jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Edison Barbieri | Pedro Luiz Barbosa | Pinto Leonidio Hanamulamba | Thalassia Giaccone
Source: O Mundo da Saúde (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Toxicological effects of nanoparticles on Plankton: implications for Environmental Health Nanoparticles (NPs) have emerged as ubiquitous contaminants in aquatic environments due to their extensive industrial, biomedical, and agricultural applications. Their small size, high surface reactivity, and potential for toxic ion release confer upon these particles a unique capacity to interact with aquatic biota, particularly planktonic communities, which form the base of aquatic food webs. This review compiles and analyzes recent ecotoxicological findings on the effects of metallic (Ag, ZnO, TiO₂, Cu, Fe₃O₄), polymeric, and composite nanoparticles on zooplankton and phytoplankton, with emphasis on physiological, biochemical, and population-level responses. Evidence indicates that NPs can induce oxidative stress, membrane damage, growth inhibition, reproductive impairment, and metabolic disruptions in species such as Daphnia magna, Ceriodaphnia silvestrii, and Chlorella vulgaris. Ionic dissolution (e.g., Ag⁺, Zn²⁺) and reactive oxygen species (ROS) generation have been identified as primary toxicity pathways, although surface interactions and protein corona formation also modulate their bioavailability and toxicity. Sublethal and chronic exposures often disrupt planktonic community composition and productivity, with potential cascading effects on higher trophic levels and ecosystem stability. Despite advances, significant knowledge gaps persist regarding long-term ecological consequences, toxicity of mixtures with other pollutants, and NP behavior under environmentally realistic conditions. Future research should integrate mechanistic toxicology, nanoinformatics, and ecological modeling to predict environmental fate and impacts of NPs. The synthesis of green nanoparticles and implementation of standardized testing protocols are crucial for risk mitigation and guiding sustainable nanotechnology practices. By elucidating the complex interactions between NPs and planktonic organisms, this study contributes to a broader understanding of nanoparticle-induced perturbations in aquatic ecosystems and their implications for environmental health. Trophic level ; Plankton ; Aquatic ecosystem ; Daphnia ; Ecosystem ; Biomagnification ; Bioavailability ; Aquatic toxicology ; Zooplankton Nanoparticles: synthesis and applications ; Environmental Toxicology and Ecotoxicology ; Heavy metals in environment
Authors: Soundarya T Karamcheti | Gale Brightwell | Phil Bremer | Matthew Schofield
Source: Food Microbiology (review, 2025-01-10)
Field: Chemistry — Analytical Chemistry

Hierarchical Bayesian linear mixed model to estimate variability in the thermal inactivation parameters for Listeria species A total of 476 D-values associated with 76 strains of Listeria spp. in liquid media were obtained from 27 scientific articles. Meta-analysis was carried out using hierarchical Bayesian models to assess variability, predict the D-, and estimate the z<sub>T</sub>-, and z<sub>pH</sub>-values for Listeria spp. across a range of temperatures (55-70 °C) and pH values (3-8 units). Different hypotheses regarding variability between strains or studies in each model correspond to different hierarchical assumptions about the inactivation parameters. The models produced were compared based on their predictive ability, Bayes-R<sup>2</sup> and widely applicable information criterion (WAIC) values. A hierarchical model that considered random effects due to both strain and study effects on the thermal inactivation parameters was determined to be the "best" model and was subsequently used to estimate the posterior distributions for the D-, z<sub>T</sub>-, and z<sub>pH</sub>-values. The variability introduced in the parameters due to differences between studies was higher than that of variability between strains. The parameters estimated using the model for different strains of Listeria species may be applicable for processing aqueous foods such as milk and liquid products such as sauces and gravies across a temperature range of 55-70 °C and pH values of 3-8 units. Listeria ; Bayesian probability ; Listeria monocytogenes ; Generalized linear mixed model ; Mixed model ; Biology ; Mathematics ; Biological system ; Statistics ; Food science ; Computer science ; Environmental science ; Genetics ; Bacteria Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Edible Oils Quality and Analysis
Authors: Mikhail Gorchtein
Source: ArXiv.org (preprint, 2025-01-25)
Field: Chemistry — Spectroscopy

