jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yijie Zhang | Luzhe Huang | Nir Pillar | Yuzhu Li | Yuhang Li | Lukasz G. Migas | Raf Van de Plas | Jeffrey M. Spraggins | Aydogan Özcan
Source: Science Advances (article, 2025-08-01)
Field: Chemistry — Spectroscopy

Virtual staining of label-free tissue in imaging mass spectrometry Imaging mass spectrometry (IMS) is a powerful tool for untargeted, highly multiplexed molecular mapping of tissue in biomedical research. IMS offers a means of mapping the spatial distributions of molecular species in biological tissue with unparalleled chemical specificity and sensitivity. However, most IMS platforms are not able to achieve microscopy-level spatial resolution and lack cellular morphological contrast, necessitating subsequent histochemical staining, microscopic imaging and advanced image registration steps to enable molecular distributions to be linked to specific tissue features and cell types. Here, we present a virtual histological staining approach that enhances spatial resolution and digitally introduces cellular morphological contrast into mass spectrometry images of label-free human tissue using a diffusion model. Blind testing on human kidney tissue demonstrated that the virtually stained images of label-free samples closely match their histochemically stained counterparts (with Periodic Acid-Schiff staining), showing high concordance in identifying key renal pathology structures despite utilizing IMS data with 10-fold larger pixel size. Additionally, our approach employs an optimized noise sampling technique during the diffusion model's inference process to reduce variance in the generated images, yielding reliable and repeatable virtual staining. We believe this virtual staining method will significantly expand the applicability of IMS in life sciences and open new avenues for mass spectrometry-based biomedical research. Staining ; Mass spectrometry imaging ; MALDI imaging ; Microscopy ; Pathology ; Stain ; Computer science ; Resolution (logic) ; Mass spectrometry ; Biomedical engineering ; Chemistry ; Artificial intelligence ; Medicine ; Matrix-assisted laser desorption/ionization ; Chromatography Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Cameron J. Nickerson | Erin R. Johnson
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Assessment of a foundational machine-learned potential for energy ranking of molecular crystal polymorphs First-principles crystal structure prediction (CSP) of isolable polymorphs of organic compounds is a grand challenge in computational chemistry. The adoption of dispersion-correction density-functional theory (DFT) has allowed great strides to be made in the accuracy of the final energy ranking of candidate crystal structures. Consequently, CSP methods are seeing increasing use in development of new pharmaceuticals, organic electronics, energetic materials, and pigments, among other applications. However, lower-cost methods, such as classical force-field potentials, are still necessary for the early stages of CSP, where hundreds of thousands of candidates are commonly generated. Recently developed foundational machine-learned potentials represent a seductive alternative to force fields for this purpose due to their promise of near-DFT accuracy at a vastly reduced computational cost. In this work, the performance of the MACE-OFF23(M) machine-learned potential is assessed for geometry optimisation and energy ranking of candidate crystal structures of 28 compounds from the first seven CSP blind tests, as well as 12 helicene compounds. The performance of MACE-OFF23(M) is found to be highly dependent on the particular compound, providing good accuracy for compounds similar to those in its training set, but failing dramatically for compounds containing unusual functional groups (such as diazo) and organic salts. Physically motivated inclusion of long-range electrostatic interactions remains an open problem for development of foundational machine-learned potentials. Ranking (information retrieval) ; Crystal structure prediction ; Crystal (programming language) ; Energy (signal processing) ; Artificial intelligence ; Computer science ; Chemistry ; Machine learning ; Physics ; Crystal structure ; Crystallography ; Quantum mechanics Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Emma Hernández-Suárez | Adrian Rodriguez | Ámbar Pérez-García | Julia Claudia Mirza Rosca | José F. López
Source: IEEE Transactions on Instrumentation and Measurement (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Corrosion Severity Index (CSI) for Spectral Characterization of Corroded Steel and Iron Samples Metals in coastal regions are threatened by corrosion, underscoring the need for precise detection and monitoring methods. Traditional methods often faces limitations in terms of accuracy and applicability under diverse conditions. This study introduces the Corrosion Severity Index (CSI), an innovative spectral index for assessing corrosion in steel and iron structures. Several iron samples were placed in a salt spray chamber to generate different degrees of corrosion. The samples were analysed using hyperspectral cameras covering the visible near-infrared (VNIR) to the shortwave infrared (SWIR) spectrum. A Scale-Invariant Feature Transform (SIFT) registration algorithm was employed to generate the full spectral signatures from 400 nm to 1700 nm for each pixel. The CSI combines four spectral bands (457.50 nm, 791.91 nm, 1305.08 nm, and 