jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Monica Di Maria | Diego Planeta | Francesca Venturini | Federica Torregrossa | Pasquale Crupi
Source: Frontiers in Nutrition (article, 2026-01-08)
Field: Chemistry — Organic Chemistry

How can we sustainably assess the shelf life of EVOO? A systematic review on analytical strategies and food waste reduction Nowadays, olive oil quality is assessed through a combination of physicochemical parameters and sensory evaluation performed by trained tasting panels. The International Olive Council (IOC) and Codex Alimentarius define the legal limits and reference values for these parameters. The current analytical methods used to characterize extra virgin olive oil (EVOO), are based on destructive chemical techniques that are time-consuming, require long sample preparation (highly skilled operators), resource-intensive and involve the use of toxic solvents, with marked environmental impact and costs representing an obstacle in the green transition. Additionally, many of them do not allow for real-time analysis or analysis in line with industrial processes. Furthermore, none of the currently established methods adequately address the qualitative deterioration of virgin olive during storage. Thus, quality evaluation should include not only regulatory criteria but also parameters related to human health, the formation of degradation products, especially with respect to storage conditions. In addition, attention should be focused particularly on the concept of secondary shelf-life, with the aim of reducing food waste of olive oil while characteristics still remain unaltered. In light of this, it is necessary to explore alternative analytical strategies that are rapid, non-destructive, and sustainable, capable of guaranteeing the quality and safety of EVOO, reducing food waste, and respecting the principles of environmental sustainability. This review aims to critically analyze the latest analytical methodologies applied to determine the shelf life of EVOO, with a particular focus on their potential contribution to reducing waste and aligning with the objectives of Agenda 2030. Sustainability ; Food waste ; Quality (philosophy) ; Olive oil ; Shelf life ; Consumer safety ; Sample (material) ; Food safety Edible Oils Quality and Analysis ; Food Waste Reduction and Sustainability ; Date Palm Research Studies
Authors: Satya Parkash
Source: Springer Link (Chiba Institute of Technology) (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Advances in Material Science: Foundations, Innovations, and Future Directions Materials science is a critical interdisciplinary field that bridges engineering, physics, and chemistry. The focus of materials science is to understand how the structure, properties, and behavior of materials are influenced by their atomic and molecular composition. In the 21st century, materials science is contributing to revolutionary technologies that are transforming industries such as aerospace, electronics, energy, and medicine. Recent advances, particularly in nanotechnology, biomaterials, and computational modeling, are enabling scientists to engineer materials with unprecedented precision, allowing for the creation of new materials with superior properties. Nanomaterials are being engineered to exhibit exceptional mechanical strength, electrical conductivity, and flexibility, which have vast applications in electronics, energy storage, and even drug delivery systems. Biomaterials, on the other hand, are redefining the future of regenerative medicine and prosthetic design. Simultaneously, there is a growing emphasis on sustainable materials that reduce waste and carbon footprints, contributing to environmental conservation. This paper provides an overview of both traditional and emerging advancements in materials science, with a focus on advanced characterization techniques, artificial intelligence in material discovery, and the shift towards environmentally responsible materials. Emerging technologies ; Biological materials ; Sustainability ; Field (mathematics) ; Characterization (materials science) ; Smart material ; Biomimetic materials MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Graphene and Nanomaterials Applications
Authors: Mai Ahmed | Deniz Yilmaz | Purushottam Poudel | Felix H. Schacher | Eva Perlt
Source: The Journal of Physical Chemistry B (article, 2026-01-27)
Field: Materials Science — Biomaterials