A hitchhiker's guide to nuclear polarization in muonic atoms I consider the so-called nuclear polarization correction to the 1S-levels in light to intermediate muonic atoms. An easy to use recipe to compute it is given. The calculation includes the effect of the nucleon polarization, i.e. the contribution from inelastic states in the hadronic range, and Coulomb corrections beyond the leading logarithm approximation to both nuclear and nucleon polarization. I provide numerical estimates for $4\leq Z\leq41$, compare to the estimates in the literature and discuss the need for future improvements. Polarization (electrochemistry) ; Physics ; Atomic physics ; Nuclear physics ; Political science ; Chemistry Advanced NMR Techniques and Applications ; Nuclear physics research studies ; Crystallography and Radiation Phenomena
Authors: SHAM SUNDAR M
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2025-04-25)
Field: Chemistry — Inorganic Chemistry

A Study on Effectiveness of Multimodal Transportation ABSTARCT: This study explores the export potential and performance of coconut and its by-products, with a focus on global demand, trade dynamics, and value addition processes. Coconut, often referred to asthe "tree of life," offers a wide range of by-products such as coconut oil, desiccated coconut, coir, copra, activated carbon, and coconut water, all of which have seen rising demand in international markets. The research analyzes historical export data, major importing countries, key players in the global market, and government policies that influence trade. Challenges such as price volatility, supply chain inefficiencies, and competition from synthetic alternatives are also examined. The study concludes by suggesting strategies to enhance export competitiveness, including technology adoption, value-added product innovation, and stronger market linkages. This research aims to provide insights for stakeholders to harness the full potential of coconut exports and contribute to sustainable agricultural andeconomicdevelopment Multimodal transport ; Multimodal therapy ; Computer science ; Transport engineering ; Engineering ; Psychology ; Psychotherapist Coconut Research and Applications ; Global trade, sustainability, and social impact ; Innovations and Analysis in Business and Education
Authors: Vasilios Drakopoulos | Alex Reichenbach | Romana Stark | Claire J. Foldi | Philip Jean-Richard-dit-Bressel | Zane B. Andrews
Source: eNeuro (article, 2025-07-29)
Field: Chemistry — Spectroscopy

