Jan David Fischbach, Fridtjof Betz, Nigar Asadova, Pietro Tassan, Darius Urbonas, Thilo Stöferle, Rainer F. Mahrt, Sven Burger, Carsten Rockstuhl, Felix Binkowski, Thomas Jebb Sturges
Numerous natural and technological phenomena are governed by resonances. In nanophotonics, resonances often result from the interaction of several optical elements. Controlling these resonances is an excellent opportunity to provide light with properties on demand for applications ranging from sensing to quantum technologies. The inverse design of large, distributed resonators, however, is typically challenged by high computational costs when discretizing the entire system in space. Here, this limitation is overcome by harnessing prior knowledge about the individual scatterers that form the resonator and their interaction. In particular, a transition matrix multi‐scattering framework is coupled with the state‐of‐the‐art adaptive Antoulas–Anderson (AAA) algorithm to identify complex poles of the optical response function. A sample refinement strategy suitable for accurately locating a large number of poles is introduced. The AAA algorithm is tied into an automatic differentiation framework to efficiently differentiate multi‐scattering resonance calculations. The resulting resonance solver allows for efficient gradient‐based optimization, demonstrated here by the inverse design of an integrated exciton‐polariton cavity. This contribution serves as an important step towards efficient resonance calculations in a variety of multi‐scattering scenarios, such as inclusions in stratified media, periodic lattices, and scatterers with arbitrary shapes.
{"title":"A Framework to Compute Resonances Arising from Multiple Scattering","authors":"Jan David Fischbach, Fridtjof Betz, Nigar Asadova, Pietro Tassan, Darius Urbonas, Thilo Stöferle, Rainer F. Mahrt, Sven Burger, Carsten Rockstuhl, Felix Binkowski, Thomas Jebb Sturges","doi":"10.1002/adts.202400989","DOIUrl":"https://doi.org/10.1002/adts.202400989","url":null,"abstract":"Numerous natural and technological phenomena are governed by resonances. In nanophotonics, resonances often result from the interaction of several optical elements. Controlling these resonances is an excellent opportunity to provide light with properties on demand for applications ranging from sensing to quantum technologies. The inverse design of large, distributed resonators, however, is typically challenged by high computational costs when discretizing the entire system in space. Here, this limitation is overcome by harnessing prior knowledge about the individual scatterers that form the resonator and their interaction. In particular, a transition matrix multi‐scattering framework is coupled with the state‐of‐the‐art adaptive Antoulas–Anderson (AAA) algorithm to identify complex poles of the optical response function. A sample refinement strategy suitable for accurately locating a large number of poles is introduced. The AAA algorithm is tied into an automatic differentiation framework to efficiently differentiate multi‐scattering resonance calculations. The resulting resonance solver allows for efficient gradient‐based optimization, demonstrated here by the inverse design of an integrated exciton‐polariton cavity. This contribution serves as an important step towards efficient resonance calculations in a variety of multi‐scattering scenarios, such as inclusions in stratified media, periodic lattices, and scatterers with arbitrary shapes.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"47 45 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents a novel approach to implementing an artificial neural network (ANN) model for simulating high electron mobility transistors (HEMTs) in Keysight ADS through integrating Verilog‐A coding. It streamlines the realization of ANN models characterized by diverse complexities and layer structures. The proposed method is demonstrated by developing nonlinear models for GaN HEMT on two distinct substrates. GaN‐on‐Si and GaN‐on‐SiC with respective and gate widths are characterized by S‐parameters at a grid of gate and drain bias conditions. The intrinsic gate capacitance and conductances are extracted from the de‐embedded S‐parameters, which are then integrated to find the gate charges and currents. The drain current with the inherent self‐heating and trapping effects is modeled based on the pulsed IV measurement at well‐defined quiescent voltages. Subsequently, the related ANN models of these nonlinear elements are interconnected to form the intrinsic part of the large‐signal model. This intrinsic part with all ANN sub‐models is then completely implemented using a Verilog‐A‐based code. The whole ANN large‐signal model is then validated by single‐ and two‐tone radio frequency large‐signal measurements, which shows a perfect fitting with a high convergence rate. The overall simulation time is five times reduced when the developed Verilog‐A‐based ANN is used instead of the table‐based model. Overall, the large‐signal Verilog‐A‐based ANN model exhibits an improved performance enhancement compared to the conventional table‐based models. This indicates the practical viability of the Verilog‐A integration technique in modeling the nonlinear GaN HEMTs.
