jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tingting Gong | Zia Ullah | Shubham Roy | Caixue Cheng | Menglong Li | Jing Cheng | Yinghe Zhang | Bing Guo | Yingwei Qiu
Source: International Journal of Nanomedicine (review, 2025-07-01)
Field: Materials Science — Materials Chemistry

Implication of Bimodal Magnetic Resonance and Fluorescence Imaging Probes in Advanced Healthcare: Enhancing Disease Diagnosis and Targeted Therapy Molecular imaging probes hold great promise in disease diagnostics and their therapeutic interventions. However, a single imaging modality sometimes lacks efficiency in all kinds of diseases. Conditions such as cancer, cardiovascular disorders, and neurodegenerative diseases critically require multimodal imaging to characterize complex biological environments accurately. Efficient targeting further enhances probe performance, improving diagnostic precision. This study explores the design rationale and application of bimodal probes designed for Magnetic Resonance (MR) and fluorescence (FL) imaging for their complementary strengths. MRI enables deep tissue visualization, while fluorescence offers high sensitivity and cellular-level resolution. Meticulous attention has been devoted to presenting methodologies aimed at improving the targeting efficacy of these probes. This involves the methodology to enhance targeting efficacy, including ligand-based strategies, nanoparticle functionalization, and molecularly imprinted polymers. The probes combine fluorescence for precise cellular imaging with MRI for in-depth tissue visualization, providing synergistic benefits that elevate their diagnostic potential. Moreover, we offer recent developments in Machine Learning and Artificial Intelligence-based computational approaches for image analysis, enabling more precise diagnosis across a range of diseases that may propel their diagnostic abilities for better therapeutic outcomes. Through systematic analysis and in vitro and in vivo evaluations, we demonstrate the ability of the probes to achieve superior spatial and temporal resolution, facilitating the accurate delineation of biological targets. The integration of these bimodal probes holds great promise for advancing our understanding of complex biological processes, enabling more precise diagnostics, and paving the way for targeted therapeutic interventions. Magnetic resonance imaging ; Materials science ; Fluorescence ; Medicine ; Medical imaging ; Fluorescence-lifetime imaging microscopy ; Health care ; Nanotechnology ; Nuclear magnetic resonance ; Radiology ; Optics ; Physics Lanthanide and Transition Metal Complexes ; Carbon and Quantum Dots Applications ; Nanoplatforms for cancer theranostics
Authors: Xiaobo Ma | Zeyu Yin | Zhen Cao | Baolong Shi | Xin Yan | Di Wu | Yue‐Yue Wang | Chao‐Qing Dai | Min Hong
Source: Chemical Engineering Journal (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Machine learning assisted photocurrent wavelength classification in a self-powered broadband photoelectrochemical photodetector based on Bi2Te0.6Se2.4/MoSe2 heterojunctions Self-powered operation and broadband spectral adaptability are critical for the next generation of photodetectors, enabling sustainable performance and multispectral functionality. The broadband wavelength response capability can expand the application scenarios of a photodetector. However, achieving both broadband photoresponse and accurate wavelength recognition remains a significant challenge. Herein, we develop a self-powered photoelectrochemical photodetector based on Bi 2 Te 0.6 Se 2.4 /MoSe 2 heterojunctions, capable of identifying 256, 365, 546, and 650 nm and simulated sunlight with the assistance of machine learning. To achieve precise energy band alignment with MoSe 2 and enhance the heterojunction interface, the selenium content in Bi 2 Te 3-x Se x and the mass ratio of the composite components were systematically optimized. These structural optimizations improve charge carrier separation and transport via band structure engineering and interfacial modulation, resulting in superior photoelectrochemical performance. By integrating material design with machine-learning-assisted wavelength discrimination, this study demonstrates a multidisciplinary approach to broadband detection and highlights the potential of such devices in optical communication, environmental monitoring, and multispectral imaging applications. • Integration of Bi 2 Te 0.6 Se 2.4 and MoSe 2 for PEC photodetection, combining complementary light absorption and efficient charge separation. • Simultaneous realization of zero-bias operation and broadband photoresponse from ultraviolet to visible regions. • Enabling accurate classification of incident light wavelengths based on photocurrent features. • Demonstrating a synergistic strategy that integrates material design, device engineering, and data analytics for multifunctional PEC photodetectors. Photocurrent ; Photodetector ; Broadband ; Heterojunction ; Multispectral image ; Photodetection ; Wavelength ; Absorption (acoustics) ; Visible spectrum 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Lei Chen | Tinghong Gao | Yutao Liu | Yong Ma | Yongchao Liang
Source: Physica B Condensed Matter (article, 2025-03-21)
Field: Materials Science — Materials Chemistry