FiPhoPHA—A Fiber Photometry Python Package for Post Hoc Analysis Fiber photometry is a neuroscience technique that can continuously monitor in vivo fluorescence to assess population neural activity or neuropeptide/transmitter release in freely behaving animals. Despite the widespread adoption of this technique, methods to statistically analyze data in an unbiased, objective, and easily adopted manner are lacking. Various pipelines for data analysis exist, but they are often system specific, are only for preprocessing data, and/or lack usability. Current post hoc statistical approaches involve inadvertently biased user-defined time-binned averages or area under the curve analysis. To date, no post hoc user-friendly tool with few assumptions for a standardized unbiased analysis exists, yet such a tool would improve reproducibility and statistical reliability for all users. Hence, we have developed a user-friendly post hoc statistical analysis package in Python that is easily downloaded and applied to data from any fiber photometry system. This Fiber Photometry Post Hoc Analysis (FiPhoPHA) package incorporates a variety of tools, a downsampler, bootstrapped confidence intervals (CIs) for analyzing peri-event signals between groups and compared with baseline, and permutation tests for comparing peri-event signals across comparison periods. We also include the ability to quickly and efficiently sort the data into mean time bins, if desired. This provides an open-source, user-friendly Python package for unbiased and standardized post hoc statistical analysis to improve reproducibility using data from any fiber photometry system. Python (programming language) ; Computer science ; Usability ; Data mining ; Statistical hypothesis testing ; Statistics ; Operating system ; Mathematics Mass Spectrometry Techniques and Applications ; Adipose Tissue and Metabolism ; Neurotransmitter Receptor Influence on Behavior
Authors: Sylvain Le Tonquesse | Laura Agnarelli | S. Sakthivel
Source: Chemistry of Materials (article, 2025-07-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bayesian Optimization of the Experimental Parameters of Material Synthesis: Application to the Magnesioreduction of Rare-Earth-Free (Mn<sub>1–<i>x</i></sub>Fe<sub><i>x</i></sub>)<sub>5</sub>Si<sub>3</sub> Magnetocalorics Optimizing complex synthesis processes remains a significant challenge in materials science, particularly when high phase purity must be achieved in complex multivariable systems with restricted experimental throughput. Here, we demonstrate the application of a fully experimental Bayesian Optimization (BO) framework to efficiently optimize the synthesis conditions of (Mn1–xFex)5Si3, a rare-earth-free magnetocaloric material. Using magnesioreduction as the synthesis route, seven experimental parameters were iteratively refined via active learning to maximize phase purity. Phase-pure samples, including the challenging peritectoid-forming Fe5Si3, were obtained within five active learning iterations. The approach generalizes to unseen compositions and yields chemically meaningful insights via SHAP analysis of the trained Gaussian Process model, identifying maximum temperature and SiO2 excess as key drivers of phase purity. X-ray diffraction and scanning electron microscopy confirmed high crystallinity, uniform submicron grain size, and accurate compositional control in the optimized samples. Magnetic measurements revealed a near-room-temperature magnetocaloric effect in ferromagnetic Mn1.25Fe3.75Si3, with a peak −ΔSmag of 1.06 J kg–1 K–1 at 275 K under a 2 T magnetic field change. This study establishes BO as a powerful and data-efficient framework for accelerating the synthesis of functional materials from small experimental data sets. Rare earth ; Materials science ; Bayesian optimization ; Earth (classical element) ; Mineralogy ; Crystallography ; Chemistry ; Physics ; Metallurgy ; Mathematics ; Mathematical optimization Magnetic and transport properties of perovskites and related materials ; Magnetic Properties of Alloys ; Thermal Expansion and Ionic Conductivity
Authors: Robert Dürr | Eric Otto | Rudolph Kok | Stefan Hempfling | Stefanie Duvigneau | Achim Kienle | Andreas Bück
Source: Industrial & Engineering Chemistry Research (article, 2025-09-12)
Field: Materials Science — Biomaterials

Surrogate Modeling of Microbial PHA-Biopolymer Synthesis Sophisticated control and optimization of biotechnological processes, such as production of polyhydroxyalkaonate (PHA) biopolymers, require mathematical models, which allow accurate representation of the process dynamics and also exhibit a moderate degree complexity. One possible solution is found in surrogate modeling. Here, simulations of (complex) first-principles process models are used to design suitable model formulations of moderate complexity that can subsequently be used for model-based process control and optimization. In this contribution, dynamic mode decomposition with control (DMDc), including a time-delay embedding is applied to derive surrogate models for a fed-batch and a continuous production of two specific PHAs, namely, poly(3-hydroxybutyrate) (PHB) and bioco-polymer poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), using simulation data of a recently presented highly nonlinear first-principles process model. The obtained surrogates are reduced order discrete-time linear models, which allow for efficient application of established linear control and optimization techniques. Predictive capabilities of the surrogates are evaluated for in silico validation experiments. It is shown that the nonlinear process dynamics can be approximated accurately by the surrogates, and the latter are thereby a valuable tool for subsequent process optimization. Studies for different orders of time-delay embedding indicate that more detailed embeddings yield improved accuracy. This effect becomes less pronounced when further increasing the embeddings complexity. Furthermore, it is also shown for a repeated fed-batch setup that the extrapolative capabilities of the fed-batch surrogate for related but different process setups are limited and can only approximate the nonlinear process behavior qualitatively. Process (computing) ; Nonlinear system ; Embedding ; Surrogate model ; Process control ; Representation (politics) ; Model predictive control ; Process modeling ; Decomposition biodegradable polymer synthesis and properties ; Orthopaedic implants and arthroplasty ; Natural Fiber Reinforced Composites
Authors: Harry Martin | A. Rocio | Niamh Nolan | Jeremy K. Klosterman
Source: Journal of Chemical Education (article, 2025-07-29)
Field: Chemistry — Physical and Theoretical Chemistry

Accessible Molecular Models: A Modular 3D-Printed Space-Filling Atomic Model Set and Active Learning Modules for Introductory Organic Chemistry Molecular models are an invaluable learning aid for developing the representation literacy and visuospatial thinking skills needed for success in organic chemistry. Sadly, space-filling models are no longer readily available. Here, we report a new space-filling atomic model set from which the wealth of organic structures can be rapidly assembled to provide visual and tactile feedback when learning the effects of atomic and molecular size, shape, and resultant steric interactions guiding organic structure and reactivity. The affordable models can be printed on accessible, consumer-grade 3D printers (typically fused deposition modeling (FDM) or stereolithography (SLA) printing processes) on a scale for student use in the classroom. We also report active learning exercises in which students utilized the 3D printed space-filling models to examine molecular conformations and steric interactions in an undergraduate organic chemistry course. Positive student responses and reported self-efficacies highlight the benefits of using physical models in self-guided in-class small group activities. The accessible 3D printed models are scaled at 1 in. = 1 Å for ease of manipulation across a wide spectrum of students and are equally suited for demonstrations of organic structure. Modular design ; Set (abstract data type) ; Space (punctuation) ; Chemical space ; Nanotechnology ; Chemistry ; Computer science ; Materials science ; Programming language ; Drug discovery Various Chemistry Research Topics ; Chemistry and Chemical Engineering
Authors: Wei Zhang | MingJian Tang | Haoxuan Mu | X.-X. Yang | Xiaowei Zeng | Rui Tuo | Wei Chen | Wei Gao
Source: Extreme Mechanics Letters (article, 2025-06-10)
Field: Materials Science — Biomaterials

Inverse design of nonlinear mechanics of bio-inspired materials through interface engineering and Bayesian optimization Interface (matter) ; Nonlinear system ; Bayesian optimization ; Inverse ; Inverse problem ; Mechanical engineering ; Engineering ; Nanotechnology ; Materials science ; Computer science ; Mathematics ; Physics ; Artificial intelligence ; Chemical engineering ; Mathematical analysis ; Geometry Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Additive Manufacturing and 3D Printing Technologies
Authors: Armin Shayesteh Zadeh | Alexander Winton | Joseph M. Palomba | Andrew L. Ferguson
Source: The Journal of Physical Chemistry B (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

High-Throughput Virtual Screening of Protein-Catalyzed Capture Agents for Novel Hydrogel-Nanoparticle Fentanyl Sensors Fast, portable, and reliable detection of chemical and biological compounds is an important challenge in many safety and healthcare applications. Chemical sensors based on photonic crystals that use protein-catalyzed capture (PCC) agents as molecular sensors are promising tools for the portable detection of chemical and biological compounds. We present the development and deployment of a high-throughput virtual screening protocol to computationally identify PCC candidates that maximize the binding sensitivity and selectivity for fentanyl. The approach integrates enhanced sampling molecular dynamics free-energy calculations, Gaussian process regression surrogate models, and Bayesian optimization to efficiently navigate the design space of over 1 million PCC candidates, resolve the sensitivity-selectivity Pareto frontier, and identify the top-performing PCC candidates. We analyze the molecular interactions between our top candidates and fentanyl target to propose design rules for high-performance PCC agents to be used for experimental testing and, ultimately, incorporation into next-generation hydrogel-nanoparticle-based chemical sensing devices. Bayesian optimization ; Virtual screening ; Gaussian process ; Chemical space ; Bayesian probability ; Protocol (science) ; Chemical process ; Sensitivity (control systems) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Proteomics Techniques and Applications
Authors: Qia Ke | Cory M. Simon
Source: Nature Computational Science (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