Atomistic Insights into Structure and Properties of ε-Caprolactone Oligomers The design of functional and sustainable materials requires a detailed understanding of the material properties and degradation mechanisms. In particular, the design of fully biodegradable polymers could allow a quick and controlled decomposition of materials before they accumulate in the environment and break down to micro- and nanoplastics. An important degradation pathway proceeds via the hydrolysis of polyesters. To obtain the best performing material candidates, a multiscale-level understanding that takes into account electronic structure combined with multiple configurations at the macroscopic scale is necessary. In this contribution, we present the extension of the multiscale Quantum Cluster Equilibrium method to oligomer materials. We showcase the first application of this methodology to oligomer systems, in particular oligo(ε-Caprolactone). The ε-Caprolactone oligomers were synthesized and characterized comprehensively by means of NMR, SEC, DSC, and TGA. Experimentally, two melting temperatures were observed, which were predicted by theoretical calculations and are in convincing agreement. Oligomer ; Polymer ; Decomposition ; Degradation (telecommunications) ; Hydrolytic degradation ; Cluster (spacecraft) ; Scale (ratio) ; Material properties biodegradable polymer synthesis and properties ; Polymer crystallization and properties ; Fiber-reinforced polymer composites
Authors: Shohag Rana | Humaira Rahman Tanjila
Source: Scientia. Technology, science and society. (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

A Comprehensive Review on Characterization of Thermal Anisotropy in Inorganic Functional Materials Thermal anisotropy directional dependence of thermal conductivity is a key property governing the performance of many inorganic functional materials, including thermoelectric, semiconductors, layered crystals, and composites. Understanding and controlling thermal anisotropy is essential for the rational design of materials for thermal management, energy conversion, and electron-ic applications. This review presents a comprehensive overview of the fundamental origins, characterization techniques, and theoretical modeling of thermal anisotropy in inorganic materials. The focus is placed on state-of-the-art experimental methods such as time-domain thermos reflectance (TDTR), frequency-domain thermos reflectance (FDTR), laser flash analysis (LFA), micro-Raman thermometry, and scanning thermal microscopy (SThM). We also discuss computational tools based on first-principles phonon transport calculations and molecular dynamics simulations. Finally, the review highlights representative case studies such as layered van der Waals crystals, perovskite oxides, and nanostructured ceramics illustrating how structural anisotropy, grain boundaries, and defects influence heat conduction. Emerging challenges and future perspectives are outlined toward achieving precise, multi-scale characterization of anisotropic thermal behavior in functional inorganic materials. Characterization (materials science) ; Anisotropy ; Thermal conductivity ; van der Waals force ; Thermal barrier coating ; Thermal ; Phonon Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Kris Helten | Nicholaus Prasetya | Bradley P. Ladewig
Source: ACS Omega (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Data-Driven Evaluation for Multicriteria Selection of Covalent Organic Frameworks for Membrane-Based Hydrogen Separation Hydrogen is considered the fuel of the future for the decarbonizing industry and the transport sector. A membrane containing porous materials can be used to extract hydrogen from various gas mixtures. The diversity of porous materials makes it difficult to select a specific material for hydrogen separation. Covalent organic frameworks (COFs) are becoming increasingly popular for gas separation processes, especially for high-temperature applications. The properties of a COF depend on the configuration of various building blocks. A data-driven evaluation was performed to select the most promising COF features and topologies from the CoRE COF database of 1242 reported COFs based on quantification using a scoring approach. The results of the property-based evaluation model show that 3D COFs have a higher potential on average than 2D COFs. Four different normalized evaluations describe the design with the highest potential. The interdisciplinary quantification approach of the preferred properties enables the selection of a specific topology from 2D or 3D covalent organic frameworks and serves as an indication of further advantageous features for a selective hydrogen separation material. Selection (genetic algorithm) ; Hydrogen ; Work (physics) ; Separation (statistics) ; Separation method Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Mina Kim | Sang Yun Han
