jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Priyadeep Ghosh | Arun Sharma | Malay Yadav | Vinay Kumar Pandey
Source: Journal of Food Process Engineering (article, 2026-02-01)
Field: Chemistry — Analytical Chemistry

Acoustics‐Driven Artificial Intelligence Framework for Non‐Destructive Quality Estimation of Fruits: A Comprehensive Review ABSTRACT Quality estimation of agro‐food products is a top priority for producers, suppliers, and consumers. Non‐destructive techniques, particularly acoustic vibration methodology, have emerged as promising tools for the pre‐harvest and post‐harvest quality evaluation of agricultural products. This technique is effective in assessing the stiffness or firmness of fruits based on the mass and resonance frequency of the sample. This review explores the principles and applications of acoustic vibration methods in evaluating the quality of agro‐food products. Impact and forced excitation methods induce sample vibration, while contact and non‐contact sensors detect the resulting acoustic signals. These signals are processed to extract features that determine quality parameters. The review highlights the integration of artificial neural networks (ANN) and machine learning algorithms to enhance these assessments' accuracy, reliability, and efficiency. The acoustic vibration technique has the capability to evaluate several internal quality parameters such as texture, firmness, crunchiness, and crispiness. It is also useful for defect detection, sorting, grading, and assessing the ripeness of fruits and vegetables. Incorporating ANN and machine learning algorithms reduces human error and improves system reliability and speed. Additionally, this technique can indirectly measure soluble solid content, titratable acidity, and water activity in various commodities. Acoustic vibration technology holds tremendous potential for online quality detection of food products in an industrial context. However, further research is needed to develop cost‐effective and accurate devices to make this technology accessible to the agro‐food industry. Ripeness ; Vibration ; Artificial neural network ; Quality (philosophy) ; Reliability (semiconductor) ; Sample (material) Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Microwave and Dielectric Measurement Techniques
Authors: M. S. Zambare Gaikar Shubhangi | Dr. Kailash Sapnar A. D. Shaligram
Source: International Journal of Advanced Research in Science Communication and Technology (article, 2026-01-10)
Field: Materials Science — Biomaterials

Comprehensive Soil Characterization Using Spectroscopic and Microscopic Techniques Various analytical methods have been employed to elucidate the physical and chemical properties of soil.The study employed X-ray diffraction (XRD), Raman spectroscopy, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), and ultraviolet-visible spectroscopy (UV-Vis).Among these, X-ray diffraction (XRD) was chosen for this study because it provides information on the major crystalline minerals (e.g., quartz, feldspar, kaolinite, and montmorillonite), which are the primary factors influencing soil texture, cation exchange capacity, and water retention capacity.FTIR and Raman Spectroscopy revealed organic and clay minerals' molecular structures and functional groups (silicates, -OH, and carbonates).SEM and EDS are used to obtain data on specific spatial and microstructural properties. In addition to this, EDS helped to analyze the elemental composition of soils and many oxides, which are known to be the sources of major soil fertility and structural integrity, including pH, on which soil productivity depends, especiallySiO, AlO, MgO, and CaO. By means of ultraviolet- visible spectroscopy, the connections between the content and quality status of the soils, organic matter, and iron oxides have been determined. The combination of all these complementary analysis methods assisted in the complete description of the I/O properties of the soils. The paper demonstrates the importance of applying both destructive and non-destructive methods (especially those that can be implemented within a short period) to manage soils adaptively Raman spectroscopy ; Characterization (materials science) ; Spectroscopy ; Infrared spectroscopy ; Soil water ; Elemental analysis ; Scanning electron microscope ; Analytical Chemistry (journal) Clay minerals and soil interactions ; Soil Geostatistics and Mapping ; Soil and Unsaturated Flow
