jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kazuharu Yoshizuka | Manja Kubeil | Holger Stephan
Source: Journal of Inclusion Phenomena and Macrocyclic Chemistry (article, 2026-04-02)
Field: Chemistry — Spectroscopy

Characterization of macrocyclic host-guest systems by liquid-liquid extraction Abstract Macrocyclic compounds have advanced the molecular recognition of many organic and inorganic guest molecules, utilizing in particular their high degree of pre-organization. This has led to a number of attractive applications, for example in sensor technology, selective separation processes, and the manufacture of advanced materials, right through to the development of artificial enzymes. Liquid-liquid extraction has accompanied these developments from the outset and has contributed, on the one hand, to a better understanding of the fundamentals of molecular recognition processes and, on the other hand, to the development of practical methods for the effective and selective separation of different guest molecules using macrocyclic compounds. Extraction offers the possibility to investigate host-guest systems where the reaction partners have very different solubility properties. The use of the radiotracer method in particular provides very reliable data with a high degree of accuracy. This approach yields highly accurate separation and selectivity factors, as well as information on the lipophilicity of host compounds and even thermodynamic parameters, which provide insight into the enthalpic and entropic contributions of the molecular recognition process. Graphical Abstract Lipophilicity ; Extraction (chemistry) ; Selectivity ; Characterization (materials science) ; Molecular recognition ; Solubility ; Molecule Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes ; Molecular spectroscopy and chirality
Authors: Jiaoli Li | Yuwei Zhang | Congjie Wei | Yanxiao Li | Shuo He | Risheng Wang | Brian C. Wyatt | Reza Namakian | Babak Anasori | Kelvin Y. Xie | Tobin Filleter | Ali Erdemir | Wei Gao | Chenglin Wu
Source: ArXiv.org (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Compositional Complexity-Induced Ultralow Friction in Medium-Entropy MXenes Two-dimensional MXenes are promising solid lubricants, but the roles of compositional complexity and surface chemistry in governing interfacial friction remain unclear. Here, we systematically investigate the adhesion and friction behaviors of medium-entropy (ME) MXenes, TiVNbMoC3 and TiVCrMoC3, and compare them with conventional titanium carbide MXenes, Ti2C and Ti3C2, using a SiO2 colloidal atomic force microscopy probe. Thermal annealing at 200 C converts OH surface terminations to O terminations, leading to pronounced reductions in adhesion energy and friction force across all MXenes studied. ME MXenes exhibit larger adhesion reductions because of their higher initial OH contents and more extensive OH-to-O conversion. In addition, their intrinsically higher out-of-plane bending stiffness suppresses energy dissipation during sliding, enabling ultralow friction. Notably, superlubricity is achieved in ME MXenes, with annealed TiVCrMoC3 exhibiting a coefficient of friction as low as 0.0022, outperforming graphene, MoSe2, and other MXenes evaluated using the same experimental approach. These findings identify compositional complexity as a powerful strategy for engineering MXenes with exceptional tribological performance and establish ME MXenes as a new class of solid lubricants. MXenes ; Adhesion ; MAX phases ; Tribology ; Dissipation ; Carbide ; Titanium carbide MXene and MAX Phase Materials ; Lubricants and Their Additives ; Calcium Carbonate Crystallization and Inhibition
Authors: Andriy Kovalenko
Source: ArXiv.org (article, 2026-03-20)
Field: Materials Science — Biomaterials

Multiscale theory, modelling, and simulation of hemicellulose and lignin in solution This review examines multiscale modelling approaches for cellulose nanocrystals (CNCs) and lignocellulosic plant cell walls, with a focus on hemicellulose and lignin interactions in aqueous environments. The three-dimensional reference interaction site model with the Kovalenko-Hirata closure (3D-RISM-KH) is highlighted as a powerful molecular solvation theory applied in nanochemistry and biomolecular simulations. The method has been successfully employed to investigate hemicellulose hydrogels, the influence of hemicellulose composition on nanoscale forces in primary cell walls, and lignin-lignin and lignin-hemicellulose interactions. Findings indicate that these interactions are predominantly hydrophobic and entropy-driven, arising from water exclusion effects. Insights gained through this modeling framework deepen the understanding of molecular-scale forces in plant cell walls and inform strategies for biomass valorization, including genetic engineering and pretreatment technologies aimed at enhancing cellulose extraction and utilization. Hemicellulose ; Lignin ; Cellulose ; Lignocellulosic biomass ; Solvation ; Biomass (ecology) Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Lignin and Wood Chemistry
Authors: Rong Phoophuangpairoj | Panida Sampranpiboon
Source: Journal of Current Science and Technology (article, 2026-03-30)
Field: Chemistry — Analytical Chemistry

