jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lorenzo Cracco | Roberto Penasa | Paolo Zardi | Giulia Licini | Manuel Orlandi | Cristiano Zonta
Source: Organic Chemistry Frontiers (article, 2025-10-14)
Field: Chemistry — Spectroscopy

A unified predictive model for chiroptical sensing: a substrate-centric approach to predicting circular dichroism outputs across two chemically distinct organic classes A unified predictive model, developed using a substrate-centric approach, enables rapid and reliable CD analysis of amides and carboxylates, providing a novel method towards a universal platform for high-throughput enantiomeric excess determination. Circular dichroism ; Enantiomer ; Enantiomeric excess ; Vibrational circular dichroism ; Chiral stationary phase Molecular spectroscopy and chirality ; Molecular Sensors and Ion Detection ; Computational Drug Discovery Methods
Authors: Ali A. Yousif | Mohamad H. H. Deifalla | Ramadan Mohmmed
Source: Global Journal of Engineering and Technology Advances (article, 2025-09-27)
Field: Chemistry — Analytical Chemistry

Non-Catalytic Staged Thermal Pyrolysis of Polypropylene and High-Density Polyethylene for Fuel-Range Hydrocarbon Production The conversion of waste plastics into liquid fuels via thermal pyrolysis presents a sustainable solution to both energy scarcity and plastic waste management. This study investigates the staged non-catalytic pyrolysis of polypropylene (PP), high-density polyethylene (HDPE), and their binary blends to evaluate liquid fuel yields, char formation, and the influence of feedstock composition on product distribution. Experiments were conducted in a 1.0 L semi-batch reactor at 450 °C (Stage A) with secondary cracking of waxes at 500 °C (Stage B). Product analysis via GC–FID quantified gasoline-, kerosene-, diesel-, and heavy-oil-like fractions. Results demonstrate that HDPE produces the highest liquid yield (39.05 g) with minimal char (0.77 g), whereas PP favors gas and char formation due to random chain scission. Binary blends exhibit synergistic behavior, where HDPE-rich mixtures (30% PP–70% HDPE) maximize liquid recovery (36.81 g) while moderating char formation, and PP-rich mixtures benefit from secondary cracking to enhance oil yield (25.1 g). Stage B pyrolysis substantially increased liquid fractions across all formulations, with negligible char generation. Fuel-range composition analysis revealed that PP favors lighter hydrocarbons (gasoline 32%), whereas HDPE yields heavier fractions (diesel 33.5%, heavy oil 26%), with intermediate profiles observed in blends. These findings highlight the tunable nature of co-pyrolysis for optimizing liquid fuel yield and quality, providing a viable route for converting heterogeneous plastic wastes into transportation and industrial fuels. Pyrolysis ; Char ; Polypropylene ; Polyethylene ; High-density polyethylene ; Pyrolysis oil ; Cracking ; Raw material ; Polyolefin ; Yield (engineering) Petroleum Processing and Analysis ; Thermochemical Biomass Conversion Processes
Authors: Inken Fender | Jannik Adrian Gut | Thomas Lemmin
Source: Journal of Cheminformatics (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Beyond performance: how design choices shape chemical language models Chemical language models (CLMs) have shown strong performance in molecular property prediction and generation tasks. However, the impact of design choices, such as molecular representation format, tokenization strategy, and model architecture, on both performance and chemical interpretability remains underexplored. In this study, we systematically evaluate how these factors influence CLM performance and chemical understanding. We evaluated models through fine-tuning on downstream tasks and probing the structure of their latent spaces using probing predictors, vector operations, and dimensionality reduction techniques. Although downstream task performance was similar across model configurations, substantial differences were observed in the structure and interpretability of internal representations, highlighting that design choices meaningfully shape how chemical information is encoded. In practice, atomwise tokenization generally improved interpretability, and a RoBERTa-based model with SMILES input remains a reliable starting point for standard prediction tasks, as no alternative consistently outperformed it. These results provide guidance for the development of more chemically grounded and interpretable CLMs. Interpretability ; Representation (politics) ; Chemical space ; Lexical analysis ; Task (project management) ; Language model ; Curse of dimensionality ; Point (geometry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Wafaa Mahdi Alkoofee | Zainab Y. Kadhim
Source: Oriental Journal Of Chemistry (article, 2025-12-30)
Field: Chemistry — Organic Chemistry