本研究提出了一种新方法，通过集成 Verilog-A 编码，在 Keysight ADS 中实现用于模拟高电子迁移率晶体管 (HEMT) 的人工神经网络 (ANN) 模型。它简化了具有不同复杂性和层结构特征的 ANN 模型的实现过程。通过为两种不同基底上的 GaN HEMT 开发非线性模型，演示了所提出的方法。硅基氮化镓和碳化硅基氮化镓具有各自的栅极宽度，在栅极和漏极偏置条件下通过 S 参数进行表征。从去嵌入式 S 参数中提取固有栅极电容和电导，然后对其进行积分，以求得栅极电荷和电流。具有固有自热和陷波效应的漏极电流是根据在定义明确的静态电压下进行的脉冲 IV 测量建立模型的。随后，这些非线性元素的相关 ANN 模型相互连接，形成大信号模型的内在部分。然后，使用基于 Verilog-A 的代码完全实现这一包含所有 ANN 子模型的内在部分。然后通过单音和双音射频大信号测量对整个 ANN 大信号模型进行验证，结果表明该模型具有完美的拟合度和较高的收敛速度。使用所开发的基于 Verilog-A 的 ANN 代替基于表格的模型，整体仿真时间缩短了五倍。总体而言，与传统的基于表格的模型相比，基于 Verilog-A 的大信号 ANN 模型表现出更高的性能提升。这表明 Verilog-A 集成技术在非线性 GaN HEMT 建模中的实际可行性。
{"title":"Improved Verilog‐A Based Artificial Neural Network Modeling Applied to GaN HEMTs","authors":"Anwar Jarndal, Md Hasnain Ansari, Kassen Dautov, Eqab Almajali, Yogesh Singh Chauhan, Sohaib Majzoub, Soliman A. Mahmoud, Talal Bonny","doi":"10.1002/adts.202400645","DOIUrl":"https://doi.org/10.1002/adts.202400645","url":null,"abstract":"This study presents a novel approach to implementing an artificial neural network (ANN) model for simulating high electron mobility transistors (HEMTs) in Keysight ADS through integrating Verilog‐A coding. It streamlines the realization of ANN models characterized by diverse complexities and layer structures. The proposed method is demonstrated by developing nonlinear models for GaN HEMT on two distinct substrates. GaN‐on‐Si and GaN‐on‐SiC with respective and gate widths are characterized by S‐parameters at a grid of gate and drain bias conditions. The intrinsic gate capacitance and conductances are extracted from the de‐embedded S‐parameters, which are then integrated to find the gate charges and currents. The drain current with the inherent self‐heating and trapping effects is modeled based on the pulsed IV measurement at well‐defined quiescent voltages. Subsequently, the related ANN models of these nonlinear elements are interconnected to form the intrinsic part of the large‐signal model. This intrinsic part with all ANN sub‐models is then completely implemented using a Verilog‐A‐based code. The whole ANN large‐signal model is then validated by single‐ and two‐tone radio frequency large‐signal measurements, which shows a perfect fitting with a high convergence rate. The overall simulation time is five times reduced when the developed Verilog‐A‐based ANN is used instead of the table‐based model. Overall, the large‐signal Verilog‐A‐based ANN model exhibits an improved performance enhancement compared to the conventional table‐based models. This indicates the practical viability of the Verilog‐A integration technique in modeling the nonlinear GaN HEMTs.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"22 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
vacancy‐ordered double perovskites (VODPs) have captured substantial research interest in the scientific community as they offer environmentally friendly and stable alternatives to lead halide perovskites. In this study, the investigation is focused on (B = Ti, Se, Ru, Pd) VODPs as promising optoelectronic materials employing state‐of‐the‐art first‐principles‐based methodologies, specifically density functional theory combined with density functional perturbation theory (DFPT) and many‐body perturbation theory (within the framework of GW and BSE). These calculations reveal that all these materials possess a cubic lattice structure and are both dynamically and mechanically stable. Interestingly, they all exhibit indirect bandgaps, except displays a metallic character. The bandgap values for these compounds fall within the range of 3.63 to 5.14 eV. Additionally, the results of the BSE indicate that they exhibit exceptional absorption capabilities across the near‐UV to mid‐UV light region. Furthermore, studies on transport and excitonic properties suggest that they exhibit lower effective electron masses compared to holes, with exciton binding energies spanning between 0.16 and 0.98 eV. A significant observation is the prevalent hole‐phonon coupling compared to electron‐phonon coupling in these compounds. Overall, this study provides valuable insights to guide the design of vacancy‐ordered double perovskites as promising lead‐free candidates for future optoelectronic applications.