Authors: Rafał J. Kopiasz | Hadijatou Sallah | Robert J. Cavanagh | S J Jones | Cara Moloney | Lewis O'Shaughnessy | Phoebe McCrorie | Ala’ Salem | Hulya Bayraktutan | Aimée S. Jeluk | Luke A. Granger | Francois Hallac | Alison Ritchie | Shreeya Parmar | Anna M. Grabowska | Poulam Patel | Srinivasan Madhusudan | Ruman Rahman | Alan McIntyre | Robin J. Shattock | Charalampos Makatsoris | Roger J. Coulston | Andrew M. Howe | Pratik Gurnani | Snjezana Stolnik | John S. Tregoning | Cameron Alexander
Source: Advanced Materials (article, 2026-02-15)
Field: Chemistry — Organic Chemistry

Modular Supramolecular Polycations Enable Efficient Delivery of Diverse RNA Therapeutics and Vaccines We describe a modular, diverse, and customizable supramolecular-materials platform that can deliver nucleic acids in vitro and in vivo. The chemistries deployed enable the generation of multiple supramolecular polycations, which can associate with RNA to form polyelectrolyte complexes, but which have the unique feature of reversible cross-links, via host-guest interactions of monomers that display aromatic amino acid termini with cucurbit[8]uril (CB[8]). Families of supramolecular polymers can be prepared by simple variation in monomer structure, enabling the tuning of properties. We demonstrate that these supramolecular polyelectrolyte complexes with RNA can be prepared easily via automatable procedures to generate nanoparticles that meet Critical Quality Attributes for manufactured RNA vaccines and therapeutics. We show that these materials can deliver RNA to a range of cell types, displaying reporter-protein expression at levels equivalent to, or greater than, commercial transfection reagents, with no acute adverse phenotypic effects. Finally, we demonstrate the success of our materials platform across a range of nucleic acid types, with expression of mRNA within tumors of an orthotopic Triple-Negative Breast Cancer mouse model, knockdown of a kinase implicated in cancer progression via siRNA, and effective protection against H1N1 influenza virus challenge in mice following injections of self-amplifying RNA. Nucleic acid ; RNA ; Supramolecular chemistry ; Gene knockdown ; Monomer ; In vitro ; Supramolecular polymers ; Supramolecular assembly Supramolecular Chemistry and Complexes ; RNA Interference and Gene Delivery ; Supramolecular Self-Assembly in Materials
Authors: Yan Liang | Jiahang Qiu | Mu Zhang | Yudong Li | Xudong Sun | Dianning He | Xiaochen Wang | Hanhui Lei | Terence Xiaoteng Liu | Bowen Liu
Source: Advanced Composites and Hybrid Materials (article, 2026-03-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

One-dimensional well-defined peapod CoxNiy@C nanocomposite hollow nanorod towards enhanced electromagnetic performance The online version contains supplementary material available at 10.1007/s42114-026-01702-0. Microwave ; Polarization (electrochemistry) ; Impedance matching ; Conductivity ; Nanocomposite ; Absorption (acoustics) ; Reflection loss ; Nanorod Electromagnetic wave absorption materials ; Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies
Authors: Ganti Srikanth
Source: Tuijin Jishu/Journal of Propulsion Technology (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Thermodynamics Guided Machine Learning Models for CO₂ desublimation temperature prediction Accurate prediction of the carbon dioxide desublimation temperature (CDDT)—the threshold below which CO₂ transitions directly from vapor to solid phase—is critical for cryogenic carbon-capture systems, natural-gas processing, and pipeline safety. This paper presents a unified, three-tier study covering: a classical thermodynamic model based on the Peng–Robinson equation of state (PR EoS) solved via Lagrange’s analytically stable cubic solver; machine learning (ML) approaches including Decision Tree (DT), Gaussian Process Method (GPM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Genetic Programming (GP) applied to a consolidated dataset of 430 experimental measurements; and a physics-informed hybrid machine learning (PI-HML) framework that embeds fugacity equilibrium, Clausius–Clapeyron consistency, and monotonicity constraints directly into the learning objective. Together, these frameworks provide a complete, validated toolkit for managing CO₂ solidification in industrial cryogenic operations. Decision tree ; Process (computing) ; Gaussian process ; Pipeline (software) ; Inference ; Equation of state ; Thermodynamic process Machine Learning in Materials Science ; Chemical Looping and Thermochemical Processes ; Phase Equilibria and Thermodynamics
Authors: Zirui He | Shang-Peng Gao | Meng Chen