BiLSTM-based complete stress–strain behavior prediction in monolayer graphene under uniaxial stretching: An integrated molecular dynamics study Materials science ; Molecular dynamics ; Monolayer ; Graphene ; Dynamics (music) ; Strain (injury) ; Stress (linguistics) ; Chemical physics ; Composite material ; Condensed matter physics ; Nanotechnology ; Computational chemistry ; Physics ; Chemistry ; Acoustics Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Carbon Nanotubes in Composites
Authors: Giulia Fischetti | Nicolas Schmid | Simon Bruderer | Björn Heitmann | Andreas Henrici | Alessandro Scarso | Guido Caldarelli | D. Wilhelm
Source: Journal of Magnetic Resonance (article, 2025-02-16)
Field: Chemistry — Spectroscopy

A deep learning framework for multiplet splitting classification in 1H NMR One-dimensional <sup>1</sup>H Nuclear Magnetic Resonance (NMR) stands out as the quickest and simplest among various NMR experimental setups. Unfortunately, it suffers from lengthy annotation times and does not always have a clear and unique interpretation. From NMR discovery, efforts have been dedicated to introducing an automated approach to streamline the characterization of chemical compounds while ensuring consistency of the results across the scientific community. Nonetheless, this remains an ongoing challenge that has garnered renewed interest with the emergence of deep learning techniques. Here, we present MuSe Net, a novel supervised probabilistic deep learning framework that can emulate the tasks performed by an expert spectroscopist in annotating one-dimensional NMR spectra generated by small molecules. Considering only the spectrum, MuSe Net detects and classifies multiplets with up to four coupling constants for their splitting phenotype, providing a segmentation of the spectral range. We exploit uncertainty quantification to produce a confidence score to both assess classification reliability and to detect signals that do not fit into any other phenotype class. The results of the evaluation against 48 experimental <sup>1</sup>H NMR spectra of small molecules annotated by experts demonstrate that MuSe Net can deal with anomalies and unclear signals while correctly classifying multiplets and detecting overlapping peaks. Multiplet ; Nuclear magnetic resonance ; Chemistry ; Computational chemistry ; Materials science ; Chemical physics ; Physics ; Spectral line ; Quantum mechanics Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Molecular spectroscopy and chirality
Authors: Yonghui Gong | Kai Yong | Weiling Zhu | Chunfeng Shao | Chun‐Guey Wu | Mengmeng Sun | Lan Zhang | Wei Wang | Gehong Su | Yanying Wang | Jianshan Ye | Tao Liu | Hanbing Rao | Zhiwei Lu
Source: Electrochimica Acta (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

Vacancy mediated CdZnS/SnIn4S8 heterojunction coupling Co3O4/CuO nanozymes: Artificial intelligence assisted ratiometric electrochemiluminescence sensing of tetracycline Electrochemiluminescence ; Heterojunction ; Optoelectronics ; Chemistry ; Materials science ; Coupling (piping) ; Photochemistry ; Nanotechnology ; Physical chemistry ; Electrode ; Metallurgy Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Edgar Martín-Hernández | Borja Hernández | Aurora del Carmen Munguía-López | Sidney Omelon
Source: Current Opinion in Chemical Engineering (article, 2025-11-29)
Field: Materials Science — Materials Chemistry