A Hybrid Deep Learning and Machine Learning Approach for Predicting Aqueous Solution Concentrations Assessing solution concentration is essential across multiple scientific disciplines; however, it is often complicated by limitations in instrument precision, sample impurities, and environmental variables. Low concentration levels frequently necessitate sophisticated methods such as spectroscopy or chromatography, which require specific apparatus and expertise. Conventional methods might be laborious and occasionally inadequate for accurate measurements. Consequently, researchers continually develop better, more efficient, and economically viable methodologies. Recent technological advancements, including deep learning and machine learning, facilitate the development of efficient, cost-effective systems for determining solution concentration levels, applicable to environmental monitoring and food safety tests. Therefore, this research developed a methodology for estimating solution concentrations through deep learning feature extraction and machine learning-based prediction. Images of the solution at varying concentrations were used to train models that apply deep learning for feature extraction. Linear regression (LR), artificial neural network (ANN), support vector regression (SVR), and random forest (RF) were then evaluated for using the extracted features to forecast the concentrations. Using features extracted from Visual Geometry Group 16-layer Convolutional Neural Network (VGG16) with LR, ANN, SVR, and RF yielded absolute prediction errors of 0.056229, 0.080000, 0.112172, and 0.026640, respectively, for concentration class prediction (classes 1–10). When the concentrations of classes 1 to 10 were evenly changed from 0 ppm to 4500 ppm, using VGG16 to extract features and RF to predict concentrations resulted in an average absolute error of 13.32 ppm, an RMSE of 0.072531 (normalized class scale) and 36.31 ppm (concentration scale), and an R² of 0.999361. The findings indicated that the proposed inexpensive method could efficiently classify the solution in different concentration classes and forecast their concentrations. Support vector machine ; Convolutional neural network ; Random forest ; Artificial neural network ; Deep learning ; Mean squared error ; Feature extraction ; Regression Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Air Quality Monitoring and Forecasting
Authors: Ntombenhle Maria Masanabo | Usisipho Feleni | Richard Moutloali | Lawrence Mzukisi Madikizela | Lueta-Ann de Kock
Source: Environmental Science and Pollution Research (article, 2026-03-25)
Field: Chemistry — Electrochemistry

Electrochemical sensor based on iron-based metal-organic framework nanocomposite derived from acid mine drainage for the detection of lead (II) ions in water Lead (Pb<sup>2+</sup>) in water poses significant health risks, which require sensitive and cost-effective detection techniques. This study addresses this challenge by presenting a novel electrochemical sensor based on Fe-alginate metal-organic framework (Fe-alg-MOF) composite. This composite was prepared by leveraging the high iron content in acid mine drainage (AMD) to form Fe-alg-MOF composite via an in situ synthesis process. Characterization revealed a semi-amorphous material with irregularly shaped Fe-MOF particles embedded within the alginate network, and a surface area of 302.4 m<sup>2</sup>/g. The Fe-alg-MOF was incorporated with polytetrafluoroethylene (PTFE) on a glassy carbon electrode (GCE) to fabricate a Fe-alg-MOF/PTFE/GCE sensor. Square wave voltammetry (SWV) was employed for Pb<sup>2+</sup> detection at - 0.59 V, achieving a detection limit of 1.0 µM and a linear range of 4-50 µM. Furthermore, the practical applicability of the sensor was demonstrated by high recovery rates (~ 100%) in real wastewater effluent samples. Compared to conventional sensors, this platform utilizes an AMD waste derived material, demonstrating significant potential as a cost-effective and reliable tool for monitoring lead contamination in environmental waters. Detection limit ; Nanocomposite ; Electrochemical gas sensor ; Wastewater ; Acid mine drainage ; Effluent ; Electrode ; Electrochemistry ; Composite number ; Contamination Electrochemical Analysis and Applications ; Arsenic contamination and mitigation ; Heavy metals in environment
Authors: Elaine M. Senra | Ana Carolina da Silva Guimarães | Renan Henriques Gonçalves De Almeida | Ana Lúcia Nazareth da Silva | José Carlos Costa da Silva Pinto | Christine Rabello Nascimento | Élen Beatriz Acordi Vasques Pacheco
Source: Recycling (article, 2026-02-05)
Field: Materials Science — Polymers and Plastics

