Advanced Theory and Simulations最新文献

{"title":"Machine Learning Guided Discovery of Non-Linear Optical Materials (Adv. Theory Simul. 2/2025)","authors":"Sownyak Mondal,&nbsp;Raheel Hammad","doi":"10.1002/adts.202570003","DOIUrl":"https://doi.org/10.1002/adts.202570003","url":null,"abstract":"<p>In article 2400463, Sownyak Mondal and Raheel Hammad use predictive modeling to identify new nonlinear optical (NLO) materials, using refractive index as a proxy. It focuses on non-centrosymmetric materials with optimal hardness and bandgap properties, which are validated through density functional theory, successfully confirming several established NLO materials and enhancing solid-state laser performance.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"8 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adts.202570003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Issue Information (Adv. Theory Simul. 2/2025)","authors":"","doi":"10.1002/adts.202570004","DOIUrl":"https://doi.org/10.1002/adts.202570004","url":null,"abstract":"","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"8 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adts.202570004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Variability-Aware Behavioral Model of Monolayer MoS2 RRAM for Tunable Stochastic Sources","authors":"Lavanya Peddaboina, Kartik Agrawal, Piyush Kumar, Girija Hegde, Oves Badami, Shubhadeep Bhattacharjee","doi":"10.1002/adts.202401235","DOIUrl":"https://doi.org/10.1002/adts.202401235","url":null,"abstract":"Stochastic switching in resistive random-access memories (RRAMs), while presenting challenges in digital memory applications, can be leveraged beyond von Neumann's stochastic computing and hardware security applications. In this regard, it is crucial to identify and model RRAMs where microscopic stochastic events can enable sizeable and tunable variability in macroscopic device characteristics. In this regard, chalcogen vacancy-mediated multifilamentary switching consisting of a multitude of hotspots in monolayer transition metal dichalcogenide (TMDCs) RRAMs can be promising candidates for high-quality, tunable stochastic sources. In this work, an efficient physics-based model is developed to capture the behavior of stochastic switching in monolayer MoS₂ RRAMs. The microscopic origin of stochasticity, arising from clusters of sulfur vacancies transforming into metallic hotspots, is modeled using the kinetic Monte Carlo method. The rate equations designed to capture the physics of abrupt SET and gradual RESET processes provide an excellent fit to experimental data, allowing to extract key material parameters. The calibrated macroscopic model is then employed to explore multiple non-volatile resistance states in the gradual RESET process, area scalability trends and cycle-to-cycle C2C variability over 100k cycles. Furthermore, the statistical distribution of HRS and LRS variability is modeled and large tunability of the distribution is demonstrated using stop voltage in RESET. Finally, it is demonstrated that these devices are excellent candidates as bit stream generators for stochastic computing applications with accuracy values comparable to an ideal source. It is envisioned that the work will induce significant interest in the deployment of 2D materials-based RRAMs for high-quality tunable stochastic sources.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"1 1 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417486","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}

{"title":"Numerical Approximation of the Fractional Model of Atmospheric Dynamics of CO2 Using the Gegenbauer Wavelet Collocation Method","authors":"Manohara G, Kumbinarasaiah S","doi":"10.1002/adts.202401463","DOIUrl":"https://doi.org/10.1002/adts.202401463","url":null,"abstract":"This work provides an understanding of the fractional order nonlinear mathematical model that describes the dynamic variation of carbon dioxide (&lt;span data-altimg=\"/cms/asset/5f7b4724-f1f4-48ef-9608-ae2b98dfdab4/adts202401463-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"5\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\" location=\"graphic/adts202401463-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow data-semantic-children=\"6,4\" data-semantic-content=\"4\" data-semantic- data-semantic-role=\"endpunct\" data-semantic-speech=\"upper C upper O 2 right parenthesis\" data-semantic-type=\"punctuated\"&gt;&lt;mjx-mrow data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"0,3\" data-semantic-content=\"5\" data-semantic- data-semantic-parent=\"7\" data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"6\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-mo data-semantic-added=\"true\" data-semantic- data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"6\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-msub data-semantic-children=\"1,2\" data-semantic- data-semantic-parent=\"6\" data-semantic-role=\"latinletter\" data-semantic-type=\"subscript\"&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"3\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-script style=\"vertical-align: -0.15em;\"&gt;&lt;mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"3\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mn&gt;&lt;/mjx-script&gt;&lt;/mjx-msub&gt;&lt;/mjx-mrow&gt;&lt;mjx-mrow style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"punctuated\" data-semantic-parent=\"7\" data-semantic-role=\"closefence\" data-semantic-type=\"punctuation\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;/mjx-mrow&gt;&lt;/mjx-mrow&gt;&lt;/mjx-semantics&gt;&lt;/mjx-math&gt;&lt;mjx-assistive-mml display=\"inline\" unselectable=\"on\"&gt;&lt;math altimg=\"urn:x-wiley:25130390:media:adts202401463:adts202401463-math-0001\" display=\"inline\" location=\"graphic/adts202401463-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow data-semantic-=\"\" data-semantic-children=\"6,4\" data-semantic-content=\"4\" data-semantic-role=\"endpunct\" data-semantic-speech=\"upper C upper O 2 right parenthesis\" data-semantic-type=\"punctuated\"&gt;&lt;mrow data-semantic-=\"\" data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"0,3\" data-semantic-content=\"5\" data-semantic-parent=\"7\" data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"&gt;&lt;mi data-semantic-=\"\" data-semantic-ann","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"1 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417641","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}