1442.60 nm) where a pixel value close to 0 represents the absence of corrosion, whereas a higher value indicates greater severity of corrosion. Based on the average CSI values, samples are classified into Grade A, B, C, or D which indicates the degree of corrosion. CSI demonstrates its ability to detect early-stage corrosion and has been evaluated for robustness across a variety of steel and iron samples in different environmental conditions. In addition, the performance of the CSI is validated by comparing it with the previously published Corrosion Index (CI). CSI demonstrates a higher accurate ability to detect corrosion products and identify the degree of corrosion with a simplified approach. This index allows a balance between accuracy, low computational demands, and usability, providing an optimal solution for early diagnosis and proactive management of corrosion in coastal infrastructures. Corrosion ; Materials science ; VNIR ; Hyperspectral imaging ; Robustness (evolution) ; Metallurgy ; Computer science ; Artificial intelligence ; Chemistry Corrosion Behavior and Inhibition ; Thermography and Photoacoustic Techniques ; Infrastructure Maintenance and Monitoring
Authors: Mehmet Keçeci
Source:  (preprint, 2025-07-03)
Field: Materials Science — Materials Chemistry

The Keçeci Layout: A Cross-Disciplinary Graphical Framework for Structural Analysis of Ordered Systems, Mehmet Keçeci, 2025 The visualization of scientific data is a fundamental step in uncovering hidden patterns and relationships within complex systems. While conventional force-directed graph layout algorithms (e.g., spring layout) are effective at displaying the general topology and clustering tendencies of systems, they possess a critical shortcoming: they are inherently non-deterministic and obscure the natural, sequential, or index-based identities of nodes. This poses a significant limitation when analysing systems that contain an intrinsic sequential logic, such as quantum circuits, metabolic pathways, crystal structures, or chronological data structures. This paper introduces the "Keçeci Layout," a deterministic and order-preserving algorithm designed to fill this analytical gap. The algorithm operates by arranging nodes sequentially along a primary axis according to their IDs, employing a predictable zigzag pattern on a secondary axis to prevent node overlaps. This approach establishes a direct and intuitive bridge between a system's abstract data structure (e.g., qubit indices, reaction steps) and its visual representation. To demonstrate the versatility and efficacy of the Keçeci Layout, four case studies from distinct scientific disciplines are presented: Chemistry, Physics, Biology, Quantum Computing, Materials Science, Molecular Biology, Computer Science, etc. In conclusion, the Keçeci Layout is proven to be a powerful scientific tool that offers analytical depth by remaining faithful to the underlying data structure, moving beyond purely aesthetic presentation. The algorithm provides a significant advantage over conventional methods, particularly for the visual analysis of ordered, hierarchical, or index-dependent systems. Cross disciplinary ; Discipline ; Computer science ; Sociology ; Data science ; Social science Structural mechanics and materials
Authors: Yuting You | Lei Zhang | Zhuo Yu | Daqing Zhao | Xueyuan Bai | Wei Zhang
Source: Frontiers in Chemistry (review, 2025-06-20)
Field: Chemistry — Analytical Chemistry

Progress in the application of hyperspectral imaging technology in quality detection and in the modernization of Chinese herbal medicines Hyperspectral imaging (HSI) technology integrates spectral analysis and image recognition with non-destructive and efficient advantages, and is widely used in the agriculture, geological exploration, military sectors, among others. Traditional Chinese medicine (TCM) has a long history of use in China, and to ensure the quality of TCM herbs, it is necessary to perform accurate quality assessments. It is also crucial to evaluate the active ingredients and changes in cultivation strategies and processing parameters over time. The use of HSI technology for the investigation of Chinese medicines has grown in importance, and recent advances in HSI have enabled the multi-dimensional non-destructive analyses of various components, origins, and growth statuses, thereby providing innovative solutions for modernization. This paper systematically reviews the application of HSI for detecting active ingredients, evaluating their quality, and recognizing the authenticity and species of Chinese herbal medicines. It clearly describes the limitations of hyperspectral technology in terms of data processing, emphasizes the importance of textural information, and suggests the application of HSI for large-scale detection. Hyperspectral imaging ; Quality (philosophy) ; Modernization theory ; China ; Computer science ; Chinese herbs ; Traditional Chinese medicine ; Artificial intelligence ; Data science ; Traditional medicine ; Medicine ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Phytochemistry and Biological Activities
Authors: Nico Unglert | Lívia B. Pártay | Georg K. H. Madsen
Source: Journal of Chemical Theory and Computation (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

