jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Olga A. Marina | R. Springer | Long Le | Seraphim Belko | Chris Coyle | Sangbong Ryu | Surendra B. Karki | Lorraine Seymour | Doyeub Kim | Matt Olszta
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Investigation of Long-Term Durability of Solid Oxide Electrolysis Cells High temperature electrolysis is an efficient technology to produce hydrogen, and solid oxide cell (SOC) stacks can achieve over 95% electrical efficiency. To become a commercial technology, high temperature electrolyzers need to demonstrate at least a 40,000-60,000 hours of reliable operation. In this work, we performed systematic degradation studies of Ni-YSZ and LSCF-GDC electrodes for 1,000-12,000 hours in a broad range of experimental conditions, while varying temperature, steam concentration, steam utilization, current, voltage, operating pressure, Cr and S contaminants, and applying dynamic operation in order to map potential degradations and establish their impact on device life. Tests were conducted in either potentiostatic or galvanostatic mode and at the open-circuit voltage (OCV) to separate effects of electrochemical aging from materials thermal aging. Multiple repeats were used to eliminate the occasional artifacts and atypical trends. Steam concentration was varied from 10 to 98% and steam utilization was adjusted to 30-85%. Electrochemical characteristics, such as current, voltage, and area specific resistances were measured continuously over time. In each test, the ohmic and polarization resistances were obtained using electrochemical impedance spectroscopy. Following each test termination, extensive post-test characterization of Ni-YSZ and LSCF microstructures using SEM/EDS, EBSD, and STEM was performed. Advanced machine learning tools were applied to provide the quantitative estimation on the probability of electrode microstructure variation under different operating conditions. High-temperature electrolysis ; Electrolysis ; Electrode ; Oxide ; Microstructure ; Polarization (electrochemistry) ; Durability ; Electrochemistry Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes
Authors: Jiaxuan Xue | Cheng Chen | Shilei Tian | Yalong Wang | Zihan Wang | Jixin Wang | Wu Zhao | Zhiyong Zhang | Johan Stiens
Source: Journal of Sol-Gel Science and Technology (article, 2025-10-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermally switchable absorption in VO_2–graphene terahertz metamaterials enabled by machine learning optimization Absorption (acoustics) ; Metamaterial ; Terahertz radiation ; Graphene ; Dielectric ; Broadband ; Metamaterial absorber ; Layer (electronics) Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Transition Metal Oxide Nanomaterials
Authors: Hui-Dong Zhang | Xiao-Dong Li | Yan-Yu Xie | Xiuying Liu | Jing-Xin Yu | Junfei Wang
Source: Microporous and Mesoporous Materials (article, 2025-11-01)
Field: Chemistry — Inorganic Chemistry

Machine learning assisted high-throughput computational investigation of SF6/N2 separation in pure silica zeolite structures Zeolite ; Adsorption ; Pressure swing adsorption ; Vacuum swing adsorption ; Air separation ; Selective adsorption ; Volume (thermodynamics) Zeolite Catalysis and Synthesis ; Carbon Dioxide Capture Technologies ; Mesoporous Materials and Catalysis
Authors: Yifan Li | Bingjia Yang | Chunyi Zhang | Axel Gomez | Pinchen Xie | Yuli Chen | Pablo M. Piaggi | Roberto Car
Source: arXiv (Cornell University) (preprint, 2025-12-30)
Field: Materials Science — Materials Chemistry

Assessment of First-Principles Methods in Modeling the Melting Properties of Water First-principles simulations have played a crucial role in deepening our understanding of the thermodynamic properties of water, and machine learning potentials (MLPs) trained on these first-principles data widen the range of accessible properties. However, the capabilities of different first-principles methods are not yet fully understood due to the lack of systematic benchmarks, the underestimation of the uncertainties introduced by MLPs, and the neglect of nuclear quantum effects (NQEs). Here, we systematically assess first-principles methods by calculating key melting properties using path integral molecular dynamics (PIMD) driven by Deep Potential (DP) models trained on data from density functional theory (DFT) with SCAN, revPBE0-D3, SCAN0 and revPBE-D3 functionals, as well as from the MB-pol potential. We find that MB-pol is in qualitatively good