jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kiera Ronda | Jeremy R. Gauthier | Khanisha Singaravadivel | Peter M. Costa | Katelyn Downey | William W. Wolff | Daniel H. Lysak | Jacob Pellizzari | Owen Vander Meulen | Katrina Steiner | Amy Jenne | Monica Bastawrous | Zainab Ng | Agnes Haber | Benjamin Goerling | Venita Busse | Falko Busse | Christopher Elliot | Scott A. Mabury | Mohamed Ateia | Derek C. G. Muir | Robert J. Letcher | Krish Krishnamurthy | Sonya Kleywegt | Karl J. Jobst | Myrna J. Simpson | André J. Simpson
Source: Magnetic Resonance in Chemistry (article, 2025-05-13)
Field: Chemistry — Electrochemistry

NMR as a Discovery Tool: Exploration of Industrial Effluents Discharged Into the Environment NMR provides unprecedented molecular information, urgently needed by environmental researchers and policy makers. However, NMR is underutilized in environmental sciences due to the lack of available technologies, limited environmental-specific training opportunities, and easy-to-use workflows. NMR has considerable potential as a discovery tool for novel pollutants, and by-products, exemplified by the recent discovery of the degradation by-product of a rubber additive, 6PPD-quinone, now considered one of the most toxic compounds presently known. This work represents a proof-of-concept case study highlighting the use of NMR to profile effluents from 38 industries across Ontario, Canada. Wastewater effluents from various industrial sectors were analyzed using several 1D and 2D <sup>1</sup>H/<sup>13</sup>C NMR and <sup>19</sup>F experiments and were screened both unconcentrated and after lyophilization. Common species could be identified using human metabolic NMR databases, but environmental-specific NMR databases desperately need further development. An example of manually identifying unusual NMR signatures is included; these resulted from phosphinic and phosphonic acids originating from the electroplating industry, for which the environmental impacts are not well understood. Basic <sup>1</sup>H NMR quantification is performed using ERETIC, while an optimized approach combining relaxation agents and steady-state-free-precession <sup>19</sup>F NMR, to reduce detection limits (at 500 MHz) to sub-ppb (< 1 μg/L) in under 15 min, is demonstrated. The future potential of benchtop NMR (80 MHz) is also considered. This paper represents a guide to others interested in applying NMR spectroscopy to environmental media and demonstrates the potential of NMR as a complementary tool to assist MS in environmental pollutant and by-product discovery. Chemistry ; Effluent ; Proton NMR ; Carbon-13 NMR ; Nuclear magnetic resonance spectroscopy ; Industrial effluent ; Biochemical engineering ; Environmental chemistry ; Environmental science ; Organic chemistry ; Environmental engineering ; Engineering Electrochemical Analysis and Applications ; Microfluidic and Capillary Electrophoresis Applications ; NMR spectroscopy and applications
Authors: Yan Xue | Shuling Liu | Jiaqi Wang | Huanhuan Zhang | Huijuan Wei | Yanyan Liu | Xiangyu Wang | Jinjun Tian | Jianchun Jiang | Baojun Li
Source: Advanced Science (review, 2025-11-07)
Field: Chemistry — Inorganic Chemistry

A Review on Understanding and Strengthening of Active Species in Titanium Silicalite‐1 (TS‐1) Catalysts Titanium silicalite-1 (TS-1) zeolite stands out as a top-tier catalyst for producing key chemical intermediates under mild conditions. This paper briefly summarizes its applications in oxidation reactions such as ammoximation and epoxidation, and explores the various reaction pathways dominated by Ti active species. Its catalytic oxidation performance depend upon the content and coordination of Ti active species. Strategies for designing highly efficient TS-1 catalysts focus on regulating its structure and active species by adjusting synthesis conditions. Balancing hydrolysis rates of titanium and silicon sources facilitates more framework Ti species and little anatase TiO<sub>2</sub>. Nucleation and crystallization can be controlled to adjust the microenvironment around the active sites by such as pH control, using modifiers, and tuning crystall pathways. Effective regulation of the crystallization process is conducive to the generation of new coordination Ti species beyond the tetra-coordinated TiO<sub>4</sub>. By employing bases and templates, Ti species in TS-1 can be redistributed via dissolution-recrystallization process. Additionally, the introduction of heteroatoms can alter the electronic state of Ti species. These strategies can convert extra-framework titanium into framework titanium or highly coordinated titanium species, thereby introduce mesopores in TS-1 crystals. The design of TS-1 zeolites with diverse active species demonstrates promising industrial applications. Titanium ; Catalysis ; Anatase ; Crystallization ; Heteroatom ; Mesoporous material ; Nucleation Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science