Source: Analytica—A Journal of Analytical Chemistry and Chemical Analysis (article, 2026-03-10)
Field: Chemistry — Spectroscopy

Assessing Transesterification Reagents for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry of Biodegradable Polymers Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a powerful technique for polymer identification because it can ionize intact macromolecules with large molecular weights (&gt;100 kDa) and detect them over a theoretically unlimited mass range. However, limited mass resolution, accuracy, and sensitivity in the high-mass region restrict detailed structural characterization of polymers. Depolymerization into smaller fragments using established chemistry, such as transesterification, provides a versatile alternative approach. The resulting characteristic fragments (&lt;3 kDa) are polymer-specific and enable both polymer identification and detailed structural analysis, benefiting from the higher mass accuracy and sensitivity in the low-mass range and allowing MS/MS-based identification. To establish general sample-preparation conditions, this study systematically investigated the effects of transesterification of biodegradable polyesters using MeO−/MeOH, EtO−/EtOH, and t-BuO−/t-BuOH on subsequent MALDI-TOF MS analysis. Among the reagents examined, EtO−/EtOH combined with THAP/THF matrix treatment was found to be the most efficient, enabling rapid polyester analysis by MALDI-TOF MS within 10 min when the reaction time and reagent concentration were carefully controlled within an optimal window. Depolymerization ; Reagent ; Polyester ; Mass spectrometry ; Polymer ; Macromolecule ; Transesterification ; Biodegradable polymer Mass Spectrometry Techniques and Applications ; Advanced Polymer Synthesis and Characterization ; Metabolomics and Mass Spectrometry Studies
Authors: Carlos Cobas
Source: ChemRxiv (article, 2026-02-12)
Field: Chemistry — Spectroscopy

Splitting-Domain Reformulation of Symmetric Strongly Coupled Four-Spin NMR Systems The analysis of strongly coupled ¹H NMR spin systems of the AA′BB′ and AA′XX′ type has remained one of the most challenging problems in spectral interpretation for over six decades. The classical Pople framework provides a complete closed-form solution, but requires the prior assignment of observed peaks to specific quantum-mechanical transitions, a combinatorial problem that has rendered the method impractical for routine use. Alternative approaches based on iterative quantum-mechanical fitting exist but remain computationally demanding and sensitive to initial parameter estimates, limiting their adoption for routine analysis. Here we demonstrate that both limitations can be bypassed by reformulating the analysis in the domain of pairwise frequency differences. We show that the coupling interaction between nuclei guarantees the existence of characteristic shared splittings within this set, reducing the problem from combinatorial assignment to pattern recognition. A Set Transformer extracts the relevant splittings from the unordered difference set, and a differentiable Pople inversion layer converts them into coupling constants through exact analytical equations, enabling end-to-end training with physics-informed loss functions. The approach is validated on two distinct four-spin topologies (ortho-and para-disubstituted benzenes), achieving sub-0.05 Hz mean absolute errors on synthetic data and sub-0.08 Hz on experimental spectra from six compounds spanning the full range from the AA′XX′ limit to the genuinely second-order AA′BB′ regime. The two models share an identical neural architecture differing only in the deterministic Pople layer, demonstrating the generality of the splitting-domain representation across coupling topologies. Pairwise comparison ; Differentiable function ; Limiting ; Limit (mathematics) ; Coupling (piping) ; Coupling constant ; Representation (politics) ; Spectral line ; Inversion (geology) Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality ; NMR spectroscopy and applications
Authors: Satya Parkash