A Technical Feasibility Assessment of Chemically Recycling PET Fibers Through Glycolysis to Produce Functional Oligoesters This study investigated the chemical recycling of poly(ethylene terephthalate) (PET) fiber residues from two sources—high-molar mass mooring ropes and low-molar mass textile-grade fibers—to produce functional oligomers. Glycolysis was carried out using polyethylene glycol (PEG400) as the depolymerizing agent, and two catalysts were assessed, zinc acetate and lithium octoate, with the latter reported on for the first time in this application. Reactions were performed for 180 min under mechanical stirring, inert atmosphere, reflux, and controlled heating. The resulting oligomers were characterized by Fourier-transform infrared spectroscopy (FTIR), hydroxyl and acidity indices, and thermogravimetric analysis (TGA). Both PET feedstocks showed high reactivity toward glycolysis. Monitoring the reactions by acidity index indicated that conversion reached equilibrium at approximately 120 min. ATR-FTIR confirmed the formation of ester and hydroxyl groups, consistent with oligomer structures. Glycolysis of PET derived from mooring ropes produced oligoesters with hydroxyl values of 228 and 242 mgKOH/g for zinc acetate and lithium octoate, respectively, and molar masses of 1296 and 1338 g/mol for zinc acetate and lithium octoate, respectively. These values are suitable for subsequent syntheses such as polyester polyol production. Thermogravimetric analysis ; Zinc ; Oligomer ; Polyester ; Ethylene glycol ; Catalysis ; Lithium (medication) ; Polyethylene terephthalate ; Polyol Polymer crystallization and properties ; Fiber-reinforced polymer composites ; biodegradable polymer synthesis and properties
Authors: S.I. Simak | Yu. Kh. Vekilov | A.B. Belonoshko | O.Yu. Vekilova
Source: Computational Condensed Matter (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Critical importance of k-point convergence in supercell calculations of mechanical instabilities: Implications for shape-memory alloys First-principles calculations of solids close to mechanical instabilities, for example, in studies of martensitic transformations in shape-memory alloys, require careful convergence with respect to both supercell size and k-point sampling density. Naturally, any such simulation should employ well-converged parameters that allow one to capture the quantitatively correct energetics of the most important instability waves. We demonstrate that large supercells used in Ab Initio Molecular Dynamics (AIMD) with common but insufficient k-point sampling can also produce qualitatively incorrect predictions of mechanical stability. A simple but useful rule is suggested: the most important destabilizing wave is identified, the smallest preferably uniformly spaced supercell allowing for such a wave is taken, for which k-points are converged; then a general scaling relation for maintaining equivalent k-point density across different supercell sizes stating that the number of k-points should scale with the inverse cube root of the number of primitive cells in the supercell is used. We validate this approach considering high-temperature shape-memory alloy NbRu, in which accurate prediction of a mechanical instability is crucial for modeling the martensitic transition. Using the most important 〈 110 〉 [1 1 ̄ 0] destabilizing wave in B2-structured NbRu we show that a typical in AIMD studies Γ -point sampling alone for a 128-atom or even a 432-atom supercell cannot correctly predict stability against the wave that drives the martensitic transition, whereas the parameters for convergence can be easily estimated, and the properly converged calculations exhibit the correct behavior. These findings have direct implications for computational materials design involving large-scale, time-conserving simulations, particularly for systems in which mechanical instabilities govern functional properties. Supercell ; Scaling ; Instability ; Convergence (economics) ; Ab initio ; Stability (learning theory) ; Martensite Shape Memory Alloy Transformations ; Intermetallics and Advanced Alloy Properties ; Machine Learning in Materials Science
Authors: Andriy Kovalenko
Source: Condensed Matter Physics (article, 2026-03-30)
Field: Materials Science — Biomaterials