Authors: Yunlei Wang | Haifeng Lv | Xiaojun Wu
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-driven global optimization reveals nanometre-scale mixed phases of borophene on Ag(100) Metal-supported borophene exhibits significant polymorphism and an inherently complex potential energy landscape, posing challenges to exploring its structural diversity. In this study, we integrate a neural network-driven machine learning potential with stochastic surface walking global optimization and an active learning framework to comprehensively map the potential energy surface (PES) of large-size borophene on an Ag(100) substrate. Our exhaustive search identifies 59 857 local minima across 556 distinct supercells, revealing a PES segmented into multiple energy basins and three major funnels. Among the low-energy configurations, 1391 low-energy structures extend to the nanometre scale, showcasing a diverse array of mixed-phase borophene architectures, including monolayer ribbons (β<sub>12</sub> and χ<sub>3</sub>) and bilayer fragments (BL-α<sub>5</sub>, BL-α<sub>7</sub>, BL-α<sub>1</sub>, BL-α<sub>6</sub>, and BL-α<sub>1</sub>α<sub>6</sub>). Notably, the global minimum structures feature monolayers composed of alternating χ<sub>3</sub> and β<sub>12</sub> ribbons and bilayers formed from BL-α<sub>5</sub>, BL-α<sub>1</sub>α<sub>6</sub>, and BL-α<sub>1</sub> fragments. All mixed-phase borophenes exhibit metallic properties, and their simulated scanning tunneling microscopy (STM) images are provided to facilitate future experimental validation. These findings highlight the extraordinary structural complexity and rich polymorphism of borophene on extended metal surfaces, offering valuable insight into their formation, stability, and potential for tunable electronic properties. Borophene ; Nanometre ; Nanotechnology ; Scale (ratio) ; Materials science ; Computer science ; Artificial intelligence ; Physics ; Graphene Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Yuwei Mao | Mahmudul Hasan | Md. Maruf Billah | Youjia Li | S. K. Chakrabarty | C. S. George Lee | Kewei Wang | Muhammed Nur Talha Kilic | Vishu Gupta | Wei‐keng Liao | Alok Choudhary | Pınar Acar | Ankit Agrawal
Source: Scientific Reports (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

An AI framework for time series microstructure prediction from processing parameters In this study, we present an artificial intelligence (AI)-driven framework for predicting the microstructural texture of polycrystalline materials after a specific deformation process. The microstructural texture is defined in terms of the orientation distribution function (ODF) which indicates the volume density of crystal orientations. Our approach leverages an encoder-decoder model with Long Short-Term Memory (LSTM) layers to model the relationship between processing conditions and material properties. As a case study, we apply our framework to copper, generating a dataset of 3125 unique processing parameter combinations and their corresponding ODF vectors. The resulting predictions enable the calculation of homogenized properties. Our AI-driven framework outperforms traditional material processing simulations, yielding faster results with limited error rates (< 0.3% for both the elastic matrix C and the compliance matrix S), making it a promising tool for the expedited design of microstructures with tailored properties. Series (stratigraphy) ; Computer science ; Time series ; Artificial intelligence ; Data mining ; Machine learning ; Biology Machine Learning in Materials Science ; Advanced machining processes and optimization ; Injection Molding Process and Properties
Authors: Lei Shi | Markus Schröder | Hans‐Dieter Meyer | Daniel Peláez | Alec M. Wodtke | Kai Golibrzuch | Anna-Maria Schönemann | Alexander Kandratsenka | Fabien Gatti
Source: The Journal of Physical Chemistry A (article, 2025-01-14)
Field: Materials Science — Materials Chemistry