Source: EPJ Web of Conferences (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Advances in Material Science: Foundations, Innovations, and Future Directions Materials science is a critical interdisciplinary field that bridges engineering, physics, and chemistry. The focus of materials science is to understand how the structure, properties, and behavior of materials are influenced by their atomic and molecular composition. In the 21st century, materials science is contributing to revolutionary technologies that are transforming industries such as aerospace, electronics, energy, and medicine. Recent advances, particularly in nanotechnology, biomaterials, and computational modeling, are enabling scientists to engineer materials with unprecedented precision, allowing for the creation of new materials with superior properties. Nanomaterials are being engineered to exhibit exceptional mechanical strength, electrical conductivity, and flexibility, which have vast applications in electronics, energy storage, and even drug delivery systems. Biomaterials, on the other hand, are redefining the future of regenerative medicine and prosthetic design. Simultaneously, there is a growing emphasis on sustainable materials that reduce waste and carbon footprints, contributing to environmental conservation. This paper provides an overview of both traditional and emerging advancements in materials science, with a focus on advanced characterization techniques, artificial intelligence in material discovery, and the shift towards environmentally responsible materials. Emerging technologies ; Field (mathematics) ; Biological materials ; Smart material ; Characterization (materials science) ; Focus (optics) ; Biomimetic materials MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Graphene and Nanomaterials Applications
Authors: Dingding Cao | Mieow Kee Chan | Wan Sieng Yeo | Said Bey | Alberto Figoli
Source: arXiv (Cornell University) (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Intelligent Materials Modelling: Large Language Models Versus Partial Least Squares Regression for Predicting Polysulfone Membrane Mechanical Performance Predicting the mechanical properties of polysulfone (PSF) membranes from structural descriptors remains challenging due to extreme data scarcity typical of experimental studies. To investigate this issue, this study benchmarked knowledge-driven inference using four large language models (LLMs) (DeepSeek-V3, DeepSeek-R1, ChatGPT-4o, and GPT-5) against partial least squares (PLS) regression for predicting Young's modulus (E), tensile strength (TS), and elongation at break (EL) based on pore diameter (PD), contact angle (CA), thickness (T), and porosity (P) measurements. These knowledge-driven approaches demonstrated property-specific advantages over the chemometric baseline. For EL, LLMs achieved statistically significant improvements, with DeepSeek-R1 and GPT-5 delivering 40.5% and 40.3% of Root Mean Square Error reductions, respectively, reducing mean absolute errors from $11.63\pm5.34$% to $5.18\pm0.17$%. Run-to-run variability was markedly compressed for LLMs ($\leq$3%) compared to PLS (up to 47%). E and TS predictions showed statistical parity between approaches ($q\geq0.05$), indicating sufficient performance of linear methods for properties with strong structure-property correlations. Error topology analysis revealed systematic regression-to-the-mean behavior dominated by data-regime effects rather than model-family limitations. These findings establish that LLMs excel for non-linear, constraint-sensitive properties under bootstrap instability, while PLS remains competitive for linear relationships requiring interpretable latent-variable decompositions. The demonstrated complementarity suggests hybrid architectures leveraging LLM-encoded knowledge within interpretable frameworks may optimise small-data materials discovery. Partial least squares regression ; Ultimate tensile strength ; Mean squared error ; Regression ; Estimator ; Polysulfone ; Metric (unit) ; Modulus ; Linear regression ; Ordinary least squares Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Di Dong | Changjiu He | Tao Cui | Yudong Lu | Xuebing Qin
Source: Rapid Communications in Mass Spectrometry (article, 2026-01-20)
Field: Chemistry — Spectroscopy

Recursive Attention–Based Prediction of Theoretical MS/MS Spectra From Peptide Sequences These findings demonstrate that incorporating recursive attention and multi-ion prediction significantly improve both the peak coverage and accuracy of theoretical MS/MS spectrum prediction, thereby enhancing peptide-spectrum fitting and contributing to improved peptide detection sensitivity, making DeepMultiIon a powerful tool for advanced proteomics analysis. Peptide ; Spectral line ; Proteomics ; Spectrum (functional analysis) ; Peptide fragment Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Kazuma Isobe | Shota MORISHIGE | Taiyo Sato | Yutaka Yamada | Akihiko Horibe