Multiscale theory, modelling, and simulation of hemicellulose and lignin in solution This review examines multiscale modelling approaches for cellulose nanocrystals (CNCs) and lignocellulosic plant cell walls, with a focus on hemicellulose and lignin interactions in aqueous environments. The threedimensional reference interaction site model with the Kovalenko–Hirata closure (3D-RISM-KH) is highlighted as a powerful molecular solvation theory applied in nanochemistry and biomolecular simulations. The method has been successfully employed to investigate hemicellulose hydrogels, the influence of hemicellulose composition on nanoscale forces in primary cell walls, and lignin-lignin and lignin-hemicellulose interactions. Findings indicate that these interactions are predominantly hydrophobic and entropy-driven, arising from water exclusion effects. Insights gained through this modeling framework deepen the understanding of molecular-scale forces in plant cell walls and inform strategies for biomass valorization, including genetic engineering and pretreatment technologies aimed at enhancing cellulose extraction and utilization. Hemicellulose ; Lignin ; Cellulose ; Solvation ; Lignocellulosic biomass ; Biomass (ecology) ; Aqueous solution Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Lignin and Wood Chemistry
Authors: Isaac J. Parker | Mandy Hoffmann | William J. Baldwin | Shuang Han | Srishti Gupta | Kara D. Fong | Angelos Michaelides | Christoph Schran | Sandip De | Gábor Csányi
Source: ArXiv.org (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

False Metallization in Short-Ranged Machine Learned Interatomic Potentials Machine learned interatomic potentials (MLIPs) have enabled atomistic simulations with ab initio accuracy for a fraction of the computational cost. However, many widely used MLIPs are short-ranged and do not accurately capture long-ranged electrostatic interactions. At interfaces with polar solvents, such as water, this deficiency can drive unphysical long-distance dipolar alignment far away from the interface. Here we reveal that neglecting long-ranged physics leads to spurious metallization of the water layer due to artificially large fluctuations of the total solvent dipole, similar to the electron rearrangement observed to prevent polar catastrophes at polar interfaces. This metallization is eliminated in MLIPs that explicitly include long-ranged electrostatics. Our results showcase a fundamental flaw of short-ranged MLIPs, highlighting that long-ranged electrostatics are essential for studying systems with a polar-liquid component, especially if one is interested in electronic properties. Polar ; Spurious relationship ; Electrostatics ; Dipole ; Interatomic potential ; Ab initio ; Molecular dynamics ; Ab initio quantum chemistry methods Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems
Authors: Boshi Gao | Yuefeng Yan | K. Q. Zhang | Dongdong Liu | Xiaoxiao Huang
Source: Advanced Functional Materials (article, 2026-02-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultralight Broadband Electromagnetic Wave Absorption Enabled by Interface‐Engineered Two‐layered Periodic Carbon Fiber Fabrics ABSTRACT The long continuous carbon fiber fabric, optimized through a genetic algorithm combined with electromagnetic simulation, has emerged as the preferred choice for lightweight, high‐strength, and broadband electromagnetic wave absorbing (EMA) materials. Although this approach utilizes meta‐structure design to minimize impedance mismatch caused by the ultrahigh conductivity of carbon fibers, this genetic constraint restricts the achievement of efficient absorption at broader and lower frequencies. Herein, we propose a hyperbranched polyamide@graphene oxide@carbon fiber structure to expand interfacial polarization loss. The thickness of the coating layer is engineered by the branching degree of the hydrogen‐bonded polymer, deliver superior gene library for subsequent population fitness optimization. Incorporating a two‐layered periodic meta‐structure design to broaden the optimization dimensions, the overall fitness of the population is effectively elevated, achieving effective EMA within broader and lower frequencies. The global optimized two‐layered carbon fiber fabric (TCFF) exhibits efficient absorption over the C‐, X‐, and Ku‐bands, delivering an effective absorption (RL −10 dB) bandwidth of 14.79 GHz. Moreover, this long continuous carbon fiber fabric structure offers notable mechanical advantages, combining both exceptional strength and low density, which are rarely reported together in conventional EMA metamaterials. This work proposes valuable insights for the development of ultralightweight, high‐strength, and broadband electromagnetic stealth materials. Broadband ; Absorption (acoustics) ; Electromagnetics ; Conductivity ; Electromagnetic radiation ; Bandwidth (computing) ; Population Electromagnetic wave absorption materials ; Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies
Authors: Yaojun Hou | Oumaïma Gharbi | Chenyang Xie | Divino Salvador Ramírez Rico | Fan Sun | Mireille Turmine | Kevin Ogle | Vincent Vivier
Source: npj Materials Degradation (article, 2026-02-02)
Field: Materials Science — Metals and Alloys