Source: ArXiv.org (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Exceptionally High Carrier Mobility in Hexagonal Diamond Hexagonal diamond (h-diamond), or Lonsdaleite, has been reported to be a wide-bandgap semiconductor with high thermal conductivity and hardness. Our \textit{ab initio} calculations reveal its exceptionally high carrier mobility at room temperature. Along $xy$ and $z$ directions, the hole mobilities are 5631 and 5552 cm$^{2}$V$^{-1}$s$^{-1}$, and the electron mobilities are 11462 and 28464 cm$^{2}$V$^{-1}$s$^{-1}$, respectively. These values are significantly superior to the mobility of most known semiconductors including cubic diamond. The small effective masses in h-diamond, comparable to those in cubic diamond, cannot explain its substantially higher mobility. Instead, two crucial mechanisms are uncovered: selection rules that considerably suppress hole scattering induced by transverse acoustic phonons, and a spatial decoupling effect where electronic wavefunctions concentrated in lattice interstitials lead to minimal overlap with scattering potentials. Scattering ; Electron mobility ; Diamond ; Semiconductor ; Phonon scattering ; Carrier scattering ; Thermal conduction ; Phonon ; Lattice (music) ; Electron Diamond and Carbon-based Materials Research ; Thermal properties of materials ; Electronic and Structural Properties of Oxides
Authors: Maria Angeliki Ntrivala | Evangelia Balla | Ermis P. Christodoulou | Margaritis Kostoglou | Panagiotis Klonos | Apostolos Kyritsis | Dimitrios N. Bikiaris
Source: Polymers (article, 2026-04-09)
Field: Materials Science — Polymers and Plastics

Sustained Delivery of Paliperidone Palmitate via Encapsulation in Bio-Based NIPU Nanoparticles In this study, Paliperidone Palmitate (PP), a second-generation antipsychotic, commonly used for the treatment of schizophrenia, was encapsulated in bio-based non-isocyanate polyurethane (NIPU) nanoemulsions. NIPU was synthesized via an isocyanate-free polyaddition route, addressing safety and environmental concerns associated with conventional polyurethanes. The drug-loaded nanoparticles were produced utilizing oil-in-water (O/W) emulsions followed by solvent evaporation and lyophilization. NIPU concentrations of 0.3% and 0.5% w/v, as well as 0.5% w/v PVA were employed, while PP was incorporated at 0.2%, 0.5% and 1% w/v. The formulations were characterized by FTIR, DSC and XRD analyses, and the mechanical strength of neat sponges was evaluated. The nanoparticle formation and size were assessed by DLS and SEM analyses. The water contact angle, porosity measurements and aquatic and enzymatic hydrolysis were additionally performed. The resulting nanocarriers exhibited controlled particle size, increased drug-loading values, structural stability and biodegradability. Lastly, the in vitro dissolution studies revealed a system-specific burst release behavior, and a controlled and sustained overall drug-release profile for majority of the formulations, thereby indicating the potential of NIPU nanocarriers for drug delivery applications, particularly where sustained therapeutic effects are required. Nanocarriers ; Nanoparticle ; Drug delivery ; Particle size ; Dissolution ; Solvent ; Hydrolysis ; Biocompatibility Polymer composites and self-healing ; Hydrogels: synthesis, properties, applications ; Pickering emulsions and particle stabilization
Authors: John Allan | Lisa J. K. Zillig | Simona Della Valle | Harrison Steel
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-18)
Field: Materials Science — Biomaterials

A Toolbox for Biomanufacturing of Functionalised PHA Nanoparticles with <i>C. necator</i> Abstract Microbes have the potential to manufacture plastics from sustainable feedstocks while enabling novel material properties and functions that are not easily accessible through conventional chemical synthesis. Realising this potential requires a comprehensive genetic and process engineering framework that spans chassis and bioprocess optimisation, polymer property control, and downstream functionalisation. Here we develop such a platform in Cupriavidus necator, with a focus on high-value polyhydroxyalkanoate (PHA) nanoparticles. To this end we first optimise the transformation protocol for the organism. Next, we create a library of PhaC synthase variants from C. necator , Aeromonas caviae and Brevundimonas sp. in a ΔphaC background, demonstrating that they allow customisation of the material properties of produced PHA particles. Our results combine data from Flow cytometry, Transmission Electron Microscopy (TEM), Fourier Transform InfraRed Spectroscopy (FTIR), and Differential Scanning Calorimetry (DSC) to show that it is possible to generate materials ranging from highly crystalline PHAs to softer P(3HB-co-3HHx) copolymers and that an A. caviae PhaC variant can double the yield of large PHA granules. To improve bioprocess sustainability, we coupled C. necator with B. subtilis