Replica Exchange Nested Sampling Nested sampling (NS) has emerged as a powerful tool for exploring thermodynamic properties in materials science. However, its efficiency is often hindered by the limitations of Markov chain Monte Carlo (MCMC) sampling. In strongly multimodal landscapes, MCMC struggles to traverse energy barriers, leading to biased sampling and reduced accuracy. To address this issue, we introduce replica-exchange nested sampling (RENS), a novel enhancement that integrates replica-exchange moves into the NS framework. Inspired by Hamiltonian replica exchange methods, RENS connects independent NS simulations performed under different external conditions, facilitating ergodic sampling and significantly improving computational efficiency. We demonstrate the effectiveness of RENS using four test systems of increasing complexity: a one-dimensional toy system, periodic Lennard-Jones, the two-scale core-softened Jagla model, and a machine-learned interatomic potential for silicon. Our results show that RENS not only accelerates convergence but also allows the effective handling of challenging cases where independent NS fails, thereby expanding the applicability of NS to more realistic material models. Replica ; Markov chain Monte Carlo ; Computer science ; Sampling (signal processing) ; Markov chain ; Monte Carlo method ; Ergodic theory ; Statistical physics ; Traverse ; Computational science ; Theoretical computer science ; Artificial intelligence ; Physics ; Machine learning ; Mathematics ; Statistics Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Phase Equilibria and Thermodynamics
Authors: Yu Zhang | Bing Bai | Gian Albert Alfani | Zheng Zhao | Ben Zhong Tang
Source: Journal of Materials Chemistry C (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Aggregation-induced new functions: structure–property relationships in molecular crystals This review shows how molecular aggregation enables emergent photophysics (RTP, radical emission, NIR luminescence, photothermal, X-ray scintillation) unseen in single molecules. Materials science ; Property (philosophy) ; Chemical physics ; Crystallography Chemical Thermodynamics and Molecular Structure
Authors: Aya K. Elbauomy | Islam N. Fathy | Islam M. Nabil | Ahmed Elgabry | Gehan Hamdy | Mohamed S. Saif
Source: Scientific Reports (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Mechanical, microstructural, and radiation shielding characteristics of sustainable high-strength concrete incorporating recycled wastes blended powders This study investigated the influence of blended powders of dealuminated metakaolin (DK), limestone (LS), and silica fume (SF) as partial cement replacements on the properties of high-strength concrete (HSC). Nine concrete mixes were designed, including a control mix and mixes incorporating binary, ternary, and quaternary blends of SF, LS, and DK at varying cement replacement levels. The experimental program evaluated the physical properties (slump, setting times, consistency), mechanical properties (compressive and tensile strengths), and microstructure (SEM, XRD, and EDX analysis) of the investigated concrete mixes. Furthermore, radiation shielding properties of the produced concretes were assessed using Monte Carlo (MC) simulations and Phy-X software. The analysis covered both γ-rays and fast neutrons. Results showed that quaternary blends of DK, LS, and SF reduced slump due to higher water demand, while their increased content enhanced compressive and tensile strengths. Optimal strength values were achieved with specific blend ratios: mix 4 (10% DK) for binary blends, mix 6 (15% SF+10% DK) for ternary blends, and mix 9 (15% SF+10% LS+10% DK) for quaternary blends. These optimal mixes exhibited compressive strength increases of 37.3%, 43.35%, and 23.4%, and tensile strength increases of 15.3%, 32.4%, and 22.25%, respectively, compared to the control mix. SEM analysis showed fewer voids and microcracks and a denser microstructure in the optimal replacement mixes. Furthermore, XRD and EDX analyses confirmed that DK, LS, and SF promoted the formation of calcium silicate hydrate (CSH) and calcium aluminate hydrate (CAH) through pozzolanic reactions. The γ-ray attenuation tests indicated modest improvement in γ-ray shielding capacity of HSC, with DK10 and SF15LS10DK10 mixes recording the highest linear attenuation coefficients (LAC). This improved performance is attributed to their elevated densities (2.47, 2.45 g cm<sup>-3</sup>) and substantial iron content (1.53% and 1.92%, respectively). Additionally, DK10, SF15LS10, and SF15LS10DK10 mixes exhibited excellent neutron shielding, achieving a removal cross-section (FCS) value of 0.086 cm⁻<sup>1</sup>, with the lowest half value layer (HVL<sub>FCS</sub>) of 8.059 cm, and relaxation length (λ<sub>FCS</sub>) of 11.627 cm. Ultimate tensile strength ; Silica fume ; Microstructure ; Calcium silicate hydrate ; Cement ; Compressive strength ; Metakaolin ; Portland cement ; Pozzolan Radiation Shielding Materials Analysis ; Innovative concrete reinforcement materials ; Concrete and Cement Materials Research
Authors: M.J. Jiménez–Come | Francisco Javier González Gallero | Pascual Álvarez Gómez | Victoria Matres
Source: Materials (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Prediction and Detection of Localised Corrosion Attack of Stainless Steel in Biogas Production: A Machine Learning Classification Approach Biogas contributes to environmental protection by reducing greenhouse gas emissions and promoting the recycling of organic waste. Its utilization plays a crucial role in addressing the challenges of climate change and sustainability. However, the deterioration of process plants involved in biogas production due to corrosion has a critical impact on the safety and durability of their operations. In order to maintain the safety of structures in terms of service life with respect to corrosion, it is essential to develop effective corrosion engineering control methods. Electrochemical techniques have become a useful tool by which to evaluate corrosion resistance. However, these techniques may require microscopic analysis of the material surface and the analysis may be influenced by subjective factors. To solve this drawback, this work proposes the use of SVM models to predict the corrosion status of the material used in biogas production with no need to perform microscopic analysis after the electrochemical test. The obtained results of sensitivity and specificity equal to 0.94 and 0.97, respectively, revealed the utility of the proposed stochastic models to assure the corrosion state of the equipment involved in biogas production. SVM-based models are an effective alternative for accurately evaluating material durability and comparing the corrosion resistance of different materials in biogas environments. This approach facilitates the selection of the most suitable material to achieve greater durability and long-term performance. Synopsis: The results show that the proposed model is a useful tool to predict the behaviour of stainless steel against corrosion according to the environmental conditions to which the material is exposed in biogas production. Corrosion ; Durability ; Biogas ; Process engineering ; Production (economics) ; Corrosion monitoring ; Sustainability ; Environmental science ; Materials science ; Computer science ; Waste management ; Biochemical engineering ; Engineering ; Metallurgy ; Composite material Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Jiaying Li | Peng Lin | Lingwei Wu | Yanan Yue | Guangzhong Ma
Source: Angewandte Chemie International Edition (article, 2025-04-12)
Field: Chemistry — Electrochemistry

Deciphering Complex Electrochemical Reaction Dynamics and Interactions of Single Nanoentities via Evanescent Scattering Microscopy Electrochemical reactions at the nanoscale are governed by intricate surface interactions, yet existing imaging techniques often lack the surface sensitivity and throughput needed to resolve these dynamics clearly. Here, we introduce electrochemical evanescent scattering microscopy (EC-ESM), a high-throughput, surface-sensitive imaging technique that enables real-time tracking of single-nanoentity electrochemistry with high resolution. Using EC-ESM, we monitored the motion and dissolution dynamics of silver nanoparticles and identified a clear relationship between nanoparticle velocity and electron transfer rates. The high throughput of EC-ESM not only ensures statistical reliability but also allows the detection of rare electron transfer events in molecularly modified AgNPs. Additionally, EC-ESM's high resolution enabled direct imaging of both single and interacting silver nanowires, revealing diverse dissolution behaviors that provide insights into structural and surface properties. We envision EC-ESM as a powerful platform for advancing nanoscale electrochemical research and interfacial charge transfer studies. Nano- ; Dynamics (music) ; Nanotechnology ; Microscopy ; Electrochemistry ; Materials science ; Scattering ; Scanning electrochemical microscopy ; Chemical physics ; Chemistry ; Optics ; Physics ; Electrode ; Physical chemistry ; Composite material Electrochemical Analysis and Applications ; Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Elyor Berdimurodov | N. Elangovan | Ashish Kumar | Abhinay Thakur | Fotima Sobirova | Khudaybergan Polvonov | Sevara Tojieva | Muzaffar Makhkamov | Ahmad Hosseini–Bandegharaei | I.B. Sapaev
Source: Bioresources and Bioprocessing (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Recent advancements in application of carbohydrate-derived carbon quantum dots in analytical chemistry: a comprehensive update Carbohydrate-derived carbon quantum dots (CDCQDs) have evolved at a rapid rate as green, biocompatible nanomaterial, revolutionizing analytical chemistry with their unique optical and surface properties. Synthesized from different carbohydrate sources-ranging from mono-, di-, and polysaccharides to biomass wastes-CDCQDs offer tunability of fluorescence, low toxicity, and simple functionalization, enabling ultrasensitive detection in chemical, biomedical, environmental, and food safety applications. Recent developments (2021-2025) have reached detection sensitivities of as low as 0.077 µM for antibiotics, 7 nM for glucose, and single-cell sensitivity for pathogens with recovery rates routinely > 95%. Quantum yield (QY) up to 83% and superb photostability also allow them to be included in portable and point-of-care platforms. Notwithstanding such accomplishment, scalability, reproducibility, and integration into devices continue to be issues. Mitigation of these via green synthesis, surface engineering, and smart device coupling is crucial for commercial translation. CDCQDs are thus a synthesis of sustainability, sensitivity, and versatility and are poised to drive next-generation eco-friendly analytical systems for real-world diagnostics and monitoring. Quantum dot ; Carbon quantum dots ; Biocompatible material ; Carbon fibers ; Sensitivity (control systems) ; Quantum yield Carbon and Quantum Dots Applications ; Biosensors and Analytical Detection ; Quantum Dots Synthesis And Properties