空位有序双包晶石（VODPs）为卤化铅包晶提供了既环保又稳定的替代品，因此在科学界引起了广泛的研究兴趣。本研究采用最先进的基于第一原理的方法，特别是密度泛函理论结合密度泛函扰动理论（DFPT）和多体扰动理论（在 GW 和 BSE 框架内），重点研究了作为有前途的光电材料的（B = Ti、Se、Ru、Pd）VODPs。这些计算显示，所有这些材料都具有立方晶格结构，并且在动力学和机械学上都很稳定。有趣的是，除了显示出金属特性外，它们都表现出间接带隙。这些化合物的带隙值在 3.63 至 5.14 eV 之间。此外，BSE 的结果表明，这些化合物在近紫外光到中紫外光区域表现出卓越的吸收能力。此外，对传输和激子特性的研究表明，与空穴相比，它们表现出较低的有效电子质量，激子结合能介于 0.16 和 0.98 eV 之间。一个重要的观察结果是，在这些化合物中，空穴-声子耦合比电子-声子耦合更为普遍。总之，这项研究为指导设计空位有序双包晶石提供了宝贵的见解，这些空位有序双包晶石有望成为未来光电应用的无铅候选材料。
{"title":"Probing Optoelectronic Properties of Stable Vacancy‐Ordered Double Perovskites: Insights from Many‐Body Perturbation Theory","authors":"Surajit Adhikari, Priya Johari","doi":"10.1002/adts.202400921","DOIUrl":"https://doi.org/10.1002/adts.202400921","url":null,"abstract":"vacancy‐ordered double perovskites (VODPs) have captured substantial research interest in the scientific community as they offer environmentally friendly and stable alternatives to lead halide perovskites. In this study, the investigation is focused on (B = Ti, Se, Ru, Pd) VODPs as promising optoelectronic materials employing state‐of‐the‐art first‐principles‐based methodologies, specifically density functional theory combined with density functional perturbation theory (DFPT) and many‐body perturbation theory (within the framework of GW and BSE). These calculations reveal that all these materials possess a cubic lattice structure and are both dynamically and mechanically stable. Interestingly, they all exhibit indirect bandgaps, except displays a metallic character. The bandgap values for these compounds fall within the range of 3.63 to 5.14 eV. Additionally, the results of the BSE indicate that they exhibit exceptional absorption capabilities across the near‐UV to mid‐UV light region. Furthermore, studies on transport and excitonic properties suggest that they exhibit lower effective electron masses compared to holes, with exciton binding energies spanning between 0.16 and 0.98 eV. A significant observation is the prevalent hole‐phonon coupling compared to electron‐phonon coupling in these compounds. Overall, this study provides valuable insights to guide the design of vacancy‐ordered double perovskites as promising lead‐free candidates for future optoelectronic applications.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"70 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Metamaterials or metamaterial‐inspired structures/resonators have yielded significant advancement in the imaging capabilities of Magnetic Resonance Imaging (MRI) by boosting its performance parameter, i.e., signal‐to‐noise ratio (SNR). Metamaterials have a distinctive ability to boost and redistribute magnetic fields inside the subject undergoing scan when integrated as accessories between receive arrays and the subject. However, the translation of most reported metamaterials into a clinical accessory is still limited and challenging due to their low sensitivity, sub‐optimal performance, and bulky footprints for integration inside MRI scanners. Herein, a metamaterial‐inspired structure is developed using coupled octa‐spiral resonators to boost magnetic field localization inside the scanned region. In addition, the high‐Q resonance of the metamaterial‐inspired structure improves impedance matching and enhances the transmit/receive efficiency of MRI coils. Theoretical analysis of electromagnetic responses and full‐wave simulations show a homogeneous boost in SNR by over times throughout a human‐properties mimicking phantom using the resonator with a maximum SNR enhancement factor (EF) of . The spatial distribution of SNR EF inside the phantom is also validated by preliminary laboratory experiments. Thus, the developed coupled octa‐spirals resonator can pave the way for developing and adopting metamaterial‐inspired devices as clinical accessories for facilitating better, faster, and cost‐effective MRI scans.