in sucrose-fed co-cultures, using tetracycline tolerance differences and inoculation ratios to enhance PHA production from inexpensive, sugar-rich feedstocks. Finally, we add function to the produced PHA nanoparticles by using the molecular protein-fusion technology SpyTag-SpyCatcher, showing it is possible to efficiently capture SpyCatcher-GFP on PHA granules as a proof of concept for PHA’s use as a customisable bio-based nanoparticle. Together, our work offers an innovation to produce bio-PHA nanoparticles in a customisable way, with potential applications in sustainable biomanufacturing, biosensing, drug delivery and future bioremediation technologies. Bioprocess ; Cupriavidus necator ; Polyhydroxyalkanoates ; Bioplastic ; Biomanufacturing ; Synthetic biology biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Antimicrobial agents and applications
Authors: Tufail Hassan | Doyeon Lee | Shabbir Madad Naqvi | Myungjae Kim | Jung-Min Oh | Sang Woon Park | Aamir Iqbal | Soo Yeong Cho | Zhiwang Hao | Noushad Hussain | Zubair Khalid | Shakir Zaman | Xiangmeng Kong | Ki-Min Roh | Hanjung Kwon | Chong Min Koo
Source: Nano-Micro Letters (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Quantitative Defect–Property Correlations in Ti3C2Tx MXenes via Precursor-Controlled Defect Engineering Defect engineering holds great promise for tailoring the multifunctional properties of MXenes. However, quantitative correlations between defect and material performance remain largely unexplored due to the lack of a reliable strategy to precisely control defect densities. Here, we demonstrate that the defect density of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXenes-including titanium and carbon vacancies, substitutional oxygen defects, and the associated lattice strain-is precisely controlled by adjusting carbon stoichiometry during TiC precursor synthesis and aluminum content during Ti<sub>3</sub>AlC<sub>2</sub> MAX formation. The defect densities propagate from precursors to final MXenes, enabling the fabrication of a series of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXenes with systematically controlled defect densities. This allows a quantitative correlation between defect density and multifunctional properties including electrical and thermal conductivities, infrared emissivity, electromagnetic shielding effectiveness, Joule heating performance, and oxidation stability. The defect-minimized Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene exhibits outstanding performance, with an electrical conductivity of 26,000 S cm<sup>-1</sup>, thermal conductivity of 57 W m<sup>-1</sup> K<sup>-1</sup>, electromagnetic shielding effectiveness of 90.5 dB at 10 µm, Joule heating performance of 263 °C at 1.5 V, ultralow infrared emissivity of 0.05, and superior oxidation resistance (activation energy of 72 kJ mol<sup>-1</sup>). Furthermore, this work establishes a comprehensive quantitative framework linking defect structure to multifunctional performance and stability. MXenes ; Electromagnetic shielding ; Emissivity ; Fabrication ; Joule heating ; Thermal conductivity ; Infrared ; Electrical resistivity and conductivity ; Electrical conductor MXene and MAX Phase Materials ; Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies
Authors: Qinghua Peng | Yifang Hua | Jingjing Yang | Yujia Wang | Gehao Guo | Wanen Li | Jun Sun | Xiaoyu Gu | LI Jian-hua | Sheng Zhang
Source: Polymers (article, 2026-01-28)
Field: Materials Science — Polymers and Plastics

Construction of a P/N/Zn Synergist for Enhancing the Fire Safety and Char Formation of PA6/Aluminum Diethylphosphinate Composites Polyamide 6 is an important engineering thermoplastic; however, its practical use is often constrained by its high flammability. Although aluminum diethylphosphinate is widely employed as a flame retardant for polyamide 6, its relatively slow char-forming kinetics hinders the attainment of the stringent 750 °C glow-wire ignition temperature required for electrical applications at moderate loadings. To address this limitation, a synergist was fabricated via the self-assembly of phytic acid, benzoguanamine, and ZnSO<sub>4</sub>·7H<sub>2</sub>O and subsequently incorporated to enhance the char-forming capability and flame retardancy of polyamide 6/aluminum diethylphosphinate composites. The results revealed that the synergist acted as an efficient charring promoter, improving flame retardancy. At a total loading of 15 wt%, the composite reached a UL-94 V-0 rating and high limiting oxygen index of 30.7%. Cone calorimetry data indicate that the peak heat release rate decreased by 34.0%, and the smoke production rate decreased by 33.3% compared with the polyamide 6/aluminum diethylphosphinate composites. Mechanistic analysis indicated that the synergist catalyzed the carbonization of the polyamide 6, enabling the formation of a dense thermally insulating char barrier in the condensed phase. Notably, the optimized formulation achieved a glow-wire ignition temperature of 750 °C, demonstrating its strong potential for high-safety electrical applications. Polyamide ; Fire retardant ; Limiting oxygen index ; Charring ; Char ; Cone calorimeter ; Carbonization ; Combustion ; Smoke Flame retardant materials and properties ; Synthesis and characterization of novel inorganic/organometallic compounds ; Fire dynamics and safety research
