L. Ross, Caroline A. Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering Korea National University of Transportation Chungju27469 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This study investigates the electrical conductivity (σ) of cation-substituted YIG in polycrystalline bulk form, optimizing cation substitution and Fe content for enhanced conductivity. Among tested cations (Ba, Sr, Ca, Mn, Zn, Mg, Si, Ti, and Zr), Ca substitution most significantly improved the conductivity. The undoped YIG sample exhibited a σ of approximately 4 × 10−10 S/cm, whereas Ca-YIG (Y2.7Ca0.3Fe5O12-δ) showed a σ of around 10⁻⁴ S/cm, representing a six-order increase in conductivity. Fe-deficient YIG (Y3Fe5-yO12-δ, y = 0.3) formed a secondary YFeO3 phase but showed a three-order increase in σ. Notably, Fe-deficient Ca-YIG (Y2.7Ca0.3Fe5-yO12-δ, y = 0.3) achieved 5 times higher conductivity than Ca-YIG (y = 0) without secondary phase formation. Considering charge balance and applying molecular field coefficient modeling to fit the magnetization vs. temperature data, we suggest that Ca substitution promotes Fe4+ formation at d sites which facilitates p-type conduction, while Fe deficiency induces Fe vacancies at a sites, further increasing the Fe4+ concentration. High-temperature conductivity measurements revealed thermally activated conduction, with Ca-YIG samples exhibiting a lower activation energy (~0.33 eV) compared to undoped YIG (~0.93 eV), suggesting a transition to delocalized transport. Impedance spectroscopy confirmed bulk-dominated conduction in Ca-YIG (y = 0.3), with reduced hole mobility at elevated temperatures due to charged Fe ion scattering. © 2025 Elsevier B.V.

关键词： Proton conductivity

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Recycled silicon solar cell-derived nanostructured p-black silicon device for high performance NO₂ gas sensor applications

RSC Applied Interfaces

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RSC Applied Interfaces 2024年第1期2卷 220-229页

作者： Aleem, Mahaboobbatcha Vishnuraj, Ramakrishnan Pullithadathil, Biji Nanosensors & Clean Energy Laboratory Department of Chemistry & Nanoscience and Technology PSG Institute of Advanced Studies Coimbatore 641 India Advanced Self-Powered Systems of Integrated Sensors and Technology Center Department of Electrical and Computer Engineering North Carolina State University Raleigh 27695 NC United States Center of Excellence in Advanced Materials and Green Technologies Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Chemical Engineering and Materials Science Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

Nitrogen dioxide (NO2) is a toxic gas that can cause respiratory problems, and sensing its presence is crucial for environmental monitoring and industrial safety. This investigation presents a novel approach towards s...

Nitrogen dioxide (NO₂) is a toxic gas that can cause respiratory problems, and sensing its presence is crucial for environmental monitoring and industrial safety. This investigation presents a novel approach towards sensing NO₂ gas by utilizing partially completed/recycled silicon solar cells employing a metal-assisted etching process to fabricate a high-performance p-black-silicon based sensor. Structural and morphological analyses using X-ray diffraction patterns, Raman spectroscopy and cross sectional FESEM characterization confirm the integrity of the p-B-silicon sensor. By combining recycling techniques with advanced fabrication methods, the resulting sensor exhibits exceptional sensitivity, a low detection limit of 1 ppm, and rapid response times (12-14 s) when exposed to NO₂ gas concentrations ranging from 1 to 5 ppm. The enhanced sensitivity is attributed to the unique nanostructured comb-like morphology of the sensor material, which facilitates fast charge transport mechanisms, and a plausible sensing mechanism has been proposed and explained using a depletion model diagram and energy model diagram. This eco-friendly and cost-effective solution not only addresses electronic waste concerns but also highlights the potential of sustainable practices in scientific research. The findings emphasize on the importance of environmental consciousness and innovation, showcasing a promising future for gas sensing technology. By utilizing recycled materials and advanced fabrication techniques, this study contributes to the development of efficient, eco-friendly sensors for environmental monitoring applications, paving the way for a more sustainable and technologically advanced future in the field of gas sensors. © 2025 RSC.

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