agreement with the experiment in all properties tested, whereas the four DFT functionals incorrectly predict that NQEs increase the melting temperature. SCAN and SCAN0 slightly underestimate the density change between water and ice upon melting, but revPBE-D3 and revPBE0-D3 severely underestimate it. Moreover, SCAN and SCAN0 correctly predict that the maximum liquid density occurs at a temperature higher than the melting point, while revPBE-D3 and revPBE0-D3 predict the opposite behavior. Our results highlight limitations in widely used first-principles methods and call for a reassessment of their predictive power in aqueous systems. Liquid water ; Range (aeronautics) ; Density functional theory ; Molecular dynamics ; Melting temperature ; Path integral formulation ; Experimental data ; Water model Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Ismat H. Ali | Salman Saeidlou | Pradeep Kumar Singh | Ali Alamry | Amra Al Kenany | Ali Asghar Javidparvar
Source: Journal of Pipeline Science and Engineering (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

From Data-Driven Waveform Design for Pulsed Current Cathodic Protection to Full-Scale Mechanical Validation: Improving the Service Life of Steel Pipelines • GA–GPR framework created an optimal pulsed-current cathodic protection waveform. • Evolutionary GAN augmented finite-difference data for robust machine learning. • Optimized PCCP lowered pit depth and density versus conventional direct current CP. • XPS showed higher Fe²⁺/Fe³⁺ ratio and denser carbonate film with optimized waveform. • Burst tests confirmed higher pressure capacity and improved pipeline integrity. Disbonded coatings form crevice-like environments in which conventional direct-current cathodic protection (DCCP) may not provide uniform protection, thereby promoting localized corrosion and compromising pipeline integrity. This study designs a pulsed-current cathodic protection (PCCP) waveform to enhance protection beneath disbonded coatings while preserving mechanical performance. A finite difference (FD) model was used to resolve transient potential and oxygen-transport behavior under various PCCP conditions. The resulting simulation dataset was used to train a Gaussian-process regression (GPR) model (with evolutionary generative adversarial network (EGAN)-assisted data augmentation), which was embedded in a genetic algorithm (GA) to optimize the PCCP waveform. The optimized PCCP regime was validated in large-scale soil-burial experiments on coated steel pipeline sections and benchmarked against DCCP. Surface-chemistry and pit-morphology assessments, together with full-scale hydrostatic burst tests, confirmed that waveform-optimized PCCP markedly reduced localized corrosion and better retained integrity. In particular, pit populations became sparser and shallower, the effective pit length decreased from 157.32 mm (DCCP) to 3.82 mm, and burst-pressure capacity was only decreased by a 0.15% over the tests period as opposed to a 13.12% decrement for the DCCP-protected section. Cathodic protection ; Pipeline (software) ; Corrosion ; Waveform ; Pipeline transport ; Service life ; Passivation Corrosion Behavior and Inhibition ; Geotechnical Engineering and Underground Structures ; Structural Integrity and Reliability Analysis
Authors: Feijie Wang | Chao Jia | Shenzhuo Zhang | Suyang Wang | Yueming Hu | Yichi Liu | Junhua Zhao | Shufeng Ma | Liqiang Wang
Source: Advanced Functional Materials (article, 2025-11-04)
Field: Chemistry — Spectroscopy

Self‐Powered Wood Aerogels for Efficient Air Filtration via Cell Wall Nanostructural Reconfiguration and Diatom‐Inspired Silicification Abstract Airborne particulate matter (PM) poses growing threats to human health and the environment, especially in the context of emerging infectious diseases. Conventional petroleum‐based filters are constrained by an inherent trade‐off between filtration efficiency and air resistance, and additionally suffer from a lack of mechanical durability, thermal protection, and environmental degradability. Here, a multifunctional triboelectric‐assisted aerogel filter is fabricated through a synergistic approach combining cell wall nano‐reconstruction with diatom‐inspired mineralization. This strategy constructs a hierarchical porous framework that regulates charge storage sites, leading to a 71% improvement in charge retention compared with native wood and a significant enhancement in triboelectric output. As a self‐powered filtration platform, the system achieves outstanding removal efficiencies for PM 0.3 (98.75% ± 0.08%), PM 0.5 (99.51% ± 0.21%), and PM 1 (99.98% ± 0.02%) with a low pressure drop of 53 Pa. Meanwhile, the aerogel demonstrates remarkable compressive resilience (18.1 MPa at 60% radial strain) and complete elastic recovery, together with robust thermal insulation and flame retardancy. Notably, the integrated triboelectric signal is analyzed using deep learning algorithms to identify respiratory patterns, thereby enabling real‐time health monitoring and intelligent respiratory management. This work establishes a sustainable, high‐performance material platform for advanced air purification and wearable healthcare applications in extreme environments. Aerogel ; Filtration (mathematics) ; Triboelectric effect ; Context (archaeology) ; Air filter ; Air filtration ; Porosity ; Resilience (materials science) Aerogels and thermal insulation ; Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials
Authors: 陈朋波 Chen Pengbo | Shaoyi Wang | Zhang Wen-bo | Jiaxing Wen | Wu Yu-Chi | Zongqing Zhao | Wei Du
Source: Acta Physica Sinica (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Deep learning-based design of long-wave infrared dielectric grating accelerator structures Dielectric laser accelerators (DLAs), as compact particle accelerators, rely critically on their structural design to determine both the energy gain and beam quality of accelerated bunches. Although most existing DLAs are driven by near-infrared lasers with a wavelength of approximately 1 μm, the use of long-wave infrared (LWIR) lasers at a wavelength ten times that of this wavelength indicates that it is possible to achieve excellent beam quality without sacrificing acceleration gradient. To address the lack of optimized structural designs in the LWIR band where long-distance acceleration poses unique challenges—we introduce a deep learning–based design method for LWIR dielectric grating accelerator structures. Our approach integrates geometric parameters, material properties, and optical-field energy metrics into a unified evaluation framework and uses a surrogate model to predict particle energy gain with high precision. Optimal structural parameters are then extracted to realize the final design. The simulation results show that the energy gain is 99.5 keV (a year-over-year increase of 19.9%), the transmission efficiency is 100%, the beam spot radius of 14.5 μm, and the average beam current is 20.4 fA, which is 6.9 times higher than similar near-infrared gratings, while maintaining equivalent beam brightness. This work provides a feasible technical route for designing high-netgain LWIR dielectric grating accelerators and a novel framework for optimizing the structure of complex optoelectronic devices. Grating ; Dielectric ; Infrared ; Materials science ; Optics ; Optoelectronics ; Physics ; Computer science Optical Coatings and Gratings ; Photonic and Optical Devices ; Optical Systems and Laser Technology
Authors: Chenggang Zhang | Meng Wang | Yi Li | Miao Yu | Jie Zhang | Ye Sun
Source: Sensors and Actuators B Chemical (article, 2025-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rapid meningitis pathogen typing via bioreceptor-free AI-SERS based on bacterial surface charge regulation Pathogen ; Typing ; Bacterial meningitis ; Surface charge ; Surface-enhanced Raman spectroscopy ; Raman spectroscopy ; Biosensor ; Charge (physics) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Kongping Wu | Meiyong Liao
Source: Institutional Repositories DataBase (IRDB) (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Mapping phonon dynamics to thermal transport <i>via</i> deep-learning NEMD: AlN/diamond interface engineering for GaN heat dissipation Diamond offers excellent heat sink for high power high-electron-mobility transistors based on III-nitrides. However, the GaN/diamond interfaces suffer from low thermal conductance due to phonon mismatch. Although AlN interlayers can mitigate this issue, processing-induced carbon vacancies and subsurface disorder near the AlN/diamond interface recreate a new thermal bottleneck. In this study, we employ deep learning-enhanced non-equilibrium molecular dynamics (NEMD) simulations to investigate atomic-scale thermal transport across AlN/diamond interfaces, with a particular focus on quantifying the impact of carbon vacancies. Results show interfacial thermal conductance (ITC) for AlN-Al/C(1 1 1) depends non-monotonically