{"title":"Numerical Analysis and Artificial Neural Networks for Solving Nonlinear Tuberculosis Model in SEITR Framework","authors":"N. Jeeva, K. M. Dharmalingam","doi":"10.1002/adts.202401287","DOIUrl":"https://doi.org/10.1002/adts.202401287","url":null,"abstract":"This study investigates an epidemiological model of tuberculosis dynamics by classifying the total population into five distinct compartments: susceptible, exposed, infected, treated, and recovered. To solve the system of nonlinear differential equations and obtain approximate solutions for the &lt;span data-altimg=\"/cms/asset/b2e4348d-103a-4955-b89b-15f6540438a1/adts202401287-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"1\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\" location=\"graphic/adts202401287-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"0,1,2,3,4\" data-semantic-content=\"5,6,7,8\" data-semantic- data-semantic-role=\"implicit\" data-semantic-speech=\"upper S upper E upper I upper T upper R\" data-semantic-type=\"infixop\"&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"9\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-mo data-semantic-added=\"true\" data-semantic- data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"9\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"9\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-mo data-semantic-added=\"true\" data-semantic- data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"9\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"9\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-mo data-semantic-added=\"true\" data-semantic- data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"9\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"9\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-mo data-semantic-added=\"true\" data-semantic- data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"9\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"9\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;/mjx-mrow&gt;&lt;/mjx-semantics&gt;","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"35 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417484","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}

{"title":"Rolling Bearing Fault Diagnosis Based on 2D CNN and Hybrid Kernel Fuzzy SVM","authors":"Qingbao Zhang, Zhe Ju","doi":"10.1002/adts.202400793","DOIUrl":"https://doi.org/10.1002/adts.202400793","url":null,"abstract":"Addressing the issues of poor anti-noise performance and insufficient mining of fault information in vibration signals in traditional fault diagnosis methods, a network structure algorithm (2DCNN-HKFSVM) based on the 2D convolutional neural network (CNN) and the hybrid kernel fuzzy support vector machine (HKFSVM) is proposed. First, the original bearing vibration signals are converted into 2D grayscale images; then, these grayscale images are used as inputs to the 2D convolutional neural network for feature extraction and dimensionality reduction; finally, the obtained feature vectors are passed to the hybrid kernel fuzzy support vector machine for fault detection. Compared with the support vector machine (SVM), the fuzzy support vector machine (FSVM) assigns different weights to different bearing fault samples through the fuzzy membership function, thereby reducing the impact of noise on the classification results. Furthermore, the hybrid kernel function combined according to Mercer's theorem enables the FSVM to take both global and local fitting into account, further improving the classification performance of the FSVM. Compared with some existing fault diagnosis models that combine CNN with machine learning algorithms such as SVM and random forests (RF), 2DCNN-HKFSVM exhibits better generalization ability and anti-noise performance.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"85 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417485","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}

{"title":"First Principles Study of TiS2/Graphene Heterojunction as Cathode Material for Sodium–Sulfur Batteries","authors":"Pan Long, Yunxin Zheng, Jianrong Xiao, Zhiyong Wang","doi":"10.1002/adts.202401100","DOIUrl":"https://doi.org/10.1002/adts.202401100","url":null,"abstract":"Rechargeable batteries are an indispensable part of the development of environmental sustainability, high capacity and excellent conductivity are essential for optimal energy storage. Sodium–sulfur (Na─S) batteries are recognized as promising options for stationary energy storage solutions, which can be attributed to their high theoretical energy density and cost advantages. Additionally, 2D XS₂@graphene heterojunction is considered an excellent host material for batteries. Using the first-principles calculations, a novel 2D TiS₂/graphene heterojunction have been theoretically investigated as an anchoring material for sodium–sulfur batteries to mitigate the shuttle effect. First, the composite structure is created by depositing a TiS₂ layer onto a single layer of graphene retains its inherent metallic character. Furthermore, the calculated results show that the TiS₂/graphene heterojunction exhibits adsorption energy of 1.3 eV for long-chain sodium polysulfides while maintaining its metallic properties, effectively anchoring polysulfides and inhibiting the shuttle effect. Moreover, a low diffusion barrier (0.31 eV) for Na ion diffusion shows that this heterojunction can enhance electrochemical processes. TiS₂/graphene heterojunction may be the candidate for ideal anchoring materials in sodium–sulfur batteries.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"102 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401962","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}