Tracking element-specific dissolution during pitting corrosion: an operando ICP-AES–electrochemical study of the CoCrFeMnNi Cantor alloy Abstract In this work, a flow-through electrochemical–ICP-AES platform for operando monitoring of pitting corrosion on the CoCrFeMnNi high-entropy alloy is introduced. This setup combines localized chloride injection, potentiostatic control, and online, element-resolved dissolution analysis, thereby addressing a long-standing gap in mechanistic studies of early pit initiation and repassivation. Experiments in 0.5 M H 2 SO 4 with Cl - injection enabled the continuous transfer of dissolved species from the electrode surface to the ICP-AES detector, achieving sub-ppb sensitivity and allowing quantification of Co, Cr, Fe, Mn, and Ni dissolution rates during the pitting process. The results reveal four characteristic stages, namely, incubation, initiation, propagation, and repassivation, with subtle but systematic differences between alloying elements. Co and Fe contribute slightly more during initiation, while Cr plays a dominant role during repassivation, reflecting its critical involvement in passive film regeneration. Charge analysis demonstrates that repassivation consumes a quantity of charge far greater than expected for compact passive films, pointing instead to a slow, iterative re-formation and partial dissolution of hydrated oxides. This methodology provides new mechanistic insight into the dynamic sequence of film breakdown, localized dissolution, and film repair in multicomponent alloys, and establishes a versatile framework for studying localized corrosion processes with element-specific resolution. Dissolution ; Pitting corrosion ; Alloy ; Corrosion ; Tracking (education) ; Electrode Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; High-Temperature Coating Behaviors
Authors: Jiachuang Wang | Shuai Wei | Nan Qin | Tiger H. Tao
Source: Nature Communications (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

An insect-scale artificial visual-olfactory bionic compound eye Compound eyes feature unique optical structures and high-efficiency image processing. The opto-olfactory nervous system of Drosophila has the characteristics of lightweight and low power consumption. Significant efforts have been dedicated to the design and manufacturing of artificial compound eye system. However, it is still challenging to construct a bionic visual-olfactory compound eye microsystem with sensitive photoelectric response and accurate olfactory perception in insect-scale, mimicking the biological multimodal fusion decision-making mechanism. Here, we report a miniature apposition compound eye that integrates 1027 ommatidia on 1.5×1.5 mm<sup>2</sup> by manufacturing a bionic micro-lens array onto flexible photodetectors via femtosecond laser two-photon polymerization, further construct the colorimetric olfactory sensor array through inkjet printing to achieve integrated perception of vision and smell. The bionic compound eye (bio-CE) enables wide field-of-view imaging (azimuth angle 180°), natural interocular isolation, a 1 kHz flicker fusion frequency and color response to various hazardous chemicals, resulting in high sensitivity to moving objects and rapid response to environmental gases. The microsystem can serve as a wide-angle close-range obstacle avoidance detector and a device for monitoring visual and olfactory information of moving targets. The insect-scale bionic apposition compound eye shows great potential applications in unmanned platform navigation and bionic robot intelligence. Compound eye ; Bionics ; Machine vision ; Apposition ; Sensor fusion ; Ommatidium Photochromic and Fluorescence Chemistry ; Neurobiology and Insect Physiology Research ; Photonic Crystals and Applications
Authors: Suelen Barg | Michéle Scholl | Sarah Vollath | Leon Böhmer | Jens Herb | Ricardo Thisted | Nils Meyer
Source: Research Square (preprint, 2026-01-09)
Field: Materials Science — Materials Chemistry

Additive-Free 3D Printing of Ti3C2Tx MXene-Based Ceramic Inks via Bayesian Optimisation 3D printing ; Ceramic ; Quality (philosophy) ; Fabrication ; MXenes ; Composite number ; Exploit ; Sustainable energy MXene and MAX Phase Materials ; Additive Manufacturing and 3D Printing Technologies ; Nanomaterials and Printing Technologies