Artificial intelligence and machine learning for process and policy design in the transition towards circular economy systems: advancements and opportunities Novel computational techniques raised under the concept of artificial intelligence have vast applications in science and engineering. In this work, we review the most relevant frameworks and applications of artificial intelligence and machine learning oriented to the development of sustainable production and consumption systems using a bottom-up multi-scale approach. Firstly, we address frameworks for molecular and processing unit design and flowsheet design. Secondly, we assess methods proposed for the environmental and social assessment of superstructures. Finally, we also discuss the contributions and applications of artificial intelligence in the development of policies that support the shift of paradigm to the circular economy. Process (computing) ; Circular economy ; Sustainable development ; Applications of artificial intelligence ; Consumption (sociology) ; Hyper-heuristic ; Marketing and artificial intelligence Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Sustainable Industrial Ecology
Authors: Rauf Khan | Nadeem Baig | Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬ | Hassan Younas | Mohammad Al-Suwaiyan
Source: Langmuir (article, 2025-08-28)
Field: Materials Science — Materials Chemistry

Self-Assembled Vanadium Carbide MXene-Modified Ceramic Membranes with High Permeability for Efficient Oil-in-Water Emulsion Separation Developing high-performance membranes for efficient oil-water emulsion separation is essential for advancing sustainable and cost-effective wastewater treatment technologies. MXenes, as emerging two-dimensional materials, offer distinct advantages due to their inherent hydrophilicity, chemical stability, and mechanical durability. In this work, ceramic membranes were surface-modified with vanadium carbide (V<sub>2</sub>C) MXene, a relatively underexplored member of the MXene family, and systematically characterized and evaluated for oil-in-water emulsion separation. Membranes were fabricated with varying V<sub>2</sub>C loadings (2, 4, and 6 mg), and their performance was assessed. The membrane modified with 6 mg of V<sub>2</sub>C loading exhibited outstanding separation efficiency, achieving greater than 99% oil rejection and a flux recovery ratio (FRR) exceeding 90%. The optimized membrane also demonstrated underwater superoleophobicity with an underwater-oil contact angle (UWOCA) of 153°, indicating strong resistance to oil fouling. Moreover, it maintained a stable performance across a broad pH range and varying oil concentrations. These findings highlight the potential of V<sub>2</sub>C MXene-modified ceramic membranes for robust oil-water separation. Future work will focus on incorporating predictive machine learning models to optimize the membrane performance and accelerate scale-up. Emulsion ; Vanadium ; Membrane ; Ceramic ; Chemical engineering ; Materials science ; Vanadium carbide ; Permeability (electromagnetism) ; Chemistry ; Chromatography ; Organic chemistry ; Composite material ; Metallurgy ; Engineering MXene and MAX Phase Materials ; Dendrimers and Hyperbranched Polymers ; Advanced Photocatalysis Techniques
Authors: Shinill Kang | Cheol Huh | Choong‐Ki Kim | Meang-Ik Cho | Hyuek-Jin Choi
Source: Journal of Marine Science and Engineering (article, 2025-04-16)
Field: Chemistry — Analytical Chemistry

Feasibility, Advantages, and Limitations of Machine Learning for Identifying Spilled Oil in Offshore Conditions A rapid identification of oil would facilitate a prompt response and efficient removal in the event of an oil spill. Traditional chemical methods in oil fingerprinting have limitations in terms of both time and cost. This study considers machine learning models that can be applied immediately upon measurement of oil density and viscosity. The main objective was to compare models generated from various combinations of features and data. Under five different algorithms, the resulting models were evaluated in terms of their feasibility, advantages, and limitations (FAL). The extra tree (ET) and histogram-based gradient boosting (HGB) models, which incorporated physical features, their rates of change, and environmental features, were found to be the most accurate, achieving 88.55% and 88.41% accuracy, respectively. The accuracy of the models was further enhanced by adjusting the features. In particular, incorporating the rate of change in oil properties led to an enhancement in the accuracy of ET to 92.83%. However, further inclusion of secondary features led to a reduction in accuracy. The effect of input imprecision was analyzed. A 10% of inherent error reduced the accuracy of the HGB model to 60%. Comparing these FAL, machine learning can be a simple, rapid, and cost-effective auxiliary for forensic analysis in diverse spill environments. Submarine pipeline ; Petroleum engineering ; Environmental science ; Oil spill ; Computer science ; Engineering ; Geology ; Oceanography Petroleum Processing and Analysis ; Oil Spill Detection and Mitigation ; Hydrocarbon exploration and reservoir analysis
Authors: Zhenan Zhou | Chuyan Zhang | Kun Zhang | Yu Deng | Guo Chen | Yinan Lin | Xinzhe Yu
Source: Engineering Applications of Artificial Intelligence (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

