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Carbon-doped percentage effect on the mechanical properties of nanoporous silicon sample using molecular dynamics simulation

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Case Studies in Chemical and Environmental engineering 2025年 11卷

作者： Sawaran Singh, Narinderjit Singh Ali, Ali B.M. Ameen, Hawzhen Fateh M. Al-Zahiwat, Murtadha M. Salahshour, Soheil Emami, Nafiseh Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Petroleum Technology Erbil Technology College Erbil Polytechnic University Kurdistan Region Erbil Iraq Department of Petroleum Engineering Knowledge University Erbil Iraq Department of Chemical engineering College of Engineering University of Misan Amarah Iraq Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Türkiye Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Türkiye Research Center of Applied Mathematics Khazar University Baku Türkiye Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Porous materials have attracted considerable attention from researchers due to its many uses in molecular separation, heterogeneous catalysis, absorption technologies, and electronic improvements. These solid materials, often defined by their structural voids, are essential in several sectors. This research investigated the impact of carbon doping on the mechanical characteristics of nanoporous silicon matrices. The use of high-purity silicon doping is very beneficial in the semiconductor industry and is crucial for high-power devices and automotive applications. This study simulates a nanoporous silicon sample by molecular dynamics methods, adding carbon doping at different concentrations. The findings demonstrate that when the carbon doping concentration escalated from 1 % to 30 %, the mechanical resistance of the system decreased correspondingly. The ultimate tensile strength fell from 10.26 to 9.02 GPa. Furthermore, Young's modulus rose from 83.47 to 98.37 GPa. The decline in mechanical stability was associated with a drop in the model's total weight, which had considerable ramifications for industrial applications. Thus, incorporating C-doped nanoporous silicon into real applications not only lowered the weight of target materials but also improved their use. © 2025 The Author(s)

关键词： Carbon doping Mechanical properties Molecular dynamics simulation Nanoporous Porous materials

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2024年 995卷 v-vii页

作者： Pant, Millie Nagar, Atulya Deep, Kusum Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand Roorkee India School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool United Kingdom Department of Mathematics Indian Institute of Technology Roorkee Uttarakhand Roorkee India

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The hardware is the software

arXiv

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arXiv 2023年

作者： Laydevant, Jérémie Wright, Logan G. Wang, Tianyu McMahon, Peter L. School of Applied and Engineering Physics Cornell University IthacaNY14853 United States USRA Research Institute for Advanced Computer Science Mountain ViewCA94035 United States NTT Physics and Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States Department of Applied Physics Yale University New HavenCT06511 United States Kavli Institute Cornell for Nanoscale Science Cornell University IthacaNY14853 United States

Human brains and bodies are not hardware running software: the hardware is the software. We reason that because the microscopic physics of artificial-intelligence hardware and of human biological "hardware" is distinct, neuromorphic engineers need to be cautious (and yet also creative) in how we take inspiration from biological intelligence. We should focus primarily on principles and design ideas that respect—and embrace—the underlying hardware physics of non-biological intelligent systems, rather than abstracting it away. We see a major role for neuroscience in neuromorphic computing as identifying the physics-agnostic principles of biological intelligence—that is the principles of biological intelligence that can be gainfully adapted and applied to any physical hardware. © 2023, CC BY.

关键词： Intelligent systems

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Adsorption of CO₂, CH₄ and H₂ onto zeolite 13 X: Kinetic and equilibrium studies

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Heliyon 2024年第23期10卷 e40672页

作者： Jedli, Hedi Bouzgarrou, Souhail Mohammed Hassani, Rym Sabi, Ehab Slimi, Khalifa University of Monastir National Engineering School of Monastir Laboratory of Studies of Thermal Systems and Energy LR99ES31 Monastir 5019 Tunisia Civil and Architectural Engineering Department College of Engineering and Computer Sciences Jazan University Jazan Saudi Arabia Higher Institute of Applied Sciences and Technologie of Sousse Sousse University Tunisia Chemistry Department Center for Environmental and Nature Research Jazan University P.O Box 114 Jazan Saudi Arabia

