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Differing vibrational properties of halide and chalcogenide perovskite semiconductors and impact on optoelectronic performance

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Physical Review materials 2024年第8期8卷 085402页

作者： Kevin Ye Matan Menahem Tommaso Salzillo Florian Knoop Boyang Zhao Shanyuan Niu Olle Hellman Jayakanth Ravichandran R. Jaramillo Omer Yaffe Department of Materials Science and Engineering Department of Chemical and Biological Physics Department of Industrial Chemistry “Toso Montanari” Department of Physics Chemistry and Biology (IFM) Mork Family Department of Chemical Engineering and Materials Science Department of Molecular Chemistry and Material Science Ming Hsieh Department of Electrical and Computer Engineering Core Center of Excellence in NanoImaging (CNI)

We report a comparative study of temperature-dependent photoluminescence and structural dynamics of two perovskite semiconductors, the chalcogenide BaZrS3 and the halide CsPbBr3. These materials have similar crystal structures and direct band gaps, but we find that they have quite distinct optoelectronic and vibrational properties. Both materials exhibit thermally activated nonradiative recombination, but the nonradiative recombination rate in BaZrS3 is four orders of magnitude faster than in CsPbBr3, for the crystals studied here. Raman spectroscopy reveals that the effects of phonon anharmonicity are far more pronounced in CsPbBr3 than in BaZrS3. Further, although both materials feature a large dielectric response due to low-energy polar optical phonons, the phonons in CsPbBr3 are substantially lower in energy than in BaZrS3. Our results suggest that electron-phonon coupling in BaZrS3 is more effective at nonradiative recombination than in CsPbBr3 and that BaZrS3 may also have a substantially higher concentration of nonradiative recombination centers than CsPbBr3. The low defect concentration in CsPbBr3 may be related to the ease of lattice reconfiguration, typified by anharmonic bonding. It remains to be seen to what extent these differences are inherent to the chalcogenide and halide perovskites and to what extent they can be affected by materials processing.

关键词： Electron-phonon coupling Lattice dynamics Photoemission Solar cells Photovoltaic absorbers Photoluminescence Raman spectroscopy

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Multiphoton Resonance Meets Tunneling Ionization: High-Efficient Photoexcitation in Strong-Field-Dressed Ions

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Physical Review Letters 2024年第11期133卷 113201页

作者： Yewei Chen Hongbin Lei Quanjun Wang Hongqiang Xie He Zhang Xu Lu Ning Zhang Shunlin Huang Yuzhu Wu Jianpeng Liu Qian Zhang Yi Liu Zengxiu Zhao Jing Zhao Jinping Yao State Key Laboratory of High Field Laser Physics School of Optical-Electrical and Computer Engineering Department of Physics School of Science Center of Materials Science and Optoelectronics Engineering School of Microelectronics

Tunneling ionization, a fascinating quantum phenomenon, has played the key role in the development of attosecond physics. Upon absorption of a few tens of photons, tunneling ionization creates ions in different excited states and even enables the formation of population inversion between ionic states. However, the underlying physics is still being debated. Here, we demonstrate a significant enhancement in the efficiency of multiphoton excitation when ionization of neutral molecules and resonant excitation of ions coexist in strong laser fields. It facilitates the dramatic increase in population inversion and lasing radiation in N2+ around 1000 nm pump wavelength. Utilizing the ionization-coupling theory, we discover that the synergistic interplay between tunneling ionization and multiphoton excitation enables the ionic coherence to be maximized by phase locking of the periodically created ionic dipoles and consistently maintain an optimal phase for the follow-up photoexcitation. This Letter provides new insights into the photoexcitation mechanism of ions in strong laser fields and opens up a route for optimizing ionic lasing radiations.

关键词： Multiphoton or tunneling ionization & excitation Optical transient phenomena Stark effect Ultrafast phenomena

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Self-healable stretchable printed electronic cryogels for in-vivo plant monitoring

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npj Flexible Electronics 2023年第1期7卷 59-69页

作者： Eloïse Bihar Elliot J.Strand Catherine A.Crichton Megan N.Renny Ignacy Bonter Tai Tran Madhur Atreya Adrian Gestos Jim Haseloff Robert R.McLeod Gregory L.Whiting Paul M.Rady Department of Mechanical Engineering University of Colorado Boulder1111 Engineering DriveBoulderCO 80309USA Materials Science and Engineering University of Colorado Boulder4001 Discovery DriveBoulderCO 80303USA Department of Plant Sciences University of CambridgeDowning StreetCambridge CB23EAUK Materials Instrumentation and Multimodal Imaging Core Facility University of Colorado Boulder1111 Engineering Drive BoulderBoulderCO 80309USA Department of Electrical Computerand Energy EngineeringUniversity of Colorado Boulder1111 Engineering DriveBoulderCO 80309USA