Synthesis, Characterization, of Novel Pyrazolo [3,4-d]Pyrimidine Derivatives, Via Microwave Irradiation and Study Anti-Tumor Activity by Molecular Docking. Cancer comprises a wide spectrum of disorders characterized by uncontrolled cellular growth and the capacity of cancer cells to infiltrate and damage surrounding tissues. The increasing global incidence of cancer has intensified the search for new therapeutic agents with improved efficacy. In the present work, a new series of heterocyclic derivatives incorporating the structure based on pyrazolo.[3,4-d]pyrimidine scaffold was prepared through microwave methodology. The obtained compounds had their chemical structure successfully established and concluded C.H.N, FT-IR, NMR(¹H.and¹³C), and mass spectrometric analyses, in addition to evaluating their physical properties. The anticancer potential of the prepared derivatives was assessed toward the (MCF-7).breast carcinoma cell line using the MTT.assay. Among the compounds, Z₂ displayed the strongest growth-inhibitory effect. Hemolysis testing further revealed low percentages of red blood cell lysis at 10 mg/mL (4.05% for Z₂ and 4.18% for Z₁), indicating that both compounds exhibit acceptable biological safety. The free-radical-scavenging ability of the primed compounds was inspected using the DPPH-based antioxidant assay, and the findings confirmed that Z₂ possesses higher radical-scavenging activity compared with Z₁. Overall, the prepared obtained pyrazolo[3,4-d]pyrimidine-based derivatives exhibited notable anticancer as well as antioxidant properties, suggesting their potential as lead structures for further development. Hemolysis ; Lysis ; Elsevier Biobase ; Material chemistry ; Antioxidant ; Microwave irradiation ; Supramolecular chemistry ; Cell culture ; Theoretical chemistry Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles ; Microwave-Assisted Synthesis and Applications
Authors: Tengfei Yin | Song Meng | Yang Zhao | Tao Zhang | Fuhui Wang
Source: npj Materials Degradation (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Establishing a pre-passivation method to improve the corrosion resistance of B30 alloy in deep-sea environment The response surface methodology (RSM) was used to investigate the influence of benzotriazole (BTA), sulfosalicylic acid (SSA), H2O2, and temperature on the corrosion resistance of pre-passivation film formed on copper. The optimal pre-passivation process was predicted: BTA of 14–16 g/L, SSA of 2–2.3 g/L, H2O2 of 10–11 ml/L, sodium dodecylsulfate of 0.5 g/L and temperature of 45–50 °C. An improved pre-passivation treatment suitable for B30 alloy, using H3PO4 instead of SSA, showed good corrosion resistance; the polarization resistance increased nearly 100 times in its ‘infancy’. In the 30 days of corrosion test, the localized corrosion was significantly inhibited, thus prolonging the service life of the heat-transfer tube. Corrosion ; Benzotriazole ; Polarization (electrochemistry) ; Alloy ; Nickel alloy ; Service life Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Electrodeposition and Electroless Coatings
Authors: Dajiang Guo
Source: Economics and Management Innovation (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Research on Digital Overall Solution and Transformation Path of Fine Chemical Industry The fine chemical industry, characterized by complex multi-step processes and high-value products, faces challenges in efficiency, quality, and sustainability under traditional production methods. This review examines the application of digital technologies—including Industrial Internet of Things (IIoT), big data analytics, artificial intelligence, cloud and edge computing, and automation and robotics—in addressing these challenges. It highlights the benefits of digital transformation for process optimization, quality enhancement, cost and energy reduction, safety improvement, and environmental sustainability. A stepwise transformation roadmap, from basic automation and data collection to intelligent optimization and predictive operations, is proposed to guide enterprises through effective digitalization. Case studies and quantitative data illustrate the tangible outcomes of digital adoption, while future trends emphasize AI-driven autonomous production, integration with green chemistry, cross-industry collaboration, and platform economies. This review provides actionable insights for policymakers, managers, and engineers aiming to achieve sustainable growth and competitive advantage in the fine chemical industry through comprehensive digital solutions. Automation ; Digital transformation ; Process (computing) ; Cloud computing ; Sustainability ; Chemical industry ; Industry 4.0 ; Big data ; Quality (philosophy) Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Digital Transformation in Industry
Authors: Thanh Mai Nguyen Tran | Quang Duy Tran | M. N. Prabhakar | Xiem Nguyen Thang | Jung‐il Song
Source: Discover Materials (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Proof-of-concept ensemble machine-learning forecasting of tensile properties in hybrid polypropylene composites reinforced with flax, basalt, and rice husk powder Abstract Accurate forecasting of tensile properties is important for efficient design of eco-friendly composites. We present a proof-of-concept ensemble workflow to predict tensile strength and tensile modulus of hybrid polypropylene (PP) composites reinforced with long flax fiber bundles, basalt fibers (BF), and rice husk powder (RHP). A lab-scale dataset ( n = 65) was generated under standardized testing. Preprocessing (Savitzky–Golay denoising, feature standardization) preceded Optuna-tuned support vector regression (SVR) and XGBoost, whose predictions were combined via a stacked linear meta-learner. Under ten-fold cross-validation, the ensemble achieved R 2 = 0.881 (RMSE = 0.639 GPa) for modulus and R 2 = 0.907 (RMSE = 1.569 MPa) for strength. The framework is a computationally efficient complement to simulation-based analyses for early-stage screening within the explored domain. This is a proof-of-concept study based on a small, single-lab dataset ( n = 65) without external validation; future work will enlarge the dataset across laboratories, extend to additional properties, and incorporate independent validation. Within the explored domain, the workflow yields actionable composition windows (e.g., 30–35 wt% BF, 4–6 wt% RHP, 3–5 flax plies) that balance stiffness and strength. Ultimate tensile strength ; Polypropylene ; Preprocessor ; Fiber ; Workflow ; Stiffness ; Modulus ; Husk Machine Learning in Materials Science ; Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites
Authors: Rowan Romeyn | Samuel Ortega | Karsten Heia
Source: Journal of Spectroscopy (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging of Blood Through Fish Skin Using a Neural Network Machine Learning Approach We propose a novel model for determining residual blood concentration in both cod fillets and whole fish using interactance hyperspectral imaging. The amount and distribution of residual blood in fish muscle is an important quality parameter. Improving the ability to assess quality rapidly, automatically, and noninvasively can strengthen the ex‐vessel fishing market, incentivizing practices that result in good quality, thereby reducing food waste and economic losses in the form of reduced quality. Our method employs a fully connected neural network operating pixelwise on hyperspectral images, trained on controlled experiments with known blood concentrations imaged with and without cod skin. The partial dependence of the neural network was estimated as a function of wavelength and corresponds with the well‐known absorption spectra of hemoglobin and water. Robust performance was demonstrated on real‐world data from a commercially available hyperspectral imaging system, and the model’s sensitivity to blood through skin was demonstrated by comparing predictions for whole fish with those for fillets with skin removed. This study underscores the value of controlled experiments in constraining flexible nonlinear models like neural networks, efficiently addressing complex issues such as light transport through fish skin. Our model extends the capability of hyperspectral imaging for automated quality assessment for fillets as well as gutted whole fish, which is highly relevant to the whitefish processing industry. Hyperspectral imaging ; Artificial neural network ; Residual ; Pattern recognition (psychology) ; Fish <Actinopterygii> ; Fish fillet ; Convolutional neural network Spectroscopy and Chemometric Analyses ; Water Quality Monitoring Technologies ; Optical Imaging and Spectroscopy Techniques
Authors: Ritesh Singh | Pavar Sai Kumar | Khairunnisa Amreen | Satish Kumar Dubey | Sanket Goel
Source: ASME journal of micro and nano science and engineering. (article, 2025-06-13)
Field: Chemistry — Electrochemistry