Guidelines for multi-fidelity Bayesian optimization of molecules and materials Bayesian optimization ; Fidelity ; Bayesian probability ; Computer science ; Nanotechnology ; Computational biology ; Materials science ; Artificial intelligence ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Multi-Objective Optimization Algorithms
Authors: S. Yamazaki | Tomoya Horide | Yutaka Yoshida
Source: ACS Applied Electronic Materials (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Accelerated Process Development of YBa<sub>2</sub>Cu<sub>3</sub>O<sub>7</sub> film with Bayesian Optimization Augmented by Transferred Data Efficient process optimization is a key to the development of YBa2Cu3O7 films, whose properties are influenced by many factors such as substrate, nanostructure, and composition. The simplest optimization is exhaustive search, which requires many experiments. Bayesian optimization is more efficient but requires initial data. Here we propose the Bayesian optimization augmented by domain-aligned sampling, data transfer, and peak screening (transfer-based Bayesian optimization), which efficiently acquires the initial data. In this method, the preliminary data in a system with a consistent process nature is used as the initial data. The preliminary data of the critical current density (Jc) were obtained in the YBa2Cu3O7/SrTiO3 by exhaustive search, and the Jc of YBa2Cu3O7/LaAlO3 was optimized. A Gaussian process regression model was constructed from the preliminary data of YBa2Cu3O7/SrTiO3 and the domain-aligned sampling data of the YBa2Cu3O7/LaAlO3. The initial local maxima were included as candidates for the global maximum in the peak screening to avoid overlooking potentially high Jc. The transfer-based Bayesian optimization found an optimal Jc as high as that by the exhaustive search with a smaller number of iterations. Thus, the optimal property was obtained efficiently by transfer-based Bayesian optimization, making the optimization of coated conductors and advanced materials more efficient. Bayesian optimization ; Bayesian probability ; Process (computing) ; Materials science ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Reservoir Engineering and Simulation Methods ; Machine Learning and Algorithms
Authors: Derick E. Ober | Sesha Sai Behara | Anton Van der Ven
Source: Computational Materials Science (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Bayesian prior construction for uncertainty quantification in first-principles statistical mechanics Uncertainty quantification ; Hyperparameter ; Bayesian probability ; Bayesian inference ; Cluster expansion ; Monte Carlo method ; Prior probability ; Surrogate model ; Cluster (spacecraft) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Thermal Expansion and Ionic Conductivity
Authors: Zong-Xiu Tsai | Wei-Che Chang | Huitao Yu | Chin‐Wen Chen | Chuin‐Shan Chen
Source: Polymer (article, 2025-11-24)
Field: Materials Science — Polymers and Plastics

Molecular-level investigations in Polyetherimide during uniaxial tensile tests: A coarse-grained molecular dynamics study Polyetherimide ; Force field (fiction) ; Molecular dynamics ; Ultimate tensile strength ; Deformation (meteorology) ; Quantum entanglement ; Softening ; Thermal expansion Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies ; Composite Material Mechanics
Authors: Jennie Martin | Michele Ceriotti | Graeme M. Day
Source: Crystal Growth & Design (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

An Adapted Similarity Kernel and Generalized Convex Hull for Molecular Crystal Structure Prediction We adapted an existing approach to identifying stabilizable crystal structures from prediction setsthe Generalized Convex Hull (GCH)to improve its application to molecular crystal structures. This was achieved by modifying the Smooth Overlap of Atomic Positions (SOAP) kernel to define the similarity of molecular crystal structures in a more physically motivated way. The use of the adapted similarity kernel was assessed for several organic molecular crystal landscapes, demonstrating improved interpretability of the resulting machine-learned descriptors. We also demonstrate that the adapted kernel results in improved performance in predicting lattice energies using Gaussian process regression. Our overall findings highlight a sensitivity of similarity kernel-based landscape analysis methods to kernel construction, which should be considered when applying these methods. Kernel (algebra) ; Similarity (geometry) ; Crystal structure prediction ; Interpretability ; Convex hull ; Crystal structure ; Gaussian ; Kernel method Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Chasheng He | Guihua Luo | Hongliang Duan | Yuanyuan Xie | Guijun Zhang | An Su | Weike Su
Source: Molecular Catalysis (article, 2025-02-04)
Field: Chemistry — Inorganic Chemistry

Optimizing Phosphine Ligands for Ruthenium Catalysts in Asymmetric Hydrogenation of β-Keto Esters: The Role of Water in Activity and Selectivity Ruthenium ; Phosphine ; Selectivity ; Catalysis ; Chemistry ; Asymmetric hydrogenation ; Noyori asymmetric hydrogenation ; Combinatorial chemistry ; Organic chemistry ; Enantioselective synthesis Asymmetric Hydrogenation and Catalysis ; Catalysis and Hydrodesulfurization Studies ; Catalysts for Methane Reforming