{"title":"Electro-Thermal Response of Thin Film Heaters Based on Embedded Periodic Metallic Mesh","authors":"Dimitrios Charaklias, Dayuan Qiang, Robert Dorey, Iman Mohagheghian","doi":"10.1002/adts.202401225","DOIUrl":"https://doi.org/10.1002/adts.202401225","url":null,"abstract":"Here, the electro-thermal response of thin film heaters is investigated for a wide range of periodic metallic mesh topologies. The aim of this study is to systematically investigate the effect of topology, in particular node connectivity, on the response of these networks. The study first defines the design space by deriving analytical expressions for geometrical dependencies and permissible parameters for each geometry Closed-formed analytical expressions are developed subsequently to calculate the network resistance considering junction area compensation. Finally, transient coupled electro-thermal finite element modelling (FEM) is performed across multiple topologies using automated geometry generation, meshing, analysis, and post-processing. The analytical expressions, incorporating junction area compensation, can quickly and accurately predict the resistance of the networks. Network topology significantly impacts the resistance, demonstrating variations of up to three times over the range investigated, when compared on the same fill factor basis. Higher resistance results in a faster response time when the current density is fixed. For the same power input, however, the response time is much more similar, though spatial temperature variation remain significant. These findings provide valuable insights for designing faster, more energy-efficient thin film heaters applicable to electronic displays, wearable technologies, energy systems, optics, photonics, and multifunctional devices.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"16 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401540","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}

{"title":"First-Principles Study on Introducing Fluorine Doping and Sulfur Vacancy into MoS2 for Advanced Lithium Storage","authors":"Zhiling Xu, Yanbing Liao, Kaihui Lin, Jiayi Guan, Yuda Lin, Liting Qiu","doi":"10.1002/adts.202401101","DOIUrl":"https://doi.org/10.1002/adts.202401101","url":null,"abstract":"MoS₂, a potential anode material for lithium ion batteries (LIBs), boasts high specific capacity, a unique layered structure, and large interlayer spacing, but struggles with poor conductivity and volume effect. Starting from improving the intrinsic electronic conductivity of MoS₂, this study innovatively introduces F-doping and sulfur vacancies into MoS₂ crystals to form F-MoS_2-x crystals, and investigates its structural features and LIBs applications through first-principle calculations. The rationality and stability of F-MoS_2−x are calculated by phonon spectra. The density of states calculations reveals that F-doping and sulfur vacancies effectively alter MoS₂'s electronic state, reducing its intrinsic band-gap and confirming F-MoS_2-x's superior electronic conductivity theoretically. They also significantly decrease lithium-ion diffusion resistance on F-MoS_2-x's surface, potentially enabling high-rate performance. Besides, the calculation of adsorption energy and differential charge density reveals strong adsorption between F-MoS_2-x and lithium ions, which favors long-term cycle stability. Notably, with each F-MoS_2-x molecule storing up to 4.5 Li, corresponding to a theoretical capacity of 769 mAh g⁻¹, higher than MoS₂'s 670 mAh g⁻¹. This study provides a meaningful reference value for the modification of MoS₂.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"63 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401539","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}

{"title":"Density of States and Binding Energy Informatics for Exploring Early Disease Detection in MOF-Metal Oxide Chemiresistive Sensors","authors":"Maryam Nurhuda, Ken-ichi Otake, Susumu Kitagawa, Daniel M. Packwood","doi":"10.1002/adts.202401404","DOIUrl":"https://doi.org/10.1002/adts.202401404","url":null,"abstract":"Human breath contains over 3000 volatile organic compounds, abnormal concentrations of which can indicate the presence of certain diseases. Recently, metal–organic framework (MOF)-metal oxide composite materials have been explored for chemiresistive sensor applications, however their ability to detect breath compounds associated with specific diseases remains unknown. In this work, a new high-throughput computational protocol for evaluating the sensing ability of MOF-metal oxide toward small organic compounds is presented. This protocol uses a cluster-based method for accelerated structure relaxation, and a combination of binding energies and density-of-states analysis to evaluate sensing ability, the latter measured using Wasserstein distances. This protocol is applied to the case of the MOF-metal oxide composite material NM125-TiO₂ and is shown to be consistent with previously reported experimental results for this system. The sensing ability of NM125-TiO₂ for over 100 human-breath compounds spanning 13 different diseases is examined. Statistical inference is then used to identify diseases which subsequent experimental efforts should focus on. Overall, this work provides new tools for computational sensor research, while also illustrating how computational materials science can be integrated into the field of preventative medicine.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"28 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393284","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}