Authors: Haiwei Wu | Xuexin Li | Jianwei Liu | Zhihao Wang | Yuchun Liu
Source: Molecules (article, 2026-02-23)
Field: Chemistry — Analytical Chemistry

Quantitative Analysis of Polyphenols in Lonicera caerulea Based on Mid-Infrared Spectroscopy and Hybrid Variable Selection <i>Lonicera caerulea</i> L. (blue honeysuckle) is rich in antioxidant polyphenols, and rapid and accurate determination of its polyphenol content is of great significance for functional food quality control. This study proposed a hybrid variable selection strategy designed for high-dimensional small-sample scenarios and developed a quantitative prediction model for polyphenol content based on mid-infrared (MIR) spectroscopy. A total of 191 <i>Lonicera caerulea</i> samples were collected from Northeast China, and 7468-dimensional spectral data were acquired using a Fourier transform infrared spectrometer. Polyphenol reference values were determined by the Folin-Ciocalteu method. Samples were divided into calibration (<i>n</i> = 152) and prediction (<i>n</i> = 39) sets using the SPXY algorithm. Among the 10 preprocessing methods evaluated, MSC combined with Savitzky-Golay first derivative achieved the best performance and was therefore used for subsequent modeling. The proposed hybrid variable selection method (VIP1.0&cap;RFR30%) intersected PLS variable importance in projection (VIP ≥ 1.0) with the top 30% important variables from random forest regression, selecting 984 key wavelengths and achieving 86.8% dimensionality reduction. A three-stage hyperparameter tuning strategy was implemented across four models (PLS, RFR, SVR, and XGBoost) to validate feature stability and control overfitting. The optimized XGBoost model achieved excellent performance on the independent test set (R2 = 0.92, RMSE = 0.098, RPD = 3.47). Compared with the classical CARS method (R2 = 0.78, RPD = 2.14), R2 improved by 16.3% and RPD improved by 55.2%. The results demonstrate that the proposed hybrid variable selection strategy can effectively address the challenges of high-dimensional MIR spectral data in small-sample modeling, providing a reliable tool for rapid and non-destructive quantitative analysis of polyphenols in <i>Lonicera caerulea</i>. Feature selection ; Partial least squares regression ; Polyphenol ; Calibration ; Selection (genetic algorithm) ; Variable elimination ; Stability (learning theory) ; Pattern recognition (psychology) ; Principal component analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Phytochemicals and Antioxidant Activities
Authors: Hojung Jung | Rodrigo Hormázabal | Jaehyeong Jo | Youngrok Park | Kyunggeun Roh | Se-Young Yun | Sehui Han | Dae-Woong Jeong
Source: ArXiv.org (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models Molecular generation with diffusion models has emerged as a promising direction for AI-driven drug discovery and materials science. While graph diffusion models have been widely adopted due to the discrete nature of 2D molecular graphs, existing models suffer from low chemical validity and struggle to meet the desired properties compared to 1D modeling. In this work, we introduce MolHIT, a powerful molecular graph generation framework that overcomes long-standing performance limitations in existing methods. MolHIT is based on the Hierarchical Discrete Diffusion Model, which generalizes discrete diffusion to additional categories that encode chemical priors, and decoupled atom encoding that splits the atom types according to their chemical roles. Overall, MolHIT achieves new state-of-the-art performance on the MOSES dataset with near-perfect validity for the first time in graph diffusion, surpassing strong 1D baselines across multiple metrics. We further demonstrate strong performance in downstream tasks, including multi-property guided generation and scaffold extension. Graph ; ENCODE ; Diffusion ; Encoding (memory) ; Graph theory ; Topology (electrical circuits) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods