jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhanpeng Sun | Junjie Zou | Rui Li | Zhaofu Zhang | Jinsheng Mai | Zijun Qi | David Vázquez-Cortés | Qijun Wang | Gai Wu | Wei Shen | Sheng Liu
Source: Journal of Material Science and Technology (article, 2025-04-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Laser stealth dicing of β-Ga2O3: Theoretical and experimental studies • A combination of MD simulations and experiments is employed to investigate the LSD process and its microscopic mechanisms for β-Ga 2 O 3 . • It is demonstrated that MD simulations can provide practical guidance for the LSD process of β-Ga 2 O 3 wafers. • LSD is successfully performed on β-Ga 2 O 3 wafers in the laboratory under the guidance of MD simulations, resulting in good fracture surface quality. • The surface roughness (Ra) after LSD along the (100) crystal plane reaches 0.033 µm within a 218 µm × 218 µm area. Gallium oxide (Ga 2 O 3 ) is an ultra-wide bandgap semiconductor with excellent potential for high-power and ultraviolet optoelectronic device applications. High-performance Ga 2 O 3 -based high-power devices rely heavily on precise processing, especially in wafer dicing. Laser stealth dicing (LSD) is an innovative laser technology that utilizes a focused laser to create subsurface modifications in the wafer without surface damage. LSD has broad application prospects in the field of semiconductor precision processing. In this work, the idea of achieving high-quality dicing of β-Ga 2 O 3 wafers via LSD was proposed. A combination of atomistic simulations and experiments was used to understand the underlying mechanism of LSD of β-Ga 2 O 3 wafers. On the one hand, the laser loading and fracture process of β-Ga 2 O 3 wafers were simulated using molecular dynamics (MD) methods as well as a machine learning potential. The effects of single-pulse energy on LSD were analyzed through the lattice residual pressure, the final total energy of the system, the internal atomic strain, and the maximum stress value during uniaxial tension. On the other hand, based on the MD simulations, LSD was successfully performed on β-Ga 2 O 3 wafers along three main crystal planes in the laboratory, resulting in good surface quality. This work not only provides profound optimization strategies for the LSD process of β-Ga 2 O 3 , establishing the foundation for high-quality dicing of β-Ga 2 O 3 wafers, but also verifies the accuracy of MD simulations in predicting trends related to the LSD, offering a potential approach for high-quality dicing of other materials in future research. Wafer dicing ; Laser ; Materials science ; Optics ; Optoelectronics ; Physics ; Wafer Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Abdulrahman Aljanaahi | Muhammad K. Hakeem | Abdulla Aljanaahi | Iltaf Shah
Source: Analytical Science Advances (review, 2025-04-16)
Field: Chemistry — Spectroscopy

A Review of Analytical and Chemometric Strategies for Forensic Classification of Homemade Explosives Homemade explosives (HMEs), commonly used in improvised explosive devices (IEDs), present a significant forensic challenge due to their chemical variability, accessibility and adaptability. Traditional forensic methodologies often struggle with environmental contamination, complex sample matrices and the non-specificity of precursor residues. Recent advances in analytical techniques and chemometric methods have enhanced the detection, classification and interpretation of explosive residues. Infrared (IR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) have seen improvements in spectral resolution and real-time detection capabilities, allowing for more accurate differentiation of explosive precursors. Thermal analysis techniques, such as thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), now provide refined kinetic modelling to assess the decomposition pathways of unstable energetic materials, improving forensic risk assessments. Additionally, x-ray diffraction (XRD) has contributed to forensic material sourcing by distinguishing between industrial-grade and improvised explosive formulations. Chemometric approaches, including principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA), have revolutionized forensic data analysis by improving classification accuracy and enabling automated identification of explosive components. Advanced machine learning models are being integrated with spectral datasets to enhance real-time decision-making in forensic laboratories and portable field devices. Despite these advancements, challenges remain in adapting laboratory-based techniques for field deployment, particularly in enhancing the sensitivity and robustness of portable analytical instruments. This review critically evaluates the latest developments in forensic analytical chemistry, highlighting strengths, limitations and emerging strategies to improve real-world HME detection and classification. Explosive material ; Chemometrics ; Linear discriminant analysis ; Principal component analysis ; Partial least squares regression ; Computer science ; Forensic science ; Artificial intelligence ; Biochemical engineering ; Pattern recognition (psychology) ; Machine learning ; Engineering ; Chemistry ; Geography Mass Spectrometry Techniques and Applications ; Energetic Materials and Combustion ; Bacillus and Francisella bacterial research
Authors: Jingwen Wang | Zheng Zhu | Tianran Jiang | Ke Chen
Source: npj Computational Materials (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Machine learning revealed giant thermal conductivity reduction by strong phonon localization in two-angle disordered twisted multilayer graphene In two-dimensional (2D) layer-stacked materials, the twist angle between layers provides extensive freedom to explore novel physics and engineer remarkable thermal transport properties. We discovered that the cross-plane thermal conductivity of multilayer graphene can be effectively controlled by arranging the layers with two specific twist angles in a defined sequence. Disorderly aperiodic twisted graphene layers lead to the localization of phonons, substantially reducing the cross-plane thermal transport via the interference of coherent phonons. We employed non-equilibrium molecular dynamics simulations combined with machine learning approach, to study heat transport in the two-angle disordered multilayer stacks, and identified within the constrained structural space the optimal stacking sequence that can minimize the cross-plane thermal conductivity. Compared to pristine graphite, the optimized structure can reduce thermal conductivity by up to 80%. Through analysis of phonon transport properties across different structures, we revealed the underlying physical mechanism of phonon localization. Graphene ; Phonon ; Thermal conductivity ; Condensed matter physics ; Materials science ; Reduction (mathematics) ; Thermal ; Physics ; Nanotechnology ; Composite material ; Geometry ; Mathematics ; Thermodynamics Thermal properties of materials ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Gaurav Arora | Harshit Sharma | Papiya Bhowmik | Manoj Kumar Singh | Vinod Ayyappan | Sanjay Mavinkere Rangappa | Suchart Siengchin
Source: Archives of Computational Methods in Engineering (article, 2025-11-03)
Field: Materials Science — Polymers and Plastics

Recent Studies on Multiscale Modeling of Natural Fiber-Reinforced Composites Multiscale modeling ; Finite element method ; Natural (archaeology) ; Viscoelasticity ; Macro ; Representative elementary volume ; Durability ; Uncertainty quantification Natural Fiber Reinforced Composites ; Composite Material Mechanics ; Mechanical Behavior of Composites
Authors: Ahmad‐Reza Sadeghi | M. Shariatmadar | S. Amoozadeh | Alireza Mahmoudi Nahavandi | Mohammad Mahdavian
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2025-01-28)
Field: Materials Science — Materials Chemistry

Unlocking the potential of FTIR for corrosion inhibition prediction exploiting principal component analysis: Machine learning for QSPR modeling Principal component analysis ; Quantitative structure–activity relationship ; Fourier transform infrared spectroscopy ; Component (thermodynamics) ; Corrosion ; Computer science ; Chemistry ; Biological system ; Artificial intelligence ; Machine learning ; Chemical engineering ; Thermodynamics ; Engineering ; Organic chemistry ; Physics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Zhanpeng Sun | Hutao Shi | Yi Zhu | Rui Li | Xiang Sun | Qijun Wang | Zijun Qi | Lijie Li | Sheng Liu | Wei Shen | Gai Wu
Source: npj Computational Materials (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

A comprehensive exploration of thermal transport at Cu/diamond interfaces via machine learning potentials The fundamental thermal limitation of pure copper impedes progress in high-power devices, which is becoming more critical with advances in power electronics. The Cu/diamond composite becomes a promising candidate for thermal management due to its excellent theoretical thermal conductivity and customizable coefficient of thermal expansion (CTE). Actually, the thermal conductivity of Cu/diamond composite is much lower than its theoretical value, for which a key bottleneck is interfacial thermal transport at the Cu/diamond interface. However, many atomic-level microscopic mechanisms of heat transport at Cu/diamond interfaces remain poorly understood at present. Especially when different interlayer materials are involved, theoretical studies become extremely complex and challenging. In this work, a machine learning potential for comprehensive simulations of thermal transport at Cu/diamond interfaces has been successfully constructed. The effects of key factors, such as interlayer material, temperature, strain, and crystal orientation, on heat transport at Cu/diamond interfaces have been studied. Furthermore, the underlying mechanisms are thoroughly analyzed and discussed. Finally, the insightful strategies are proposed to optimize and enhance the thermal properties of Cu/diamond interfaces. These advancements can lay a foundation and pave the way for further investigations into interfacial thermal transport at Cu/diamond interfaces as well as in other structures containing interlayer materials. Thermal conductivity ; Bottleneck ; Thermal ; Thermal grease ; Thermal barrier coating ; Key (lock) ; Composite number ; Thermal diffusivity Thermal properties of materials ; High-pressure geophysics and materials ; Aluminum Alloys Composites Properties
Authors: Shrouq H. Aleithan | Waqas Ahmad
Source: Nanoscale (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Anisotropic photodetection in borophene: exploiting directional optical responses for next-generation polarization-sensitive devices Borophene, a rapidly emerging two-dimensional (2D) boron allotrope, has garnered significant attention owing to its inherent anisotropic structural, electronic, and optical properties, positioning it as an ideal material for polarization-sensitive photodetection. This review thoroughly explores the foundational aspects underpinning borophene's directional optical behaviors, closely tied to its distinct lattice architecture and anisotropic electronic attributes. Advanced synthesis methods, particularly molecular beam epitaxy and chemical vapor deposition are critically analyzed, emphasizing structural precision to optimize anisotropic characteristics. Additionally, recent advancements in device engineering are discussed, highlighting pioneering strategies in electrode configuration, interface tailoring, heterostructure integration, plasmonic enhancement, and strain modulation, all aimed at improving polarization selectivity, device responsivity, and spectral sensitivity. Significant experimental breakthroughs showcasing remarkable anisotropic photodetection capabilities spanning broadband spectral ranges are extensively evaluated. The review concludes by outlining ongoing challenges, potential research pathways, and innovative opportunities, notably emphasizing the integration of machine learning (ML) and artificial intelligence (AI). Leveraging these computational techniques is anticipated to accelerate the advancement and fine-tuning of future borophene-based photodetectors. This review provides insights for researchers to harness borophene's anisotropic properties, accelerating advancements in polarization imaging, flexible optoelectronics, and adaptive sensing technology applications. Photodetection ; Borophene ; Materials science ; Optoelectronics ; Anisotropy ; Polarization (electrochemistry) ; Nanotechnology ; Optics ; Photodetector ; Physics ; Monolayer ; Chemistry 2D Materials and Applications ; Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials
Authors: Yangbo Chen | Cheng Li | Shan Liu | Shanmin Gao | Chenyi Huang | Xin Yu | Xiangrui Xu | Haibo Ke | Dezhen Xue | Gui Yu | Zhe Liu | Mengyan Dai | Xueao Zhang
Source: Small (article, 2025-01-28)
Field: Materials Science — Materials Chemistry