Source: International Journal of Heat and Mass Transfer (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Improving thermal stability of a microcavity emitter for utilization under atmospheric environment With the development of micro-fabrication technology, various metamaterials with controlled emission spectra have been proposed as thermal emitters. However, general metamaterials have a risk of deformations and degradation at high temperatures in atmospheric conditions, which is inconvenient for use as a thermal emitter. In this study, we propose a concept to enhance the thermal durability of microcavity-type metamaterials. Although typical microcavities are entirely composed of metal to excite the resonance of electromagnetic waves, we assessed the feasibility of a microcavity consisting of silicon with minimal metal coatings. While usual metals are oxidized at high temperatures, gold is rarely oxidized due to its chemical stability. However, the gold layer deposited on the Si substrate has the potential to melt below 400 °C due to the formation of an Au-Si eutectic alloy, which has a much lower melting point than pure gold. Therefore, we focused on the gold-tungsten bilayer as a suitable metal coating for the silicon microcavity, thereby preventing oxidation and melting that would otherwise influence the emission spectra of the thermal emitter. The numerical analysis ensured that the proposed microcavity exhibited electromagnetic resonance, similar to that of a microcavity entirely composed of metal, unless the metal coating was too thin. The fabricated microcavity with the gold-tungsten coating also exhibited a thermal emission within a limited wavelength range, due to the microcavity resonance. Moreover, the heating experiment revealed that the microcavity with a gold-tungsten coating maintained its emissivity even when heated to 400 °C, which is higher than the oxidation point of tungsten and the melting point of the Au-Si eutectic alloy. Consequently, the gold-tungsten coating would be a reasonable approach to improve the stability of the microcavity-type metamaterial at high temperatures under oxidative conditions. • Microcavity-type metamaterial with enhanced thermal durability was proposed. • Proposed emitter was made of Si substrate and Au-W bilayer coating. • Au-W bilayer worked to prevent eutectic melting and oxidation of the emitter. • Proposed emitter kept its emissivity at 400 °C even under an oxidizing atmosphere. Common emitter ; Thermal ; Thermal stability ; Stability (learning theory) ; Thermophotovoltaic ; Thermal management of electronic devices and systems Silicon Nanostructures and Photoluminescence ; Heat Transfer and Boiling Studies ; Gas Sensing Nanomaterials and Sensors
Authors: Seezar Ibrahim Ali Al-Bayati | Ercan Aydoğmuş
Source: Polymers (article, 2026-02-26)
Field: Materials Science — Polymers and Plastics

Sustainable Epoxy Composites Filled with Natural Mineral Rocks: Comparative Evaluation of Mechanical, Thermal, and Dielectric Performance This study presents the fabrication and optimization of eco-efficient epoxy composites reinforced with ground natural stone fillers, namely pebble, sandstone, and marble, at loadings of up to 15.6 wt.%. Low content of a bio-based modifier, modified castor oil (MCO ≈ 0.5 wt.%), is incorporated to improve filler dispersion, processing behavior, and matrix-filler interfacial compatibility. The composites are designed to enhance mechanical, thermal, and dielectric performance using low-cost, abundant, and environmentally sustainable constituents. An experimental optimization approach is employed to evaluate and optimize bulk density, Shore D hardness, thermal conductivity, dielectric constant, and tensile strength. The results demonstrate that pebble-reinforced composites exhibit the highest tensile strength (≈30 MPa) and surface hardness (≈82 Shore D), which are attributed to the angular morphology and high intrinsic rigidity of pebble particles. Marble-filled systems show superior thermal stability, with residual mass increasing from approximately 2.5 wt.% for neat epoxy to over 11 wt.% at 550 °C, owing to the thermally stable calcium carbonate phase. In contrast, sandstone-reinforced composites exhibit the lowest dielectric constant (≈3.2), indicating enhanced electrical insulation capability. Fourier-transform infrared spectroscopy (FTIR) results confirm that the epoxy network structure is preserved upon filler incorporation, while MCO promotes improved interfacial interactions through physical interactions. Thermogravimetric analysis (TGA) and scanning electron microscopy (SEM) reveal enhanced thermal resistance, reduced microvoid formation, and improved filler-matrix adhesion at optimal filler contents of approximately 3.5 wt.%. Epoxy ; Dielectric ; Shore durometer ; Ultimate tensile strength ; Thermogravimetric analysis ; Dielectric loss ; Scanning electron microscope Natural Fiber Reinforced Composites ; Thermal properties of materials ; Epoxy Resin Curing Processes