A composite insulator aging level classification method based on fourier transform infrared spectroscopy and deep learning model Computer science ; Fourier transform infrared spectroscopy ; Fourier transform ; Infrared ; Fourier transform spectroscopy ; Composite number ; Spectroscopy ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Algorithm ; Optics ; Physics ; Mathematics ; Mathematical analysis High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Industrial Vision Systems and Defect Detection
Authors: Rania Jradi | Christophe Marvillet | Mohamed Razak Jeday
Source: Energy (article, 2025-01-01)
Field: Materials Science — Biomaterials

Robust intelligent paradigms for estimating fouling in phosphoric acid / steam heat exchanger Fouling ; Heat exchanger ; Phosphoric acid ; Process engineering ; Waste management ; Environmental science ; Chemistry ; Nuclear engineering ; Materials science ; Chemical engineering ; Engineering ; Mechanical engineering ; Organic chemistry ; Biochemistry Calcium Carbonate Crystallization and Inhibition ; Fault Detection and Control Systems ; Neural Networks and Applications
Authors: David Immel | Ralf Drautz | Godehard Sutmann
Source: The Journal of Chemical Physics (article, 2025-03-20)
Field: Materials Science — Materials Chemistry

Adaptive-precision potentials for large-scale atomistic simulations Large-scale atomistic simulations rely on interatomic potentials, providing an efficient representation of atomic energies and forces. Modern machine-learning (ML) potentials provide the most precise representation compared to electronic structure calculations, while traditional potentials provide a less precise but computationally much faster representation and, thus, allow simulations of larger systems. We present a method to combine a traditional and a ML potential into a multi-resolution description, leading to an adaptive-precision potential with an optimum of performance and precision in large, complex atomistic systems. The required precision is determined per atom by a local structure analysis and updated automatically during simulation. We use copper as demonstrator material with an embedded atom model as classical force field and an atomic cluster expansion (ACE) as ML potential, but, in principle, a broader class of potential combinations can be coupled by this method. The approach is developed for the molecular-dynamics simulator LAMMPS and includes a load-balancer to prevent problems due to the atom dependent force-calculation times, which makes it suitable for large-scale atomistic simulations. The developed adaptive-precision copper potential represents the ACE-forces with a precision of 10 me V/Å and the ACE-energy exactly for the precisely calculated atoms in a nanoindentation of 4 × 106 atoms calculated for 100 ps and shows a speedup of 11.3 compared with a full ACE simulation. Molecular dynamics ; Representation (politics) ; Embedded atom model ; Force field (fiction) ; Atom (system on chip) ; Interatomic potential ; Computer science ; Statistical physics ; Scale (ratio) ; Speedup ; Potential energy ; Computational science ; Algorithm ; Physics ; Chemistry ; Computational chemistry ; Classical mechanics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Wenyang Jia | Alessandro Ferragina | Saskia M. van Ruth | Anastasios Koidis
Source: Journal of Food Composition and Analysis (article, 2025-03-08)
Field: Chemistry — Analytical Chemistry

Robustness of hyperspectral imaging in food analysis of thin surfaces: A case study using thin HAM products Hyperspectral Imaging (HSI) is an advanced remote sensing tool for rapid, non-destructive food analysis, where spectral information from a large area is acquired with applications for products with heterogeneous surfaces and distribution that are traditionally analysed with difficulty. The robustness of HSI acquired data, however, requires comprehensive exploration in the case of very thin food materials. This paper aims to examine the robustness of HSI measurements for thinly sliced food products using ham products as a model system. Three replicates of 1, 2, 3, 4, and 5 mm slices of a Spanish ham were analysed against two types of background using two HSI-NIR camera sensors (400 - 1000 and 900 - 1700 nm), in both their original form (3 - dimensional) and in their average spectra and pixel spectra (2 - dimensional). Several methods were used to analyse and visualise the HSI data structures: the average spectra were undergone statistical analysis for standard derivation (SD) value; Hyperspectral Imaging - Root Mean Square (HSI - RMS) values were calculated using pixel spectra to reveal the internal differences based on the state of the sample; Principal Component Analysis (PCA) and Self - Organising Map (SOM) were applied to the 3D form. When focusing on the average or pixel spectra, low-quality HSI data (thin ham samples) have broader variation of SD value for average spectra and HSI - RMS values for pixel spectra, showing that the HSI sensors simultaneously capture the information of the background together with the sample. Conversely, both SOM and PCA are a useful approach for maintaining the information of the original form, which allow further machine learning approaches and automation. Overall, this study shows the effectiveness of HSI in capturing chemical information of thin food surfaces while exploring measurement limitations related to the penetration depth and the background which can serve as a feasibility study on how to obtain more robust HSI data in the future. • Hyperspectral Imaging effectively analyses thin sample with heterogeneous surfaces. • Thin samples show more variation in SD and HSI-RMS values under HSI scanning. • PCA and SOM maintain original data form, aiding machine learning and automation. Hyperspectral imaging ; Robustness (evolution) ; Food products ; Remote sensing ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Chemistry ; Food science ; Geography ; Biochemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote-Sensing Image Classification
Authors: Jonathan T. Willman | Romain Perriot | Christopher Ticknor
Source: The Journal of Chemical Physics (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Accurate and efficient parameterization of an atomic cluster expansion (ACE) potential for ammonia under extreme conditions We present a machine learning interatomic potential for ammonia designed to capture its complex multiphase behavior, including both molecular and superionic phases. The potential is based on the atomic cluster expansion (ACE) formulation and has been parameterized to facilitate high-fidelity molecular dynamics simulations of ammonia under extreme conditions, for pressures up to 100 GPa and for temperatures above 500 K and up to 6000 K. A diverse range of configurations was generated through high-quality ab initio molecular dynamics simulations, covering insulating and superionic ice phases, liquid ammonia, molecular nitrogen (N2) and hydrogen (H2), and metastable compounds that form upon dissociation, including NH4+, H3+, N2H4, and N3H. We demonstrate that the ammonia ACE potential accurately reproduces experimental and density functional theory predicted isotherms and Hugoniots. Crucially, the potential is able to capture the intricate phase behavior of ammonia, including the transition from insulating molecular fluid to the superionic phase. This work provides a robust interatomic potential that can be used for large-scale, accurate simulations of ammonia under extreme thermodynamic conditions, offering a powerful tool for investigating its behavior in various phases and applications. Molecular dynamics ; Metastability ; Interatomic potential ; Chemical physics ; Chemistry ; Cluster (spacecraft) ; Nitriding ; Atomic units ; Thermodynamics ; Materials science ; Nitrogen ; Computational chemistry ; Physics Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Inorganic Chemistry and Materials
Authors: F. Paraguay Delgado | Fábio Ruiz Simões | Leeor Kronik | Waldemar Kaiser | David A. Egger
Source: ACS Energy Letters (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Machine-Learning Force Fields Reveal Shallow Electronic States on Dynamic Halide Perovskite Surfaces Previous studies indicated that defects in halide perovskites can generate shallow electronic states, which are crucial for their performance in devices. However, how shallow states persist amid pronounced atomic dynamics on halide perovskite surfaces remains unknown. We reveal that electronic states at surfaces of prototypical CsPbBr3 are energetically distributed at room temperature, akin to well-passivated inorganic semiconductors, despite the presence of undercoordinated atoms and cleaved bonds. Notably, approximately 70% of surface-state energies appear within 0.2 eV of the valence-band edge. Although deep states can still form, they are rarely energetically isolated and are less likely to act as traps. Accelerating first-principles calculations via machine learning, we show that the unique atomic dynamics in halide perovskites render the formation of deep electronic states at their surfaces unlikely. These findings reveal the microscopic mechanism behind the low density of deep states at dynamic halide perovskite surfaces, which is key to their device performance. Halide ; Perovskite (structure) ; Materials science ; Chemical physics ; Nanotechnology ; Engineering physics ; Chemistry ; Physics ; Crystallography ; Inorganic chemistry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides