jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fernando Díaz-González | Camila Rojas-Villalobos | Francisco Issotta | Sofia Reyes-Impellizzeri | Sabrina Hedrich | D. Barrie Johnson | Pedro Temporetti | Raquel Quatrini
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-10-21)
Field: Chemistry — Inorganic Chemistry

Trait-Based Meta-Analysis of Microbial Guilds in the Iron Redox Cycle ABSTRACT Microbial iron redox cycling underpins key biogeochemical processes, yet the functional diversity, ecological roles, and trait architectures of iron-transforming microbes remain poorly synthesized across global environments. Here, we present a systematic review and trait-based meta-analysis of 387 microbial taxa spanning 314 studies and 76 years of research, integrating phenotypic, genomic, and environmental data to define ecologically coherent MIRC guilds. Rather than relying on taxonomy, our framework delineates first-order functional guilds - Fe(III)-reducers, Fe(II)-oxidizers, and dual-capacity Fe oxidizers/reducers - and resolves second-order guilds based on trait syndromes such as acidophily, redox flexibility, or metabolic breadth. Trait profiling revealed that iron-cycling capacities frequently transcend phylogenetic boundaries, with multiple guilds converging in chemically stratified hotspots like hot springs, hydrothermal vents, and acid mine drainages. Dual-capacity Fe oxidizers/reducers (e.g., Acidithiobacillus ferrooxidans, Metallosphaera sedula ) emerged as overlooked mediators of “cryptic” iron cycling, possessing genomic repertoires capable of toggling between oxidative and reductive modes in response to redox oscillations. Hierarchical clustering and kernel density analyses of ecophysiological traits highlighted niche partitioning along key environmental filters, including pH, iron availability, salinity, and temperature. Collectively, this work introduces the Guild Exploitation Pattern as a conceptual lens for understanding iron microbiome assembly, providing a data-driven foundation for predicting microbial contributions to iron cycling under changing environmental conditions. IMPORTANCE Iron redox reactions shape nutrient turnover, contaminant mobility, and primary productivity, yet the microbes driving these processes are often studied in isolation. By integrating decades of data into a trait-based guild framework, we reveal the ecophysiological diversity and niche differentiation of microbial iron redox cycling taxa across environments. Our synthesis exposes major gaps, such as limited trait data for &gt;80% of O/R species, and highlights the need for functional trait surveys to complement metagenomics and cultivation efforts. The guild framework presented here advances predictive microbial ecology by linking metabolic traits with environmental gradients, offering a robust foundation for incorporating iron cycling into ecosystem models and biogeochemical forecasts. Guild ; Niche ; Redox ; Metagenomics ; Microbial ecology ; Ecological niche ; Biogeochemical cycle Radioactive element chemistry and processing ; Microbial Fuel Cells and Bioremediation
Authors: Han Wang | Jiaqing Kuang | Jun Shao
Source:  (article, 2025-04-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Core Loss Modeling of Magnetics With the rapid advancement of power electronics, the application of magnetic components is expanding, making core loss a critical factor in the efficiency of power converters. This study constructs a high-precision core loss prediction model using a data-driven approach, which is highly significant for practical applications. Initially, the paper establishes a classification model for magnetic flux density waveforms, detects outliers with the smooth Z-score method, and extracts waveform shape features. It evaluates the correlation of 13 features through Pearson correlation analysis and selects 7 key features using principal component analysis. A random forest is then employed to build a classification model that successfully identifies 20 sine waves, 44 triangular waves, and 16 trapezoidal waves. Subsequently, features such as temperature, frequency, excitation waveform, and core material are extracted, and their contribution is ranked by a random forest. Finally, 11 features are selected for predicting core loss. The paper also enhances a CNN network and proposes a hybrid network integrating MLP and CNN. By comparing the MSE and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> scores of various models and their prediction effects on the test set, it is found that the prediction accuracy of the improved model is significantly enhanced. Principal component analysis ; Waveform ; Core (optical fiber) ; Outlier ; Power (physics) ; Component (thermodynamics) ; Pattern recognition (psychology) ; Key (lock) Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Electric Motor Design and Analysis
Authors: Mohsen Nikou | Abdolraouf Samadi‐Maybodi | Kaveh Yasrebi
Source: Arabian Journal for Science and Engineering (article, 2025-05-29)
Field: Chemistry — Organic Chemistry

Modeling process of reduction of 4-nitrophenol pollutant from aqueous medium by Fe3O4@SiO2@Zr-MOF-NH2/Ag nanocatalyst and fuzzy logic Fuzzy logic ; Aqueous solution ; Reduction (mathematics) ; Nitrophenol ; Pollutant ; 4-Nitrophenol ; Process (computing) ; Materials science ; Process engineering ; Chemical engineering ; Chemistry ; Computer science ; Engineering ; Nanotechnology ; Mathematics ; Organic chemistry ; Catalysis ; Nanoparticle ; Artificial intelligence ; Geometry Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Pengcheng Wu | Tao Chen | Manshang Wang | Lei Xing | Xiaobo Zou | Haoran Li
Source: Analytical Methods (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Hierarchical clustering and optimal interval combination (HCIC): a knowledge-guided strategy for consistent and interpretable spectral variable interval selection Variable selection is crucial for the accuracy of spectral analysis and is typically formulated as an optimization problem using regression techniques. However, these data-driven methods may overlook physical laws or mechanisms, leading to the deselection of physically relevant variables. To address this, we propose a hierarchical clustering and optimal interval combination (HCIC) strategy, guided by domain knowledge, in which physical principles and mechanisms inform algorithm design to capture more physically relevant feature structures. In the first step, spectral variable hierarchical clustering (SVHC) is employed to determine correlations between adjacent variables, generating non-uniform intervals. Each interval corresponds to distinct patterns that reflect underlying molecular interactions, such as peak shifts, functional group contributions, and even non-reaction background signals. Secondly, a Bayesian linear regression-based optimal interval combination (BLR-OIC) strategy is applied to identify the most effective interval combinations, capturing and exploiting the synergistic effects among functional bands or functional groups. We conduct extensive experiments on publicly available and proprietary databases to validate the efficacy of the proposed algorithm. The results demonstrate not only improved predictive performance compared to benchmarks but also greater interpretability and consistent variable selection. Feature selection ; Variable (mathematics) ; Interval (graph theory) ; Selection (genetic algorithm) ; Cluster analysis ; Computer science ; Credible interval ; Feature (linguistics) ; Artificial intelligence ; Pattern recognition (psychology) ; Bayesian probability ; Hierarchical clustering ; Machine learning ; Mathematics ; Data mining Spectroscopy and Chemometric Analyses ; Neural Networks and Applications ; Fault Detection and Control Systems
Authors: Aishwaryo Ghosh | Amitava Moitra | Tanusri Saha‐Dasgupta
Source: Journal of Physics Materials (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Ab-initio trained machine learning potential for MAX compound Ti<sub>2</sub>AlC: Construction, validation, and study of non linear elasticity Abstract One of the intriguing features exhibited by the layered MAX phase compounds, is the non linear elastic behaviour. Since the experimental observation of this curious behaviour, the underlying micro-mechanism has been discussed to interpret experimental observations. However, the theoretical investigation remained a challenge due to the associated length and time scales of the phenomena. In the present work, we adopt a data driven approach to develop a machine learned interatomic potential for the MAX compound Ti$_2$AlC following the Moment Tensor Potential protocol. The constructed potential is validated in lattice constant, formation energy, elastic constant, and stacking fault energies. Finally, applying machine learned potential in classical molecular dynamics provides a faithful&amp;#xD;representation of the experimentally observed non linear elasticity for Ti$_2$AlC. The generated atomic configurations confirm the proposal of formation of ripplocations which allow atomic layers to glide relative to each other without breaking the in-plane bonds. We find common defects, like Al vacancy, strongly influence the hysteresis properties of the stress-strain curve, paving the route to defect-engineered non linear elasticity. Ab initio ; Elasticity (physics) ; Materials science ; Chemistry ; Composite material MXene and MAX Phase Materials ; Aluminum Alloys Composites Properties ; Boron and Carbon Nanomaterials Research
Authors: Yuwei Zhou | Yongxin Lu | Xiaoyong Zhang | Pengjun Wen | Ronghao Li | Shiqing Wang | Yan Xu | Zhikang Shen
Source: Materials Testing (article, 2025-04-09)
Field: Materials Science — Materials Chemistry

Finite element simulation of natural gas pipelines in service welding repair Abstract This paper mainly studies the welding of annular fillet weld using carbon dioxide gas shielded welding and interrupted pulsed arc welding (IPAW) method, using ABAQUS finite element technology to simulate the in-service welding process of X80 pipeline, and comparatively analyze the carbon dioxide gas shielded welding and IPAW of the priming layer, the overall weld temperature field and the distribution of stress field. The distribution patterns of the priming layer, overall welding temperature field and stress field of carbon dioxide gas shielded welding and IPAW were comparatively analyzed. The results indicate that, in addition to the conventional carbon dioxide gas shielded welding method, improved pulse arc welding (IPAW) is also suitable for welding repair. IPAW can significantly mitigate the adverse effects on the base material during welding. It demonstrates superior performance in controlling thermal effects and stress concentration within the weld zone, resulting in a smaller heat-affected zone compared to that produced by carbon dioxide gas shielded welding, thereby effectively reducing overall residual stress. Through finite element simulation, it is possible to replicate in-service welding conditions, optimize welding process parameters, control heat input, monitor welding residual stress, and provide data-driven support for actual repair welding, ultimately enhancing repair quality. Welding ; Finite element method ; Natural gas ; Pipeline transport ; Service (business) ; Mechanical engineering ; Engineering ; Structural engineering ; Business ; Waste management Material Properties and Failure Mechanisms ; Welding Techniques and Residual Stresses ; Engineering and Environmental Studies
Authors: Samuele Giuli | Hasanat Hasan | B. Kloss | Marius S. Frank | Tsung-Han Lee | Olivier Gingras | Yong-Xin Yao | Nicola Lanatà
Source: arXiv (Cornell University) (preprint, 2025-12-25)
Field: Materials Science — Materials Chemistry

Linear Foundation Model for Quantum Embedding: Data-Driven Compression of the Ghost Gutzwiller Variational Space Simulations of quantum matter rely mainly on Kohn-Sham density functional theory (DFT), which often fails for strongly correlated systems. Quantum embedding (QE) theories address this limitation by mapping the system onto an auxiliary embedding Hamiltonian (EH) describing fragment-environment interactions, but the EH is typically large and its iterative solution is the primary computational bottleneck. We introduce a linear foundation model for QE that utilizes principal component analysis (PCA) to compress the space of quantum states needed to solve the EH within a small variational subspace. Using a data-driven active-learning scheme, we learn this subspace from EH ground states and reduce each embedding solve to a deterministic ground-state eigenvalue problem in the reduced space. Within the ghost Gutzwiller approximation (ghost-GA), we show for a three-orbital Hubbard model that a variational space learned on a Bethe lattice is transferable to square and cubic lattices without additional training, while substantially reducing the cost of the EH step. We further validate the approach on plutonium, where a single variational space reproduces the energetics of all six crystalline phases while reducing the cost of the EH solution by orders of magnitude. This provides a practical route to overcome the main computational bottleneck of QE frameworks, paving the way for high-throughput ab initio simulations of strongly correlated materials at a near-DFT cost. Hamiltonian (control theory) ; Subspace topology ; Embedding ; Quantum ; Eigenvalues and eigenvectors ; Unitary transformation ; Curse of dimensionality ; Space (punctuation) ; Quantum system ; Perturbation theory (quantum mechanics) Machine Learning in Materials Science ; Quantum many-body systems ; Cold Atom Physics and Bose-Einstein Condensates
Authors: Samuele Giuli | Hasanat Hasan | B. Kloss | Marius S. Frank | Tsung-Han Lee | Olivier Gingras | Yong-Xin Yao | Nicola Lanatà
Source: ArXiv.org (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Linear Foundation Model for Quantum Embedding: Data-Driven Compression of the Ghost Gutzwiller Variational Space Simulations of quantum matter rely mainly on Kohn-Sham density functional theory (DFT), which often fails for strongly correlated systems. Quantum embedding (QE) theories address this limitation by mapping the system onto an auxiliary embedding Hamiltonian (EH) describing fragment-environment interactions, but the EH is typically large and its iterative solution is the primary computational bottleneck. We introduce a linear foundation model for QE that utilizes principal component analysis (PCA) to compress the space of quantum states needed to solve the EH within a small variational subspace. Using a data-driven active-learning scheme, we learn this subspace from EH ground states and reduce each embedding solve to a deterministic ground-state eigenvalue problem in the reduced space. Within the ghost Gutzwiller approximation (ghost-GA), we show for a three-orbital Hubbard model that a variational space learned on a Bethe lattice is transferable to square and cubic lattices without additional training, while substantially reducing the cost of the EH step. We further validate the approach on plutonium, where a single variational space reproduces the energetics of all six crystalline phases while reducing the cost of the EH solution by orders of magnitude. This provides a practical route to overcome the main computational bottleneck of QE frameworks, paving the way for high-throughput ab initio simulations of strongly correlated materials at a near-DFT cost. Hamiltonian (control theory) ; Subspace topology ; Embedding ; Quantum ; Eigenvalues and eigenvectors ; Unitary transformation ; Curse of dimensionality ; Space (punctuation) ; Quantum system ; Perturbation theory (quantum mechanics) Machine Learning in Materials Science ; Quantum many-body systems ; Cold Atom Physics and Bose-Einstein Condensates
Authors: Jan Vincent
M. Madayag (22882140) | Marcel Roy B. Domalanta (18607144) | Sri Teja Garapati (22882143) | Siavash Mansouri (4094896) | Reymark D. Maalihan (21272663) | Mohiuddin Quadir (1810738) | Eugene B. Caldona (12316453)
Source: Figshare (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Fluoropolymer Coatingswith Inhibitor-Laden Zinc OxideNanoparticles: Electrochemical Characterization and Monte Carlo Simulation In this study, we developed coatings of varying concentrations (2, 4, and 6 wt %) from zinc oxide (ZnO) nanoparticles coated with a layered phytic acid shell dispersed in poly(vinylidene fluoride-<i>co</i>-hexafluoropropylene) (PVDF-HFP) matrix. Comprehensive electrochemical, mechanical, thermal, and microscopic investigations were performed. Results from impedance measurement revealed that the coating with 6 wt % inhibitor-loaded ZnO had higher impedance and charge-transfer resistance than those with lower concentrations, indicating better corrosion resistance. Moreover, improved corrosion resistance was attributed to the passive barrier properties of PVDF-HFP and active corrosion inhibition via PO<sub>4</sub><sup>3–</sup> ion released from the ZnO nanoparticles, as evidenced by spectroscopy and electrochemical results, whereas the 4 wt % formulation showed the best mechanical attributes, including surface hardness, adhesion strength, and tensile properties, due to uniform nanoparticle dispersion and interfacial interactions within the layered shell. Further, microscopy results showed enhanced nanoparticle dispersion, surface defects, and interfacial interactions. Thermal and mechanical analyses revealed enhanced thermal stability and segmental rigidity, indicative of stronger polymer–filler interactions within the coatings. Impedance trends at intermediate, untested nanofiller concentrations were predicted by propagating experimental uncertainty between measured data points using a Monte-Carlo-based stochastic interpolation methodology. This experimental and data-driven interpolation approach showed the coatings’ multifunctional protective action and rationally supports screening formulations for corrosion prevention. Dielectric spectroscopy ; Corrosion ; Oxide ; Nanoparticle ; Fluoropolymer ; Coating ; Zinc ; Thermal stability Corrosion Behavior and Inhibition ; Advanced Sensor and Energy Harvesting Materials ; Surface Modification and Superhydrophobicity
Authors: Nicolás Hernández | Yoonsun Choi | Tom Fearn
Source: Chemometrics and Intelligent Laboratory Systems (article, 2025-10-10)
Field: Chemistry — Analytical Chemistry

Bayesian optimization for interval selection in PLS models We propose a novel Bayesian optimisation framework for interval selection in Partial Least Squares (PLS) regression. Unlike traditional iPLS variants that rely on fixed or grid-based intervals, our approach adaptively searches over the discrete space of interval positions of a pre-defined width using a Gaussian Process surrogate model and an acquisition function. This enables the selection of one or more informative spectral regions without exhaustive enumeration or manual tuning. Through synthetic and real-world spectroscopic datasets, we demonstrate that the proposed method consistently identifies chemically relevant intervals, reduces model complexity, and improves predictive accuracy compared to full-spectrum PLS and stepwise interval selection techniques. A Monte Carlo study further confirms the robustness and convergence of the algorithm across varying signal complexities and uncertainty levels. This flexible, data-efficient approach offers an interpretable and computationally scalable alternative for chemometric applications. • A Bayesian optimisation framework is introduced for adaptive interval selection in PLS. • The method replaces exhaustive grid search with an efficient, data-driven approach. • Simultaneously selects single or multiple intervals to improve model performance. • Achieves competitive or superior performance compared to full-spectrum and stepwise iPLS models on real-world NIR datasets. • A Monte Carlo study confirms the algorithm’s robustness and efficient convergence. Robustness (evolution) ; Monte Carlo method ; Bayesian probability ; Model selection ; Gaussian process ; Interval (graph theory) ; Markov chain Monte Carlo ; Selection (genetic algorithm) ; Bayesian optimization Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Water Quality Monitoring and Analysis
Authors: William F. Simonds
Source: American Journal of Advanced Biomedical Studies (article, 2025-01-01)
Field: Materials Science — Biomaterials

Nanoparticle-Mediated Drug Delivery Systems: A Next-Generation Approach for Targeted Cancer Therapy Nanoparticle-mediated drug delivery systems (NMDDS) represent a groundbreaking advancement in the domain of targeted cancer therapy. The cardinal advantage of nanoparticles lies in their ability to localize therapeutic agents directly to the tumor microenvironment while minimizing exposure to healthy tissues. This precision not only enhances the cytotoxic impact on malignant cells but also significantly decreases the systemic toxicity typically associated with conventional chemotherapy. Recent innovations have led to an array of nanocarriers such as liposomes, polymeric nanoparticles, dendrimers, solid lipid nanoparticles and metallic nanoparticles, all of which differ in their physicochemical properties and mechanisms of drug release. These nanocarriers facilitate controlled release, enhanced permeability, and retention (EPR) effects and enable functionalization for active targeting using ligands, antibodies, or peptides. Consequently, the therapeutic index of anti-cancer drugs is markedly improved. This paper aims to review current clinical status, evaluate effectiveness, examine patient perspectives, analyze clinician responses, and provide data-driven insights regarding the utility of NMDDS in modern oncology practice. Through empirical case analysis and questionnaire-based data assessment, the paper explores the practical application, benefits, challenges, and future potential of nanomedicine in cancer treatment. Our results suggest that nanoparticle-based drug delivery substantially improves patient outcomes, reduces frequency of hospital visits, and offers promising avenues for personalized medicine. However, issues related to large-scale manufacturing, regulatory approval, and long-term biocompatibility must be carefully addressed. With continuous interdisciplinary research and collaborative clinical efforts, NMDDS are poised to transform the landscape of cancer therapy and become a cornerstone in next-generation medical interventions. Nanocarriers ; Nanomedicine ; Drug delivery ; Cancer therapy ; Targeted drug delivery ; Drug ; Cancer treatment ; Cancer ; Targeted therapy Nanoparticle-Based Drug Delivery ; Cancer Research and Treatment ; RNA Interference and Gene Delivery