Full Quantum Dynamics Study for H Atom Scattering from Graphen<sup>e</sup> This study deals with the understanding of hydrogen atom scattering from graphene, a process critical for exploring C-H bond formation and energy transfer during atom surface collision. In our previous work [Shi, L.; <i>J. Chem. Phys.</i> 2023, 159, 194102], starting from a cell with 24 carbon atoms treated periodically, we have achieved quantum dynamics (QD) simulations with a reduced-dimensional model (15D) and a simulation in full dimensionality (75D). In the former work, the H atom attacked the top of a single C atom, enabling a comparison of QD simulation results to classical molecular dynamics (cMD). Our approach required the use of sophisticated techniques such as Monte Carlo canonical polyadic decomposition (MCCPD) and multilayer multiconfiguration time-dependent Hartree (ML-MCTDH), as well as further development of quantum flux calculations. We could benchmark our calculations by comparison to cMD calculations. We now refined our method to better mimic experimental conditions. Specifically, rather than sending the H atom to a specific position on the surface, we employed a plane wave for the H atom in directions parallel to the surface. Key findings for these new simulations include the identification of discrepancies between classical molecular dynamics (cMD) simulations and experiments, which are attributed to both the potential energy surface (PES) and quantum effects. Additionally, this study sheds light on the role of classical collective normal modes during collisions, providing insights into energy transfer processes. The results validate the robustness of our simulation methodologies and highlight the importance of considering quantum mechanical effects in the study of hydrogen-graphene interactions. Molecular dynamics ; Hartree ; Potential energy surface ; Quantum ; Physics ; Quantum dynamics ; Hydrogen atom ; Scattering ; Monte Carlo method ; Atom (system on chip) ; Quantum simulator ; Quantum Monte Carlo ; Atomic physics ; Statistical physics ; Quantum mechanics ; Quantum computer ; Molecule ; Computer science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Graphene research and applications
Authors: Pavankumar Sonawane | Jaydeep Panchal | George Fernandez Raj A | Ankita Avathankar | Srinidhi Campli | Amar Gajbhiye
Source: Evergreen (review, 2025-03-01)
Field: Materials Science — Polymers and Plastics

Characterizing Composite Materials for High-performance Applications: A Review of Testing Methods Composite materials are extensively used in various industries due to their superior mechanical, thermal, and chemical properties.However, assessing and characterizing these materials require a comprehensive understanding of their behavior under different loading and environmental conditions.This review paper provides an overview of the most commonly used testing methods for evaluating the mechanical, thermal, chemical, and surface properties of composite materials.The paper discusses the purpose, objective, setup, sample results, significance, and description of each testing method, covering a wide range of characterization techniques from macroscopic mechanical testing to nanoscale surface analysis.The testing methods discussed include tensile testing, compression testing, flexural testing, shear testing, peel testing, interlaminar shear testing, impact testing, fatigue testing, creep testing, hygrothermal testing, electrical testing, thermal testing, microscopic analysis, spectroscopic techniques, rheological testing, and various other specialized tests.Each testing method is described in detail, highlighting its importance in assessing material performance, structural integrity, durability, and suitability for specific applications.Additionally, relevant standards and guidelines are referenced to ensure accurate and reliable characterization of composite materials.This comprehensive review serves as a valuable resource for researchers, engineers, and practitioners involved in the design, manufacturing, and evaluation of composite materials for diverse applications. Composite number ; Materials science ; Computer science ; Reliability engineering ; Engineering ; Composite material Polymer Nanocomposites and Properties ; Mechanical Behavior of Composites ; Material Properties and Processing
Authors: Alina Pilipenco | Michala Forinová | Zulfiya Černochová | Zdeňka Kolská | Ladislav Fekete | Hana Vaisocherová‐Lísalová | Milan Houška
Source: Langmuir (article, 2025-06-10)
Field: Materials Science — Surfaces, Coatings and Films

The Effective Charge of Low-Fouling Polybetaine Brushes Polybetaine nanobrushes are widely used as inert platforms for label-free biosensing due to their resistance to nonspecific interactions. Despite being considered cationic or electrically neutral, polybetaines can exhibit a negative zeta potential (ZP) at pHs above their isoelectric point (pI). To clarify whether negative zeta potential effectively contributes to surface interactions, we examined three types of nanobrushes deposited on a planar gold substrate: two polybetaines: poly(carboxybetaine methacrylamide) (pCBMAA) and poly(sulfobetaine methacrylamide) (pSBMAA) and hydrophilic poly[<i>N</i>-(2-hydroxypropyl) methacrylamide] (pHPMAA), which carries no ionic group. All three brushes exhibit a well-defined pI and negative surface ZP at pHs above their pI. The pH dependence of the interactions of these brushes with anionic dextran