A key challenge in bioelectronics is to establish and improve the interface between electronic devices and living tissues,enabling a direct assessment of biological *** integrated with plant tissue can provide valuable information about the plant itself as well as the surrounding environment,including air and soil *** obstacle in developing interfaces to plant tissue is mitigating the formation of fibrotic tissues,which can hinder continuous and accurate sensor operation over extended *** systems that utilize suitable biocompatible materials alongside appropriate fabrication techniques to establish plantelectronic interfaces could provide for enhanced environmental understanding and ecosystem management *** meet these demands,this study introduces an approach for integrating printed electronic materials with biocompatible cryogels,resulting in stable implantable hydrogel-based bioelectronic devices capable of long-term operation within plant *** inkjet-printed cryogels can be customized to provide various electronic functionalities,including electrodes and organic electrochemical transistors(OECTs),that exhibit high electrical conductivity for embedded conducting polymer traces(up to 350 S/cm),transconductance for OECTs in the mS range,a capacitance of up to 4.2mF g−1 in suitable structures,high stretchability(up to 330%strain),and selfhealing *** biocompatible functionalized cryogel-based electrodes and transistors were successfully implanted in plant tissue,and ionic activity in tomato plant stems was collected for over two months with minimal scar tissue formation,making these cryogel-based printed electronic devices excellent candidates for continuous,in-situ monitoring of plant and environmental status and health.

关键词： electronic utilize integrating

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A Performance Study of Selected Machine Learning Techniques for Predicting Heart Diseases 2nd

A Performance Study of Selected Machine Learning Techniques ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Olorunfemi, Blessing Oluwatobi Ogunde, Adewale Opeoluwa Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Li, Chun-Ta Faculty of Natural Sciences Department of Computer Science Redeemer’s University Osun Nigeria Department of Computer Science Bowen University Iwo Nigeria Faculty of Information and Communication Sciences Department of Computer Science University of Ilorin Ilorin240003 Nigeria Faculty of Engineering Department of Electrical and Electronics Engineering University of Lagos Lagos Akoka100213 Nigeria Bachelor’s Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City24206 Taiwan

ISBN: (纸本)9789819777853

Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

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computers, materials & Continua 2023年第9期76卷 3897-3920页

作者： Javed Rashid Imran Khan Irshad Ahmed Abbasi Muhammad Rizwan Saeed Mubbashar Saddique Mohamed Abbas Department of CS&SE Islamic International UniversityIslamabad44000Pakistan Department of IT Services University of OkaraOkara56310Pakistan Faculty of Science and Arts Belqarn University of BishaSabtul Alaya61985Saudi Arabia Department of CS University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowalCampusNarowal51601Pakistan Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Research Center for Advanced Materials Sciences(RCAMS) King Khalid UniversityAbhaSaudi Arabia

Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them *** plant species can be challenging to classify and segment using deep *** using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to *** the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java *** new approaches are used to formthe hybridmodel suggested *** guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET *** a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and *** proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and *** datasets are known as PLSD and *** levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.

关键词： Plant leaf species stacking ensemble model guava potato java plum MobileNetV2-UNET hybrid deep learning segmentation

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Layered Double Hydroxide Based Self-Powered Triboelectric Sensor for Biomechanical Motions 21

Layered Double Hydroxide Based Self-Powered Triboelectric Se...

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21st International Bhurban Conference on Applied sciences and Technology, IBCAST 2024

作者： Leonardi, Claudio Ur Rahman, Muhammad Saad Rehan, Fatima Giodano, Antonio Zia, Muhammad Ihzam Saleem, Mohsin Medaglia, Pier Gianni Khan, Usman University of Rome Tor Vergata Department of Industrial Engineering Via del Politecnico Roma Italy National University of Sciences and Technology School of Chemical and Materials Engineeing Islamabad Pakistan National University of Sciences and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan National University of Sciences and Technology School of Chemical and Materials Engineering Islamabad Pakistan Chung Ang University School of Electrical and Electronics Engineering Seoul Korea Republic of

ISBN: (纸本)9798331516680

Sensing of biomechanical motions is advantageous for biomedical health monitoring. Here, we propose a layered double hydroxide (LDH) based triboelectric sensor (TES) for monitoring biomechanical motions. The triboelectric sensor is based on initially triboelectrification between the friction pair of LDH and aluminum and subsequently electrostatic induction in an external electric circuit due to the contact-separation of the two friction layers. To realize the triboelectric sensor, ZnAl-LDH is synthesized using an in-situ hydrothermal method resulting in a nanostructured ZnAl-LDH with high surface-to-volume ratio. The triboelectric sensor is self-powered as it generates a peak-to-peak output voltage (Vo) of ∼ 11 V across a load resistance of 1MΩ and a short circuit current (ISC) of ∼ 2.5μA in response to hand tapping. Since the triboelectric pulses (high voltages and very low currents) are not suited for conventional electronics, a two-stage-amplifier based interface circuit is designed to interface the sensor to a microcontroller. As proof of concept, the self-powered triboelectric sensor is interfaced with Arduino Mega 2560 microcontroller through the interface circuit. The sensorial system monitored the contact-separation induced by hand tapping and the counting information was displayed on a digital LCD. The self-powered LDH based triboelectric sensor along with the two-stage-amplifier based interface circuit provides a platform for monitoring various biomechanical motions including walking with applications in biomedical health monitoring. © 2024 IEEE.