{"title":"Improving Signal‐to‐Noise Ratio of 1.5T MRI Scans Using High‐Q Resonators Based on Coupled Octa‐Spirals","authors":"Jegyasu Gupta, Ratnajit Bhattacharjee, Subramani Kanagaraj, Debabrata Sikdar","doi":"10.1002/adts.202400848","DOIUrl":"https://doi.org/10.1002/adts.202400848","url":null,"abstract":"Metamaterials or metamaterial‐inspired structures/resonators have yielded significant advancement in the imaging capabilities of Magnetic Resonance Imaging (MRI) by boosting its performance parameter, i.e., signal‐to‐noise ratio (SNR). Metamaterials have a distinctive ability to boost and redistribute magnetic fields inside the subject undergoing scan when integrated as accessories between receive arrays and the subject. However, the translation of most reported metamaterials into a clinical accessory is still limited and challenging due to their low sensitivity, sub‐optimal performance, and bulky footprints for integration inside MRI scanners. Herein, a metamaterial‐inspired structure is developed using coupled octa‐spiral resonators to boost magnetic field localization inside the scanned region. In addition, the high‐Q resonance of the metamaterial‐inspired structure improves impedance matching and enhances the transmit/receive efficiency of MRI coils. Theoretical analysis of electromagnetic responses and full‐wave simulations show a homogeneous boost in SNR by over times throughout a human‐properties mimicking phantom using the resonator with a maximum SNR enhancement factor (EF) of . The spatial distribution of SNR EF inside the phantom is also validated by preliminary laboratory experiments. Thus, the developed coupled octa‐spirals resonator can pave the way for developing and adopting metamaterial‐inspired devices as clinical accessories for facilitating better, faster, and cost‐effective MRI scans.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"150 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents an innovative mathematical model denoted as the fractional SIP(H)–SI(M) model, which aims to analyze and understand the dynamics of malaria transmission and spread. This model is distinguished by incorporating memory effects through fractional differential equations, allowing for a more accurate and realistic analysis of disease spread compared to traditional models. The proposed model is applied to Algeria by estimating its parameters using recent health data (from 2000). The results revealed that the disease-free equilibrium is stable only when the basic reproduction number is less than one, indicating that controlling the spread of malaria and possibly eradicating it can be achieved by implementing appropriate preventive measures. Simulations also demonstrated a direct correlation between the rate of infection transmission and an increase in the number of infected individuals, highlighting the need for swift action when signs of an outbreak emerge. Based on these findings, a set of preventive measures is recommended, including insecticide spraying programs, widespread distribution of insecticide-treated bed nets, and implementation of effective treatment protocols for infected individuals. This study also emphasizes the importance of continuous monitoring of health data and updating model parameters to ensure the effectiveness and sustainability of preventive measures.
{"title":"Mathematical Exploration of Malaria Transmission Dynamics: Insights from Fractional Models and Numerical Simulation","authors":"Souad Bounouiga, Bilal Basti, Noureddine Benhamidouche","doi":"10.1002/adts.202400630","DOIUrl":"https://doi.org/10.1002/adts.202400630","url":null,"abstract":"This study presents an innovative mathematical model denoted as the fractional SIP(H)–SI(M) model, which aims to analyze and understand the dynamics of malaria transmission and spread. This model is distinguished by incorporating memory effects through fractional differential equations, allowing for a more accurate and realistic analysis of disease spread compared to traditional models. The proposed model is applied to Algeria by estimating its parameters using recent health data (from 2000). The results revealed that the disease-free equilibrium is stable only when the basic reproduction number is less than one, indicating that controlling the spread of malaria and possibly eradicating it can be achieved by implementing appropriate preventive measures. Simulations also demonstrated a direct correlation between the rate of infection transmission and an increase in the number of infected individuals, highlighting the need for swift action when signs of an outbreak emerge. Based on these findings, a set of preventive measures is recommended, including insecticide spraying programs, widespread distribution of insecticide-treated bed nets, and implementation of effective treatment protocols for infected individuals. This study also emphasizes the importance of continuous monitoring of health data and updating model parameters to ensure the effectiveness and sustainability of preventive measures.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"1 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142589063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a novel filter based on the sliding mode method for filtering noise and extracting reliable signals from noisy signals by enhancing Levant's sliding mode filter. Specifically, the proposed sliding mode filter takes advantage of the generalized signum function to adjust the gain in different regions of phase space, thereby decreasing the overshoot during the response phase. Additionally, the discrete-time implementation of the proposed sliding mode filter is achieved by utilizing the implicit-Euler algorithm, which effectively eliminates chattering in the output. The effectiveness of the presented sliding mode filter is substantiated via numerical simulation cases.