sulfate (DS) and cationic poly[(<i>N</i>-trimethylammonium)ethyl methacrylate] (PTMAEMA) was monitored by infrared reflection spectroscopies (infrared reflection absorption spectroscopy (IRRAS), grazing angle attenuated total reflectance (GAATR)). DS adsorbs to pCBMAA strongly and only weakly to pSBMAA at pHs below their pI but can adsorb slightly to both polybetaines even at pHs above their pI. This is due to the displacement of their carboxylate or sulfo groups from the interaction with the quaternary ammonium cation by the DS sulfate groups. However, DS does not adsorb to pHPMAA at any pH, and PTMAEMA does not adsorb to any of the brushes, regardless of pH. These findings highlight that zeta potential determinations alone may not be sufficient to predict electrostatic interactions as the apparent negative charge does not necessarily translate into a functional surface charge influencing macromolecular interactions. Fouling ; Charge (physics) ; Chemical engineering ; Materials science ; Chemistry ; Nanotechnology ; Membrane ; Physics ; Engineering Polymer Surface Interaction Studies ; Electrospun Nanofibers in Biomedical Applications ; Surface Modification and Superhydrophobicity
Authors: Peter Takáč | Radka Michalková | Martina Čižmáriková | Zdenka Bedlovičová | Ľudmila Balážová | Štefánia Laca Megyesi | Zuzana Mačeková | Gabriela Takáčová | Almudena Moreno-Borrallo | Eduardo Ruiz‐Hernández | Leah Isakov
Source: International Journal of Molecular Sciences (review, 2025-06-02)
Field: Materials Science — Biomaterials

Do We Know Enough About the Safety Profile of Silver Nanoparticles in Oncology? A Focus on Novel Methods and Approaches Silver nanoparticles (AgNPs) have emerged as promising agents in cancer diagnostics and/or therapy, demonstrating a lot of possible pharmacological actions. However, understanding the pharmacokinetics and safety profiles of nanoparticles, which is crucial for their clinical application, still raises many questions. Studies indicate that AgNPs can accumulate in tumour tissues, improving drug delivery and specificity. However, their interaction with biological systems necessitates thorough safety evaluations. Classical methods for assessing AgNPs' safety include cytotoxicity assays, genotoxicity tests, and histopathological examinations. However, novel techniques are emerging, such as advanced imaging and biomarker analysis, offering more precise toxicity assessments. Prediction models, including computational simulations and in silico analyses, are being developed to forecast AgNPs' toxicity profiles. These models aim to reduce reliance on animal testing and expedite the evaluation process. To mitigate potential risks associated with nanoparticle-based therapies, strategies such as surface modification, controlled release systems, and targeted delivery are being explored. These methods aim to enhance therapeutic efficacy while minimizing adverse effects. The main aim of this review article is to describe AgNPs from the point of view of their pharmacokinetic/toxicokinetic profile in the light of modern knowledge. Special attention will be given to novel methods for assessing the safety and toxicity profiles of AgNPs, providing insights into their interactions with cancer therapies and their potential clinical applications. Nanotechnology ; In silico ; Computer science ; Drug delivery ; Risk analysis (engineering) ; Computational biology ; Pharmacology ; Medicine ; Chemistry ; Biology ; Materials science Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods
Authors: Nitin Gumber | Buddhadev Kanrar | Jaspreet Singh | Jitendra Bahadur | Rajesh V. Pai
Source: RSC Advances (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Unraveling the adsorption potential of Zr dithiol (MOF-DSH) through experimentation and neural network modeling In this study, an aqueous-stable metal-organic framework with two thiol groups was synthesized using Zr as the metal centre and dimercaptosuccinic acid as the ligand through a conventional heating method for the removal of Cd(ii) from aqueous solution. Different characterization tools, including XRD, FT-IR, BET, SEM, TGA, and XPS, were employed. XRD results showed a characteristic pattern of a hexa-cluster, which was in agreement with the simulated MOF-801, and the corresponding vibrational peaks were observed in the FT-IR spectra. The synthesized MOF was thermally stable up to 300 °C, as demonstrated by TGA, and exhibited a specific surface area of 290 m<sup>2</sup> g<sup>-1</sup>. Cadmium adsorption studies performed at different pH values showed a maximum adsorption capacity of 91.5 mg g<sup>-1</sup> at pH 6. The adsorption behavior was well described by the Langmuir model and the pseudo-second order (PSO) kinetics, confirming the involvement of a monolayer