Adsorption equilibrium and kinetics of CO2, H2 and CH4 on zeolite 13 X were carried out using the volumetric method. The zeolite 13 X was characterized via XRD, SEM and N2 adsorption-desorption. Adsorption isotherms o... 详细信息

Adsorption equilibrium and kinetics of CO₂, H₂ and CH₄ on zeolite 13 X were carried out using the volumetric method. The zeolite 13 X was characterized via XRD, SEM and N₂ adsorption-desorption. Adsorption isotherms of various gas were investigated over a full range of temperatures at 298, 308 and 313 K. Langmuir's equilibrium model and the heat of adsorption was used to correlate the adsorption isotherms over a wide range of pressure. A classical micropore diffusion approach was applied to investigate the kinetic adsorption. At 298 K, the selectivity of CO₂/H₂ and CO₂/CH₄ of the zeolite 13 X was 16.52 and 62.61, respectively. However, zeolite 13 X is one of the most suitable adsorbents for CO₂ and CH₄ storage, since it has a very high adsorption capacity for both gases. The influence of gas pressure on diffusivity for various absorber-adsorbent systems has been examined. The adsorption experiments with gas mixtures such as H₂, CO₂ and CH₄ on 13 X zeolite show that this adsorbent has potential application in the gas separation and purification process. © 2024 The Authors

关键词： Heat of adsorption Kinetic Langmuir Zeolite 13 X

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A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment duration

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Healthcare Analytics 2024年 5卷 100335-100335页

作者： Abdulrashid, Ismail Delen, Dursun Usman, Basiru Uzochukwu, Mark Izuchukwu Ahmed, Idris School of Finance and Operations Management Collins College of Business The University of Tulsa Tulsa 74104 OK United States Department of Management Science and Information Systems Spears School of Business Oklahoma State University Stillwater 74078 OK United States Department of Industrial Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Department of Business Management Poole College of Management North Carolina State University Raleigh 27695 NC United States Department of Mathematics and Computer Science Alabama State University Montgomery 36104 AL United States Department of Mathematics Faculty of Natural and Applied Sciences Sule Lamido University Jigawa Kafin Hausa 700271 Nigeria

Traditional randomized clinical trials are regarded as the gold standard for assessing the efficacy of chemotherapy. However, this procedure has drawbacks such as high cost, time consumption, and limited patient exploration of treatment regimens. We develop a multi-objective optimization-based framework to address these limitations and determine the best chemotherapy dosing and treatment duration. The proposed framework uses patient-specific biological parameters to create a mathematical model of cell population dynamics in the patient's body. The framework employs evolutionary heuristic search methods (simulated annealing and genetic algorithms) and a prescriptive analytics approach to optimize therapy sessions that transition from treatment to relaxation. We carefully adjust the chemotherapy dose during treatment to reduce tumor cells while preserving host cells (such as effector-immune cells). We strategically time the relaxation sessions to aid recovery, considering the ability of tumors and healthy cells to regenerate. We use a combined optimization method to determine the length of the session and the amount of drug to be administered. We compare quadratic and linear optimal control solvers for drug administration while genetic algorithms and simulated annealing are used to optimize session length. This approach is especially important in limited healthcare resources, ensuring efficient allocation while accurately identifying high-risk patients to optimize resource allocation and utilization. © 2024 The Authors

关键词： Chemotherapy Drug scheduling Healthcare analytics Heuristics Prescriptive analytics Simulation

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Short-term Prediction of Lightning in Southern Africa using Autoregressive Machine Learning Techniques

Short-term Prediction of Lightning in Southern Africa using ...

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IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE International

作者： Yaseen Essa Hugh G.P. Hunt Ritesh Ajoodha School of Computer Science and Applied Mathematics University of the Witwaterstand Johannesburg South Africa Johannesburg Lightning Research Laboratory School of Electrical and Information Engineering University of the Witwaterstand Johannesburg South Africa

ISBN: (纸本)9781665440677;9781665411608

Lightning is responsible for both human and economic loss but its prediction remains challenging. We seek to find a lightning prediction model in South Africa that uses historical lightning-flash data only. This type of prediction model is cost-effective, easy to interpret and may be used for realtime forecasting. We evaluated and compared three popular time-series machine learning techniques on their ability to predict the number of Cloud-to-ground lightning flashes in South Africa for three-hours ahead. These models are the Auto Regressive (AR), Auto Regressive Integrated Moving Average (ARIMA) and the Long-Short-Term-Memory Recurrent Neural Network (LSTM) models. We used historical lightning data from the South African Lightning Detection Network during 2018. Our prediction model parameters were AR(lag=8), ARIMA (AR lag=8, Integrate=0, MA lag=2) and LSTM (2x50 layers, activation=ReLU, optimizer=adam) and models were minimized for Root Mean Square Error but evaluated based on Mean Absolute Percentage Error (MAPE). We used a 70%/30% Train-test split. The AR and ARIMA models performed comparably with a MAPE of 15312 and 15080 respectively. The LSTM Model outperformed considerably with a MAPE of 3705. Although the LSTM model outperformed, predictions errors in absolute terms were still high. This paper highlights the usefulness of nonparametric predictions models for lightning prediction.

关键词： Economics Recurrent neural networks Mechatronics Error analysis Lightning Lighting Machine learning

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核心坍缩超新星中辐射冲击波的实验室模拟及首次量化校验（英文）

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Science Bulletin 2024年第20期 3188-3191页

作者：张璐郑建华杨正华宋天明张帅刘彤韦云锋况龙钰景龙飞林雉伟李丽灵黎航郑金华杨品张玉雪张志宇赵阳何智兵李平杨冬杨家敏赵宗清丁永坤 Research Center of Laser Fusion China Academy of Engineering Physics Department of Astronomy Xiamen University Institute of Applied Physics and Computational Mathematics

A radiative shock(RS) is one in which the density and temperature structures are affected by radiation from the shock-heated matter. RS plays a special role in astrophysics as it nontrivially combines both hydrodynamics and radiation physics. In most astrophysical shocks, the temperature and density conditions lead to strong emission, with radiation thus playing a major role therein. Various RS structures can be implied for numerous astrophysical objects, such as supernova explosions, stellar interiors [1],stellar winds, star formation, black hole accretion disks [2], accreting neutron stars [3], and gamma-ray bursts [4]. In particular, RS exists in the blast waves of core-collapse supernovae(CCSNe),where the radiation pressure in matter is larger than the thermal one. On the basis of multi-messenger supernova observations, their explosion model and particle acceleration mechanisms have been built, and the characteristics of progenitor stars can be further constrained. However, entire supernova explosions are very difficult to model numerically because of the different spatial and temporal length scales involved and the controversial neutrino-driven mechanism [5].

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Holonomic and non-holonomic geometric models associated to the Gibbs-Helmholtz equation

arXiv

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arXiv 2023年

作者： Pripoae, Cristina-Liliana Hirica, Iulia-Elena Pripoae, Gabriel-Teodor Preda, Vasile Department of Applied Mathematics The Bucharest University of Economic Studies Piata Romana 6 BucharestRO-010374 Romania Faculty of Mathematics and Computer Science University of Bucharest Academiei 14 BucharestRO-010014 Romania "Gheorghe Mihoc-Caius Iacob" Institute of Mathematical Statistics and Applied Mathematics of Romanian Academy 2. Calea 13 Septembrie nr.13 sect. 5 BucharestRO-050711 Romania "Costin C. Kiritescu" National Institute of Economic Research of Romanian Academy 3. Calea 13 Septembrie nr.13 sect. 5 BucharestRO-050711 Romania

By replacing the internal energy with the free energy, as coordinates in a"space of observables", we slightly modify (the known three) non-holonomic geometrizations from [57, 58, 63] and show that the coefficients of the curvature tensor field, of the Ricci tensor field and the scalar curvature function still remain rational functions. In addition, we define and study a new holonomic Riemannian geometric model associated, in a canonical way, to the Gibbs-Helmholtz equation from Classical Thermodynamics. The main geometric invariants are determined and some of their properties are derived. Using this geometrization, we characterize the equivalence between the Gibbs-Helmholtz entropy and the Boltzmann-Gibbs-Shannon, Tsallis and Kaniadakis entropies, respectively, by means of three stochastic integral equations. We prove that some specific (infinite) families of normal probability distributions are solutions for these *** Codes 58A17, 53A07, 53B50, 82-10, 89-10, 80A10 © 2023, CC BY-NC-ND.

关键词： Free energy

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Utilizing probabilistic entanglement between sensors in quantum networks

arXiv

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arXiv 2024年

作者： Van Milligen, Emily A. Gagatsos, Christos N. Kaur, Eneet Towsley, Don Guha, Saikat Department of Physics The University of Arizona 1630 East University Boulevard TucsonAZ85721 United States Department of Electrical and Computer Engineering The University of Arizona 1630 East University Boulevard TucsonAZ85721 United States Wyant College of Optical Sciences The University of Arizona 1630 East University Boulevard TucsonAZ85721 United States Program in Applied Mathematics The University of Arizona TucsonAZ85721 United States Cisco Quantum Lab Los Angeles United States College of Computer Science University of Massachusetts AmherstMA United States

One of the most promising applications of quantum networks is entanglement assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precision timekeeping, field sensing, and biological imaging. When measuring multiple spatially distributed parameters, current literature focuses on quantum entanglement in the discrete variable case—and quantum squeezing in the continuous variable case distributed amongst all of the sensors in a given network. However, it can be difficult to ensure all sensors pre-share entanglement of sufficiently high fidelity. This work probes the space between fully entangled and fully classical sensing networks by modeling a star network with probabilistic entanglement generation that is attempting to estimate the average of local parameters. The quantum Fisher information is used to determine which protocols best utilize entanglement as a resource for different network conditions. It is shown that without entanglement distillation there is a threshold fidelity below which classical sensing is preferable. For a network with a given number of sensors and links characterized by a certain initial fidelity and probability of success, this work outlines when and how to use entanglement, when to store it, and when it needs to be distilled. © 2024, CC BY.

关键词： Quantum entanglement

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Start-to-End Software Model: engineering and Reverse-engineering Laser Systems

Start-to-End Software Model: Engineering and Reverse-Enginee...

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CLEO: Science and Innovations, S and I 2022

作者： Hirschman, Jack Lemons, Randy Kroetz, Peter Carbajo, Sergio Department of Applied Physics Stanford University 348 Via Pueblo StanfordCA94305 United States SLAC National Accelerator Laboratory 2575 Sand Hill Rd Menlo ParkCA94025 United States Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Atomically Resolved Dynamics Division Max Planck Research Department for Structural Dynamics University of Hamburg Hamburg20146 Germany Electrical and Computer Engineering Department UCLA 420 Westwood Plaza Los AngelesCA90095 United States

ISBN: (纸本)9781557528209

A full start-to-end (S2E) software model of a laser system - including oscillator, amplifier, stretcher/compressor, pulse shaper, and non-linear conversion - can be essential in choosing the hardware design and establishing limitations on what can be accomplished given a specific h ardware s etup. F urthermore, s uch S 2E m odels c an e ven a llow o ne to reverse engineer a laser system should it be necessary for characterization. Here, we present a generalized S2E model of a photoinjector laser system, currently targeted for X-ray Free-Electron Lasers but which can readily be adapted to any broadly defined laser system. © Optica Publishing Group 2022, © 2022 The Author(s)

关键词： Free electron lasers

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