Deep‐Learning‐Enabled Fast Raman Identification of the Twist Angle of Bi‐Layer Graphene Twisted bilayer graphene (TBG) has drawn considerable attention due to its angle-dependent electrical, optical, and mechanical properties, yet preparing and identifying samples at specific angles on a large scale remains challenging and labor-intensive. Here, a data-driven strategy that leverages Raman spectroscopy is proposed in combination with deep learning to rapidly and non-destructively decode and predict the twist angle of TBG across the full angular range. By processing high-dimensional Raman data, the deep learning model extracts hidden information to achieve precise twist angle identification. This approach is further extended to a 2D plane, enabling accurate orientational mapping within individual samples. Through interpretability analysis, the model is validated in conjunction with first-principles theoretical calculations, ensuring robust and explainable results. This data-driven methodology not only facilitates efficient TBG characterization but also introduces a broadly applicable framework for studying other angle-dependent 2D materials, thereby advancing the field of material spectroscopy and analysis. Interpretability ; Raman spectroscopy ; Graphene ; Materials science ; Deep learning ; Characterization (materials science) ; Identification (biology) ; Contact angle ; Artificial intelligence ; Nanotechnology ; Computer science ; Optics ; Physics Graphene research and applications ; 2D Materials and Applications ; Thermal properties of materials
Authors: Isabella Vasquez | Ruiyang Xue | Indrajit Srivastava
Source: Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology (review, 2025-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface‐Enhanced Raman Scattering Nanotags: Design Strategies, Biomedical Applications, and Integration of Machine Learning Surface-enhanced Raman scattering (SERS) is a transformative technique for molecular identification, offering exceptional sensitivity, signal specificity, and resistance to photobleaching, making it invaluable for disease diagnosis, monitoring, and spectroscopy-guided surgeries. Unlike traditional Raman spectroscopy, which relies on weak scattering signals, SERS amplifies Raman signals using plasmonic nanoparticles, enabling highly sensitive molecular detection. This technological advancement has led to the development of SERS nanotags with remarkable multiplexing capabilities for biosensing applications. Recent progress has expanded the use of SERS nanotags in bioimaging, theranostics, and more recently, liquid biopsy. The distinction between SERS and conventional Raman spectroscopy is highlighted, followed by an exploration of the molecular assembly of SERS nanotags. Significant progress in bioimaging is summarized, including in vitro studies on 2D/3D cell cultures, ex vivo tissue imaging, in vivo diagnostics, spectroscopic-guided surgery for tumor margin delineation, and liquid biopsy tools for detecting cancer and SARS-CoV-2. A particular focus is the integration of machine learning (ML) and deep learning algorithms to boost SERS nanotag efficacy in liquid biopsies. Finally, it addresses the challenges in the clinical translation of SERS nanotags and offers strategies to overcome these obstacles. Nanotechnology ; Raman scattering ; Photobleaching ; Raman spectroscopy ; Biosensor ; Materials science ; Surface-enhanced Raman spectroscopy ; Computer science ; Fluorescence ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Extracellular vesicles in disease
Authors: Yanghwa Kim | Ocktaeck Lim | Hongsuk Kim
Source: Fuel (article, 2025-02-28)
Field: Materials Science — Materials Chemistry

Improvement of low-temperature NOx reduction performance of NH3-SCR with O3 injection under diesel engine transient conditions NOx ; Diesel engine ; Transient (computer programming) ; Diesel fuel ; Chemistry ; Reduction (mathematics) ; Environmental science ; Materials science ; Automotive engineering ; Combustion ; Organic chemistry ; Computer science ; Engineering Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Atmospheric chemistry and aerosols
Authors: Raj Dave | Kshipra Pandey | Vijay Khatri | Ritu Patel | Nidhi Gour | Dhiraj Bhatia
Source: Advanced Biology (review, 2025-03-17)
Field: Materials Science — Materials Chemistry

Biological AIE Molecules: Innovations in Synthetic Design and AI‐Driven Discovery Biological aggregation -induced emission (AIE) molecules offer significant advantages over synthetic organic fluorophores, particularly in biocompatibility, environmental sustainability, and emission properties in biological systems. Derived from biomolecules such as peptides, proteins, and nucleic acids, biological AIE molecules hold great promise for applications in biosensing, bioimaging, and target drug delivery. This review explores the design principles, mechanistic insights, and functional properties of biological AIE molecules whiles highlighting the role of artificial intelligence (AI) in accelerating their discovery and optimization. AI-driven approaches, including machine learning and computational modeling, are transforming the identification and synthesis of AIE molecules by enabling precise structural modifications and enhanced fluorescence efficiency. These advancements are paving the way for the integration of AIE molecules in next-generation smart biomedical devices, personalized medicine and sustainable technological applications. Emerging trends, including hybrid biomaterials, Ai-guided molecular engineering, and advanced imaging techniques, are expanding the scope of biological AIE molecules in healthcare and environmental monitoring. The synergy between AI and biological AIE molecules is unlocking new frontiers in biomedical technology, enabling transformative advancements in material science and healthcare applications, and shaping the future of fluorescence- based diagnostics and therapeutics. Nanotechnology ; Biomolecule ; Drug discovery ; Computer science ; Synthetic biology ; Biochemical engineering ; Chemistry ; Computational biology ; Materials science ; Engineering ; Biology Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques ; Nanoplatforms for cancer theranostics
Authors: Pandu Wisesa | Meng Li | Matthew T. Curnan | Geun Ho Gu | Jeong Woo Han | Judith C. Yang | Wissam A. Saidi
Source: Nano Letters (article, 2025-01-14)
Field: Materials Science — Materials Chemistry

Cu–Ni Oxidation Mechanism Unveiled: A Machine Learning-Accelerated First-Principles and <i>in Situ</i> TEM Study The development of accurate methods for determining how alloy surfaces spontaneously restructure under reactive and corrosive environments is a key, long-standing, grand challenge in materials science. Using machine learning-accelerated density functional theory and rare-event methods, in conjunction with <i>in situ</i> environmental transmission electron microscopy (ETEM), we examine the interplay between surface reconstructions and preferential segregation tendencies of CuNi(100) surfaces under oxidation conditions. Our modeling approach predicts that oxygen-induced Ni segregation in CuNi alloys favors Cu(100)-O c(2 × 2) reconstruction and destabilizes the Cu(100)-O (2√2 × √2)<i>R</i>45° missing row reconstruction (MRR). <i>In situ</i> ETEM experiments validate these predictions and show Ni segregation followed by NiO nucleation and growth in regions without MRR, with secondary nucleation and growth of Cu<sub>2</sub>O in MRR regions. Our approach based on combining disparate computational components and <i>in situ</i> ETEM provides a holistic description of the oxidation mechanism in CuNi, which applies to other alloy systems. Nucleation ; In situ ; Alloy ; Materials science ; Non-blocking I/O ; Transmission electron microscopy ; Metallurgy ; Nanotechnology ; Chemical physics ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides
Authors: Xinyi Song | Jin Zhou | Di Zhang | Shenghao Zhang | Guangxi Li | Feiping Du | Zhongwei Guan | Xuefeng Chen | W.J. Cantwell | V.B.C. Tan
Source: Thin-Walled Structures (article, 2025-05-11)
Field: Materials Science — Ceramics and Composites

A quantitative study of off-axis bending damage in 3D braided ceramic matrix composites Composite material ; Materials science ; Ceramic matrix composite ; Bending ; Matrix (chemical analysis) ; Ceramic Advanced ceramic materials synthesis ; Additive Manufacturing and 3D Printing Technologies ; Innovative concrete reinforcement materials