关键词： Microcontrollers

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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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Magnon gap tuning in lithium-doped MnTe

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Physical Review B 2024年第21期109卷 214434-214434页

作者： George Yumnam Duncan H. Moseley Joseph A. M. Paddison Christiana Z. Suggs Emma Zappala David S. Parker Garrett E. Granroth Gerald D. Morris Md Mobarak Hossain Polash Daryoosh Vashaee Michael A. McGuire Huaizhou Zhao Michael E. Manley Benjamin A. Frandsen Raphaël P. Hermann Materials Science and Technology Division Department of Physics and Astronomy Neutron Scattering Division Centre for Molecular and Materials Science Department of Electrical and Computer Engineering Beijing National Laboratory for Condensed Matter Physics

Manganese telluride (MnTe) is a prospective platform for ultrafast carrier dynamics, spin-based thermoelectrics, and magnon-drag transport due to its unique electronic and magnetic properties. We use inelastic neutron scattering to study both pure and lithium-doped MnTe, focusing on the influence of doping in opening a magnon gap. We use neutron powder diffraction to determine critical exponents for the phase transition in both pure and Li-doped MnTe and complement this information with muon spin rotation/relaxation. The opening of the magnon gap and spin reorientation in Li-doped MnTe is mainly due to increased magnetic anisotropy along the [001] axis, a feature not present in pure MnTe.

关键词： Antiferromagnetism Exchange interaction First-principles calculations Magnetic anisotropy Magnons Density functional theory Inelastic neutron scattering Linear spin wave theory Muon spin relaxation & rotation Neutron diffraction

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Efficient simulations of Hartree-Fock equations by an accelerated gradient descent method

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Physical Review E 2024年第5期110卷 055304页

作者： Y. Ohno A. Del Maestro T. I. Lakoba Department of Mathematics and Statistics Department of Physics and Astronomy Min H. Kao Department of Electrical Engineering and Computer Science Institute for Advanced Materials & Manufacturing

We develop convergence acceleration procedures that enable a gradient descent-type iteration method to efficiently simulate Hartree-Fock equations for many particles interacting both with each other and with an external potential. Our development focuses on three aspects: (i) optimization of a parameter in the preconditioning operator; (ii) adoption of a technique that eliminates the slowest-decaying mode to the case of many equations (describing many particles); and (iii) a novel extension of the above technique that allows one to eliminate multiple modes simultaneously. We illustrate performance of the numerical method for the two-dimensional model of the first layer of helium atoms above a graphene sheet. We demonstrate that incorporation of aspects (i) and (ii) above into the “plain” gradient descent method accelerates it by at least two orders of magnitude, and often by much more. Aspect (iii)—the multiple-mode elimination—may bring further improvement to the convergence rate compared to aspect (ii), the single-mode elimination. Both single- and multiple-mode elimination techniques are shown to significantly outperform the well-known Anderson Acceleration. We believe that our acceleration techniques can also be employed by other iterative methods, especially those handling hard-core-type interaction of many particles.

关键词： First-principles calculations Hartree-Fock methods Numerical techniques

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Impact of High-k materials and Tube Numbers on CNFET Gate's Performances 5

Impact of High-k Materials and Tube Numbers on CNFET Gate's ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Yalung, Christofer Tantraviwat, Doldet Uatrongjit, Sermsak Yamwong, Wittawat Doctor of Philosophy Program in Nanoscience and Nanotechnology International Program/Interdisciplinary Center of Excellence in Materials Science and Technology Faculty of Science Chiang Mai University Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand Chachoengsao Thailand

ISBN: (纸本)9781665474672

Carbon nanotube field effect transistor (CNFET) performance is primarily influenced by a variety of characteristics, such as nanotube diameter, operating voltage, pitch, the number of tubes, dielectric constant, and contact materials. In this study, the dielectric constant and the number of carbon nanotubes (CNTs) were emphasized to analyze the propagation delay and average power dissipation of basic logic gates. Zirconium dioxide (ZrO2), hafnium oxide (HfO2), and silicon dioxide (SiO2) were used as dielectric materials. In this experiment, the Stanford CNFET model was utilized, and variable factors such as the number of CNTs and the dielectric constant (Kox) accumulated on the nanotube gate's surface were diversified. The correlation coefficients of the delay, the average power, and the CNT numbers were also presented. Furthermore, the multi-objective genetic algorithm and the Rmethod were utilized to find the optimal Kox and the number of CNTs for minimizing the average dissipation power and the propagation delay of some logic gates designed with CNFET. For all the gates considered, the optimal number of CNTs is 4 while the dielectric constant is within the ranges of 19 to 24. © 2023 IEEE.

关键词： Carbon nanotubes

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