Zinc Oxide/Laser-Induced Graphene Matrix Electrochemical Impedance Spectroscopy Assisted Toluene Detection in Liquid Phase Abstract The effect of toluene on the environment needs to be studied as it may cause problems for aquatic life, agriculture, and human health. Monitoring the toluene level is necessary to take the necessary steps. A flexible working electrode was modified with the metal oxide nanoparticle (ZnO) as an active site for electrochemical activity. The material characterization was carried out with scanning electron microscopy (SEM), energy dispersive X-ray (EDX) analysis, and X-ray diffraction (XRD). The electrochemical impedance spectroscopy (EIS) method was used for the detection and optimization of the sensor, and the constant phase elements (CPEs) were analyzed. Furthermore, the concentration effect was analyzed between a linear range of 100 nM and 0.1 mM. The sensor showed good linearity with a limit of detection (LoD) of 97.2 nM and a limit of quantification (LoQ) of 252.36 nM. The interference study used the equivalent circuit parameter with constant phase element analysis. The surface behaves as the constant phase element instead of double-layer capacitance. The real sample analysis was carried out using tap water. The fabricated electrode showed good selectivity and appreciable recovery values in real-time samples. Owing to the conclusions drawn, the fabricated electrode is expected to show a great potential for real-time application. In further, volatile organic compounds (VOCs) being sensitive to environment, this study is a prototype for development of miniaturized sensors for VOCs monitoring. Graphene ; Dielectric spectroscopy ; Toluene ; Materials science ; Oxide ; Spectroscopy ; Electrochemistry ; Phase (matter) ; Electrical impedance ; Zinc ; Laser ; Matrix (chemical analysis) ; Analytical Chemistry (journal) ; Chemistry ; Nanotechnology ; Optics ; Chromatography ; Organic chemistry ; Electrode ; Composite material ; Physical chemistry ; Metallurgy ; Physics Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: João P. C. Oliveira | Murillo H. Queiroz | Patricio F. Provasi | Roberto Rivelino
Source: Research Square (preprint, 2025-06-05)
Field: Chemistry — Physical and Theoretical Chemistry

A Nuclear Chemical Descriptor Based on NMR Databases for Molecular Information: Characterization of π-Conjugated Oligomers Conjugated system ; Characterization (materials science) ; Chemistry ; Chemical shift ; Database ; Materials science ; Combinatorial chemistry ; Computer science ; Nanotechnology ; Organic chemistry ; Physical chemistry ; Polymer Various Chemistry Research Topics
Authors: Еvgeny V. Nazarchuk | Oleg I. Siidra | Dmitri N. Dmitriev | Dmitri O. Charkin
Source: Zeitschrift für Kristallographie - Crystalline Materials (article, 2025-04-10)
Field: Chemistry — Inorganic Chemistry

A novel microporous uranyl silicate prepared by high temperature flux technique Abstract A new uranyl silicate [(K,Na) 4 Cl 2 ][(UO 2 ) 6 (Si 2 O 7 ) 2 F 2 ] ( 1 ) was obtained from melt in evacuated silica tubes. The crystal structure of 1 is orthorhombic, Pnma , a = 36.8677(7), b = 7.7946(2), c = 19.1931(4) Å, V = 5,515.5(2) Å 3 . It represents a microporous framework comprised of Ur O 5 and Ur O 4 F pentagonal bipyramids and Si 2 O 7 disilicate groups. The framework contains two types of channels with the size of 8.14 × 8.66 and 6.38 × 4.42 Å 2 ; the smaller ones host the potassium cations while the larger ones contain more complex alkali-chloride species. Microporous material ; Silicate ; Uranyl ; Flux (metallurgy) ; Materials science ; Geochemistry ; Chemical engineering ; Mineralogy ; Geology ; Metallurgy ; Uranium ; Composite material ; Engineering Radioactive element chemistry and processing ; Nuclear materials and radiation effects ; Nuclear Materials and Properties
Authors: Albin Lahu | Shaolin Wu | Maximilian Schuler | Francesca Mazzotta | Ardit Ramadani | Emirhan Koca | Ingo Lieberwirth | Katharina Landfester | Torsten John | David Y. W. Ng | Tanja Weil
Source: Angewandte Chemie International Edition (article, 2025-12-04)
Field: Materials Science — Biomaterials

Co‐Assemblies Regulate the Catalytic Activity of Peptide Fibrils Short peptide sequences self-assemble into supramolecular structures through intermolecular interactions, creating a microenvironment in which chemical reactions can be catalyzed. In recent years, many peptide sequences have shown to demonstrate catalytic activity upon nanostructure formation, but the engineering of the catalytic microenvironment through co-assembly strategies have not been explored. We introduce a peptide sequence that gains retro-aldolase activity upon assembly to supramolecular peptide fibrils in aqueous buffer solution (pH 7.4). The catalytic activity is first optimized through synthetic sequence variation and the structure formation properties of the peptides are characterized. Co-assembly with inactive peptide sequences enables the up- or downregulation of the catalytic activity over a dynamic range, by modulating the likelihood for substrate interaction and thus the distance of the substrate to the nucleophilic lysine at the active site. It is observed that co-assemblies with positively charged sequences increase activity, whereas negatively charged peptide sequences decrease activity. We show that the emerging field of peptide-based catalysts can be further advanced by the engineering of the catalytic domain using heterogeneous supramolecular assembly. Peptide ; Supramolecular chemistry ; Catalysis ; Substrate (aquarium) ; Sequence (biology) ; Peptide sequence ; Lysine ; Fibril Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Origins and Evolution of Life
Authors: Jhionathan de Lima | Cristiano F. Woellner
Source: ArXiv.org (preprint, 2025-10-22)
Field: Materials Science — Materials Chemistry

Hexa-Graphyne: A Transparent and Semimetallic 2D Carbon Allotrope with Distinct Optical Properties Herein, we conduct a comprehensive investigation of Hexa-graphyne (HXGY), a planar carbon allotrope formed by distorted hexagonal and rectangular rings incorporating sp and sp$^2$-hybridized carbon atoms. First-principles calculations confirm its energetic, dynamical and thermal stability (up to at least 1000 K). Regarding its band structure, this material exhibits a semimetallic nature. It exhibits high mechanical compliance, with a Young's modulus approximately 13 times lower and a Poisson's ratio nearly 4 times higher than those of graphene. The optical response is marked by strong ultraviolet absorption, high infrared reflectivity, and pronounced transparency in the visible-light range. Raman and infrared spectra exhibit sharp and well-separated peaks, providing a clear signature of acetylenic linkage stretching vibrations. Nanoribbon structures derived from HXGY show distinct electronic behaviors depending on the edge termination type and width. These findings highlight the HXGY potential for nanoelectronic and optoelectronic applications. Raman spectroscopy ; Infrared ; Thermal stability ; Ultraviolet ; Planar ; Carbon fibers ; Thermal ; Infrared spectroscopy ; Hexagonal crystal system Graphene research and applications ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research
Authors: Shuo Yang | Yanqiu Ma | Yujie Chi | Yuan Chi
Source: Comprehensive Reviews in Food Science and Food Safety (review, 2025-10-30)
Field: Chemistry — Analytical Chemistry

Egg Freshness Safety and Detection Techniques: A Comprehensive Review and Future Perspective Globally, annual egg production has steadily increased, and liquid eggs are gradually replacing shell eggs, with the potential to become the dominant form of future egg consumption. However, current freshness evaluation systems mainly focus on shell eggs, with almost no global standard for liquid eggs. Beginning with deterioration mechanisms of egg freshness, this review systematically summarizes traditional and novel techniques for egg freshness detection, covering principles, attributes (accuracy, cost, model, duration, sample integrity), and potentials (online and automated detection), with particular emphasis on applicability to shell eggs and liquid eggs. Among traditional techniques, Haugh unit and candling are authoritative but limited to shell eggs. Among novel techniques, Raman spectroscopy has proven effective for evaluating liquid egg yolk freshness, and electrochemical-chemometric detection has been applied to liquid egg white. In addition, low-field nuclear magnetic resonance characterizes water distribution in liquid eggs, olfactory sensors monitor volatile compounds during storage, and machine vision and microwave near-field imaging also show potential for evaluating liquid egg freshness. Meanwhile, all above techniques are nondestructive and show strong potential for online, continuous, and automated detection. For modeling: deep learning, machine learning, and stacking ensemble strategies have enabled hyperspectral imaging and dielectric spectroscopy to achieve 100% accuracy. In context of Food Industry 4.0, future research should integrate diverse models with promising techniques to enhance applicability to both shell and liquid eggs and improve practicality, thereby providing critical references for establishing global standards on liquid egg freshness. Context (archaeology) ; Perspective (graphical) ; Yolk ; Shell (structure) ; Hyperspectral imaging ; Liquid water Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Animal Nutrition and Physiology