on the carbon vacancy concentration. The ITC peaks at 151.7 MW·m−2K−1 at a carbon vacancy concentration of 2.4% due to the formation of resonant vibrational states that bridge the phonon gap and promote phonon delocalization, enabling efficient tunneling across the interface. However, beyond this optimal concentration, vacancy clustering induces destructive phonon interference, strong scattering, and severe localization effects, leading to a sharp decline in ITC. This work provides a pathway for optimizing AlN thermal bridges to achieve low thermal resistance in GaN-on-diamond devices through precise control of vacancy concentration and crystallinity. Phonon ; Thermal ; Dissipation ; Interface (matter) ; Dynamics (music) ; Thermal conductivity ; Thermal management of electronic devices and systems Thermal properties of materials ; GaN-based semiconductor devices and materials ; Machine Learning in Materials Science
Authors: Farhan Tanvir Nabil | Gürşans Güven
Source:  (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Detection of Structural Components and Data from Legacy Engineering Drawings The lack of digitization and efficient utilization of legacy data stored in paper-based engineering drawings pose a significant challenge in the maintenance and management of aging infrastructure, as these documents are critical for assessing structural conditions and planning repairs.Extracting and organizing information from a large volume of drawings is traditionally a manual, timeconsuming, and error-prone process.This paper presents an ongoing research on developing an automated asset inventory framework designed to extract, structure, and utilize data from legacy engineering drawings using a state-of-the-art Deep Learning (DL)-based object detection model (YOLOv11).The proposed methodology integrates YOLOv11 to identify and extract geometric and nongeometric data from scanned engineering drawings.The dataset, collected from real-world electrical substations, includes key structural assets information such as asset names, ages, cross-sectional views, plan views, dimensions, and general notes.Preliminary findings demonstrate that the model accurately detects and localizes critical structural components and their attributes, achieving a mean average precision (mAP) of 0.953 and an F1 score of 0.92 at a 0.344 confidence level, validating its effectiveness in automatically extracting information with high accuracy.By automating an image-based asset inventory creation from legacy engineering drawings, this framework facilitates structural condition assessment at electrical substations.Additionally, it assists informed decision-making for repairs, material reuse, and lifecycle optimization.By streamlining data extraction from legacy engineering drawings, this research contributes to the development of more efficient, data-driven infrastructure asset management practices. Component (thermodynamics) ; Visualization ; Line drawings ; Process (computing) Machine Learning in Materials Science ; Topology Optimization in Engineering ; Advanced Neural Network Applications
Authors: Qimeng Zhang | Yaru Chai | Xinyu Li | Ziqi Wang | Xi Zhang | Clement Yaw Effah | Lihua Ding | Yongjun Wu
Source: Talanta (review, 2025-09-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface-enhanced Raman spectroscopy for protein detection: Challenges and countermeasures Raman spectroscopy ; Protein detection ; Surface-enhanced Raman spectroscopy ; Spectroscopy ; Core (optical fiber) Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: E-Ping Tsai | I-Hsuan Chou | Yu-Chuan Lin | Sanskruti Swain | Ting Lin | Y.J. Ting | Hsing Ying Lin | Chen-Han Huang
Source:  (article, 2025-03-21)
Field: Chemistry — Analytical Chemistry

Deep-learning-enhanced SERS tag analysis for multiplex detection of cancer cells Surface-enhanced Raman scattering (SERS) tags, combining nanoparticles with Raman reporter molecules, offer high sensitivity and quantification for detecting target molecules. However, analyzing SERS spectra, especially in mixtures, is challenging due to interference and noise. This study developed a Convolutional Neural Network-based model trained on 10,000 augmented SERS spectra to identify components in mixtures. Baseline correction using airPLS and the Nonnegative Elastic Net algorithm enabled accurate ratio estimation of each reporter. The system used antibody-functionalized SERS tags for multiplex labeling and AI spectral analysis for component classification and concentration calculation in single cancer cells, showing high detection specificity. This method significantly enhances multispectral recognition, promising advances in tumor cell analysis. Multiplex ; Cancer detection ; Computer science ; Cancer ; Deep learning ; Artificial intelligence ; Nanotechnology ; Materials science ; Medicine ; Bioinformatics ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Hao Hu | Mengjie Li | Huangwei Zhang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Dynamic Surrogate-Assisted Particle Swarm Optimization for Chemical Kinetic Mechanisms Particle swarm optimization ; Kinetic energy ; Particle (ecology) ; Computer science ; Mathematical optimization ; Physics ; Mathematics ; Classical mechanics ; Algorithm ; Biology Various Chemistry Research Topics ; Innovative Microfluidic and Catalytic Techniques Innovation ; Dendrimers and Hyperbranched Polymers
Authors: Tapan Kumar Mohanta | Karan Martens Mohanta
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-25)
Field: Chemistry — Spectroscopy

Meltome Atlas of <i>Arabidopsis thaliana</i> Proteome: A Melting Temperature-Based Identification of Heat &amp; Cold Resistant Proteins Abstract The study elucidated the thermal properties of the entire Arabidopsis thaliana proteome by considering the melting temperature (Tm) and the melting temperature index (TI). In total, 48359 protein sequences were analyzed, and the melting temperature of the proteins was recorded in three groups (Tm &lt; 55°C, 55-65°C, and &gt; 65°C). The Tm index of the A. thaliana proteome ranged from -15.6008 (&lt; 55°C) to 9.605 (&gt; 65°C). At least 22826 proteins were found in the Tm group of 55°C to 65°C, 20640 proteins were found in the Tm group of &gt; 65°C, and only 4893 proteins were found in the Tm group of &lt; 55°C. The mediator of RNA polymerase II transcription subunit-like protein was found to possess the highest Tm index (9.60), while the NADH dehydrogenase 5B subunit was found to contain the lowest TI (-15.60). The amino acid composition analysis of the A. thaliana proteome revealed that the frequency of Ala, Asp, Glu, Gly, Lys, Gln, and Val increased with the increase in Tm, while the amino acids Cys, Phe, and Trp decreased with the increase in the Tm of the A. thaliana proteome. The molecular mass of the A. thaliana proteome ranged from 0.149 to 611.888 kDa, and protein in the Tm group at 55-65°C showed the highest average molecular mass. The machine learning analysis revealed an increase in the molecular mass positively correlated with the increase in the Tm of the proteins. The codon usage pattern revealed, the codon pair prefer the Tm group specific occurrence where ATG-ATG, CAA-CAA codon pairs were predominated. Relative synonymous codon usage of the three Tm groups revealed AGA (Arg) and CCA (Pro) were the preferred codons for the low and high Tm group DNA sequences, respectively. Codon context analysis revealed the presence of preferences of the Tm group specific codon pairing. There was a variation in the nucleotide position of the codons in different Tm groups. Evolutionary study revealed, gene duplication was the predominant evolutionary feature and all of the studied genes in the three Tm group undergone duplication. Codon context analysis revealed distinct clustering pattern in high Tm protein group. The study underscores the role of amino acid composition, molecular mass, and codon usage in determining the thermal stability of the proteins in the A. thaliana . The study reflected the evolution of high Tm-adapting genes through gene duplication, highlighting the role of gene and genome evolution towards encoding high Tm proteins for stress resilience. Proteome ; Arabidopsis thaliana ; Melting temperature ; Atlas (anatomy) ; Chemistry ; Biology ; Biochemistry ; Materials science ; Anatomy ; Gene Advanced Proteomics Techniques and Applications
Authors: Thamer Sami Alhalaybeh | Yanshan Lou
Source: Archives of Civil and Mechanical Engineering (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Experimental characterization and numerical simulation of the thermoforming behavior of AA6061-T6 round bars at high temperatures Finite element method ; Ultimate tensile strength ; Plasticity ; Thermoforming ; Artificial neural network ; Nonlinear system ; Constitutive equation ; Subroutine ; Robustness (evolution) High-Velocity Impact and Material Behavior ; Metal Forming Simulation Techniques ; High Temperature Alloys and Creep