with chemisorption as the dominant mode of adsorption. The synthesized MOF could be reused at least 4 times while retaining ∼80% of its initial adsorption capacity. FT-IR, XPS, and pH studies after Cd(ii) adsorption revealed that the predominant mode of interaction of Cd(ii) with the MOF is an ion-exchange mechanism. An artificial neural network-based (ANN) methodology was employed to model the adsorption capacity of Cd(ii) and predict the adsorption capacity as a function of Cd(ii) concentration, time of contact, and pH of the medium. The model demonstrated excellent results, with an average error of 2.3% and precision of 3.0%. The outcomes of these studies were consistent with the experimental results. Adsorption ; Artificial neural network ; Dithiol ; Metal-organic framework ; Thiol ; Computer science ; Materials science ; Chemistry ; Artificial intelligence ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning and ELM
Authors: Rongguan Yin | Lianshun Luo | Xiaolei Hu | Hironobu Murata | Jaepil Jeong | Feng Gao | Zijie Qiu | Ben Zhong Tang | Michael R. Bockstaller | Krzysztof Matyjaszewski
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Brush-modified fluorescent organic nanoparticles by ATRP with rigidity-regulated emission Organic nanoparticles provide exceptional intraparticle tailorability, enabling the incorporation of functional molecules for diverse applications. In this study, we present the synthesis and characterization of fluorescent organic nanoparticles (FoNPs) with encapsulated aggregation-induced emission (AIE) luminogens with emission properties regulated by particle rigidity. Atom transfer radical polymerization (ATRP) was employed in dispersed media to develop various fluorescence colors tuned by precise control over particle rigidity. Comprehensive analyses revealed that increased particle rigidity significantly enhanced photoluminescence, achieving quantum yields of up to 22% in selected solvents. The high chain-end fidelity facilitated the grafting of hydrophilic polymer brushes from surfaces of FoNPs used as macroinitiators, enabling their dispersion in aqueous media while maintaining bright fluorescence. These findings highlight the potential of rigidity-regulated FoNPs as versatile platforms for advanced material applications, particularly in fluorescent waterborne films and aqueous-phase sensing systems. Brush ; Fluorescence ; Rigidity (electromagnetism) ; Nanoparticle ; Nanotechnology ; Chemistry ; Materials science ; Photochemistry ; Composite material ; Optics ; Physics Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Covalent Organic Framework Applications
Authors: K Sandhyarani | Frank Jensen | Garrett Goh | O Nathan | Abhinav Hodas | Vishnu | Sam Mcardle | Suguru Endo | Aln Aspuru-Guzik | Simon Benjamin | Xiao Yuan | Gunnar Karlstrm | Roland Lindh | Per-ke Malmqvist | Bjrn Roos | Ulf Ryde | Valera Veryazov | Per-Olof Widmark | Geoff Downs | John Barnard | David Feller
Source: Journal on Applied and Chemical Physics (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Computational Chemistry Computational chemistry serves as a powerful tool for analyzing catalytic systems and molecular properties without the need for extensive laboratory experimentation. Leveraging modern electronic structure theory and density functional theory (DFT), researchers can model catalysts, predict activation energies, evaluate site reactivity, and calculate other critical thermodynamic parameters. In the field of drug development, computational methods accelerate the discovery process by simulating drug molecules, estimating difficult-to-measure values such as pKa, and optimizing synthetic routes, thereby reducing both time and cost. These simulations are grounded in quantum chemical approaches that solve the molecular Schrödinger equation. Among these, ab initio methods—derived purely from theoretical principles without empirical data—play a central role. Such methods require the specification of a level of theory and a basis set, which together enable the description of molecular orbitals through the linear combination of atomic orbitals (LCAO). A widely used ab initio technique is the Hartree–Fock method, which approximates electron-electron interactions by averaging them rather than treating them explicitly. As basis set complexity increases, the results converge towards the Hartree–Fock limit, providing increasingly accurate models of molecular behavior. Overall, computational chemistry not only enhances understanding of chemical systems but also supports the development of efficient, cost-effective solutions in catalysis and pharmaceutical research. Chemistry ; Computer science Machine Learning in Materials Science ; Analytical Chemistry and Chromatography ; Various Chemistry Research Topics