{"title":"A New Filter Based on Sliding Mode Method with Discrete-Time Implementation for Noise Attenuation and Differentiation","authors":"Zuoping Zhao, Dongyang Li","doi":"10.1002/adts.202400835","DOIUrl":"https://doi.org/10.1002/adts.202400835","url":null,"abstract":"This paper presents a novel filter based on the sliding mode method for filtering noise and extracting reliable signals from noisy signals by enhancing Levant's sliding mode filter. Specifically, the proposed sliding mode filter takes advantage of the generalized signum function to adjust the gain in different regions of phase space, thereby decreasing the overshoot during the response phase. Additionally, the discrete-time implementation of the proposed sliding mode filter is achieved by utilizing the implicit-Euler algorithm, which effectively eliminates chattering in the output. The effectiveness of the presented sliding mode filter is substantiated via numerical simulation cases.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"19 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142589067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Prakash Raj, A. Venkatesh, K. Arun Kumar, M. Manivel
This study presents a comprehensive mathematical model to analyze the dynamics of co-infection between dengue and malaria using delay differential equations. The model investigates the transmission dynamics of both diseases, focusing on the stability of equilibrium points and the basic reproductive ratio, which measures the number of secondary infections caused by a single infected individual. A time-delay component is incorporated to account for the incubation periods, enhancing the model's realism. The study performs a detailed sensitivity analysis and global stability assessments, providing insights into the control and management of diseases. Numerical simulations are conducted to illustrate the effect of various transmission parameters on disease spread. This research highlights the importance of mathematical modeling in understanding co-infection dynamics and provides critical insights for public health interventions, particularly in regions where both diseases are endemic. The results emphasize the role of controlling transmission rates and the use of vector management strategies in mitigating disease outbreaks.
{"title":"Mathematical Modeling of the Co-Infection Dynamics of Dengue and Malaria Using Delay Differential Equations","authors":"M. Prakash Raj, A. Venkatesh, K. Arun Kumar, M. Manivel","doi":"10.1002/adts.202400609","DOIUrl":"https://doi.org/10.1002/adts.202400609","url":null,"abstract":"This study presents a comprehensive mathematical model to analyze the dynamics of co-infection between dengue and malaria using delay differential equations. The model investigates the transmission dynamics of both diseases, focusing on the stability of equilibrium points and the basic reproductive ratio, which measures the number of secondary infections caused by a single infected individual. A time-delay component is incorporated to account for the incubation periods, enhancing the model's realism. The study performs a detailed sensitivity analysis and global stability assessments, providing insights into the control and management of diseases. Numerical simulations are conducted to illustrate the effect of various transmission parameters on disease spread. This research highlights the importance of mathematical modeling in understanding co-infection dynamics and provides critical insights for public health interventions, particularly in regions where both diseases are endemic. The results emphasize the role of controlling transmission rates and the use of vector management strategies in mitigating disease outbreaks.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"10 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142589066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zafari Umar, Oleg Khyzhun, Mekhrdod S. Kurboniyon, Tomoyuki Yamamoto, Mikhail G. Brik, Mega Novita, Justyna Barzowska, Michal Piasecki
To understand excellent emission and sensitivity for hydrostatic pressure luminescent ions host material, the first principles calculations carried out within density functional theory (DFT) framework are performed to clarify the electronic structure of neat and doped with Ni2+ ions KMgF3 single crystals. The results of band structure calculations show that F2p states are the principal contributors to the KMgF3 valence band, mainly in its upper and central parts, while in the energy band gap of the KMgF3:Ni2+ phosphor, new electronic states associated with the Ni2+ 3d-orbitals are formed. Furthermore, the zero phonon line (ZPL) spin-forbidden transition emission energies, (3A2⇄1E) ZPL, (3A2⇄3T2) ZPL, strength of the octahedral crystal field, 10Dq (3A2→3T2)ZPL, are calculated for the KMgF3:Ni2+ phosphor. Any changes of the Em(3A2⇄1E)ZPL transition energy of the KMgF3:Ni2+ phosphor with pressure increasing from 0 to 20 GPa are not detected, while the crystal-field strength increases linearly with increasing pressure. Present results bring a foresight tool for predicting physicochemical properties of undoped and doped wide-gap fluorides; KMgF3:Ni2+, without any toxic/harmful or expensive rare-earth can be effectively used as an optical manometer in 0–20 GPa, which covers the almost whole pressure range available at present in Diamond anvil cell experiments.
{"title":"The Effect of Hydrostatic Pressure on Structure, Crystal-Field Strength, and Emission Properties of Neat and Ni2+-Activated KMgF3","authors":"Zafari Umar, Oleg Khyzhun, Mekhrdod S. Kurboniyon, Tomoyuki Yamamoto, Mikhail G. Brik, Mega Novita, Justyna Barzowska, Michal Piasecki","doi":"10.1002/adts.202400734","DOIUrl":"https://doi.org/10.1002/adts.202400734","url":null,"abstract":"To understand excellent emission and sensitivity for hydrostatic pressure luminescent ions host material, the first principles calculations carried out within density functional theory (DFT) framework are performed to clarify the electronic structure of neat and doped with Ni<sup>2+</sup> ions KMgF<sub>3</sub> single crystals. The results of band structure calculations show that F2<i>p</i> states are the principal contributors to the KMgF<sub>3</sub> valence band, mainly in its upper and central parts, while in the energy band gap of the KMgF<sub>3</sub>:Ni<sup>2+</sup> phosphor, new electronic states associated with the Ni<sup>2+</sup> 3<i>d</i>-orbitals are formed. Furthermore, the zero phonon line (ZPL) spin-forbidden transition emission energies, (<sup>3</sup>A<sub>2</sub>⇄<sup>1</sup>E) <sub>ZPL</sub>, (<sup>3</sup>A<sub>2</sub>⇄<sup>3</sup>T<sub>2</sub>) <sub>ZPL</sub>, strength of the octahedral crystal field, 10<i>Dq</i> (<sup>3</sup>A<sub>2</sub>→<sup>3</sup>T<sub>2</sub>)<sub>ZPL</sub>, are calculated for the KMgF<sub>3</sub>:Ni<sup>2+</sup> phosphor. Any changes of the <i>E<sub>m</sub></i>(<sup>3</sup>A<sub>2</sub>⇄<sup>1</sup>E)<sub>ZPL</sub> transition energy of the KMgF<sub>3</sub>:Ni<sup>2+</sup> phosphor with pressure increasing from 0 to 20 GPa are not detected, while the crystal-field strength increases linearly with increasing pressure. Present results bring a foresight tool for predicting physicochemical properties of undoped and doped wide-gap fluorides; KMgF<sub>3</sub>:Ni<sup>2+</sup>, without any toxic/harmful or expensive rare-earth can be effectively used as an optical manometer in 0–20 GPa, which covers the almost whole pressure range available at present in Diamond anvil cell experiments.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"5 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142589064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of machine learning (ML) with perovskite solar cells (PSCs) signifies a groundbreaking era in photovoltaic (PV) technology. The traditional iterative approaches in PSC research are often time‐consuming and resource‐intensive. In contrast, ML leverages available data and sophisticated algorithms to quickly identify properties and optimize parameters for novel materials and devices. This review explores how ML‐driven approaches are improving various facets of PSCs research, including the rapid screening of novel compositions, enhancing stability, refining device architectures, and deepening the understanding of underlying physics. The paper is structured to gradually familiarize readers with essential terminologies and concepts, ensuring a solid foundation before delving into more intricate topics. A concise workflow and various introductory toolkits for ML are also briefly discussed. Through a detailed analysis of compelling case studies, a basic research framework within ML‐PSC‐integrated research is provided. This comprehensive review can serve as a valuable reference for researchers aiming to understand and leverage ML‐driven approaches in PSCs research, advancing the path for more efficient and sustainable PV technologies.
机器学习（ML）与过氧化物太阳能电池（PSC）的结合标志着光伏（PV）技术进入了一个开创性的时代。在 PSC 研究中，传统的迭代方法往往耗费大量时间和资源。相比之下，人工智能利用现有数据和复杂算法，可快速确定新型材料和设备的特性并优化参数。本综述探讨了以 ML 为驱动的方法如何改善 PSCs 研究的各个方面，包括快速筛选新型成分、提高稳定性、完善器件架构以及加深对基础物理学的理解。本文在结构上让读者逐步熟悉基本术语和概念，确保在深入探讨更复杂的主题之前打下坚实的基础。此外，还简要讨论了简明的工作流程和各种 ML 入门工具包。通过对引人注目的案例研究的详细分析，提供了 ML-PSC 整合研究的基本研究框架。本综述可作为研究人员的宝贵参考资料，帮助他们了解和利用 ML 驱动的 PSCs 研究方法，从而推动更高效、更可持续的光伏技术的发展。
{"title":"Machine Learning Approaches in Advancing Perovskite Solar Cells Research","authors":"Subham Subba, Pratika Rai, Suman Chatterjee","doi":"10.1002/adts.202400652","DOIUrl":"https://doi.org/10.1002/adts.202400652","url":null,"abstract":"The integration of machine learning (ML) with perovskite solar cells (PSCs) signifies a groundbreaking era in photovoltaic (PV) technology. The traditional iterative approaches in PSC research are often time‐consuming and resource‐intensive. In contrast, ML leverages available data and sophisticated algorithms to quickly identify properties and optimize parameters for novel materials and devices. This review explores how ML‐driven approaches are improving various facets of PSCs research, including the rapid screening of novel compositions, enhancing stability, refining device architectures, and deepening the understanding of underlying physics. The paper is structured to gradually familiarize readers with essential terminologies and concepts, ensuring a solid foundation before delving into more intricate topics. A concise workflow and various introductory toolkits for ML are also briefly discussed. Through a detailed analysis of compelling case studies, a basic research framework within ML‐PSC‐integrated research is provided. This comprehensive review can serve as a valuable reference for researchers aiming to understand and leverage ML‐driven approaches in PSCs research, advancing the path for more efficient and sustainable PV technologies.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"63 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial Intelligence (AI) is pivotal in advancing science, including nanomaterial studies. This review explores AI‐based image processing in nanoscience, focusing on algorithms to enhance characterization results from instruments like scanning electron microscopy, transmission electron microscopy, X‐ray diffraction, atomic force microscopy etc. It addresses the significance of AI in nanoscience, challenges in advancing AI‐based image processing for nano material characterization, and AI's role in structural analysis, property prediction, deriving structure‐property relations, dataset augmentation, and improving model robustness. Key AI techniques such as Graph Neural Networks, adversarial training, transfer learning, generative models, attention mechanisms, and federated learning are highlighted for their contributions to nano science studies. The review concludes by outlining persisting challenges and thrust areas for future research, aiming to propel nanoscience with AI. This comprehensive analysis underscores the importance of AI‐powered image processing in nanomaterial characterization, offering valuable insights for researchers.
{"title":"Intelligent Nanomaterial Image Characterizations – A Comprehensive Review on AI Techniques that Power the Present and Drive the Future of Nanoscience","authors":"Umapathi Krishnamoorthy, Sukanya Balasubramani","doi":"10.1002/adts.202400479","DOIUrl":"https://doi.org/10.1002/adts.202400479","url":null,"abstract":"Artificial Intelligence (AI) is pivotal in advancing science, including nanomaterial studies. This review explores AI‐based image processing in nanoscience, focusing on algorithms to enhance characterization results from instruments like scanning electron microscopy, transmission electron microscopy, X‐ray diffraction, atomic force microscopy etc. It addresses the significance of AI in nanoscience, challenges in advancing AI‐based image processing for nano material characterization, and AI's role in structural analysis, property prediction, deriving structure‐property relations, dataset augmentation, and improving model robustness. Key AI techniques such as Graph Neural Networks, adversarial training, transfer learning, generative models, attention mechanisms, and federated learning are highlighted for their contributions to nano science studies. The review concludes by outlining persisting challenges and thrust areas for future research, aiming to propel nanoscience with AI. This comprehensive analysis underscores the importance of AI‐powered image processing in nanomaterial characterization, offering valuable insights for researchers.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"60 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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