jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Negese Alemu Tassew | Tebelay Liknaw Andualem | D.M. Reddy Prasad | Meketaye Abera Endeshaw | R. Ramesh
Source: Scientific Reports (article, 2025-11-20)
Field: Materials Science — Biomaterials

Avocado seed starch bioplastic film reinforced with enset cellulose for sustainable packaging The increasing environmental concerns associated with conventional petroleum-based plastics have driven the urgent need to develop sustainable, biodegradable alternatives for packaging applications. This research explores the development of an environmentally friendly and strong bioplastic film. It's made from avocado seed starch and strengthened with cellulose fibers obtained from the enset pseudostem, offering a potential solution to the environmental problems linked to traditional plastic materials. A Central Composite Design (CCD) was employed to optimize key processing parameters: drying temperature (30-70 °C), glycerol concentration (0.1-0.4 mL/g), and starch-to-cellulose ratio (60:40-90:10%w). The optimal formulation, achieved at a drying temperature of 49 °C, glycerol concentration of 0.23 mL/g, and a starch-to-cellulose ratio of 77:23%w, resulted in a bioplastic film with a tensile strength of 10.49 MPa, Young's modulus of 265.93 MPa, elongation at break of 8.73%, and 41.37% weight loss after two weeks of soil burial. Structural and physicochemical analyses, including FTIR, SEM, DSC, XRD, and RVA, revealed enhanced intermolecular hydrogen bonding, increased crystallinity (~ 26%), and improved thermal stability, suggesting strong molecular interactions between starch and cellulose. The optimized film exhibited favorable barrier properties, with moisture content of 14.3%, solubility of 16.8%, and water vapor permeability of 2.76 × 10⁻¹⁰ g/Pa·s·m. Furthermore, the films showed increased opacity and compact morphology, making them suitable for light-sensitive packaging applications. The developed composite film is a high-performance, eco-friendly alternative to conventional plastics, particularly in food packaging and other single-use plastic products. Bioplastic ; Starch ; Crystallinity ; Cellulose ; Food packaging ; Ultimate tensile strength ; Glycerol ; Moisture Nanocomposite Films for Food Packaging ; Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies
Authors: Ashley Lim | Maria Ho | Yujie Ke | Wei Wei Loh | Zhaogang Dong | Fuke Wang | Jason Y. C. Lim | Xin Ting Zheng | Le Yang | Yuwei Hu
Source: Soft Matter (article, 2025-01-01)
Field: Materials Science — Biomaterials

A cofactor mediated supramolecular oligo-adenine triplex for reprogrammable macroscopic hydrogel assembly Noncanonical DNA structures mediated by low-molecular-weight cofactors significantly enrich the arsenal of the DNA toolbox and expand its functional applications. In this study, cyanuric acid (CA), a cofactor with three thymine-like edges, is employed to assemble adenine-rich strands (A-strands) into a parallel noncanonical A-CA triplex configuration through Watson-Crick and Hoogsteen interactions. This assembly occurs at a system pH value below the p<i>K</i><sub>a</sub> of the CA cofactor (6.9), where CA is protonated, while its deprotonation at higher pH levels leads to the dissociation of the A-CA triplex into single A-strands and free CA cofactors. The structural transition is fully pH reversible. The A-CA triplex is further utilized as a crosslinking element for reprogrammable macroscopic object assembly, exemplified by hydrogel cubes (5 × 5 × 5 mm), a topic that has been less explored compared to nano- and microscopic constructs. Controlled, modular assembly and disassembly of various configurations, such as square, line, and T-shape, are demonstrated through reversible pH adjustments. This strategy offers a streamlined approach using a single DNA sequence and cofactor for hydrogel modification and complex construction, providing cost-effective, recyclable, and stimuli-responsive functionality, which inspires the development of versatile and adaptive supramolecular systems in chemistry and materials science. Cofactor ; Supramolecular chemistry ; Thymine ; Chemistry ; DNA ; Combinatorial chemistry ; Biophysics ; Nanotechnology ; Biochemistry ; Molecule ; Materials science ; Biology ; Organic chemistry ; Enzyme Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; DNA and Nucleic Acid Chemistry
Authors: Keith R. Fratus | Nicklas Enenkel | Sebastian Zanker | Jan-Michael Reiner | Michael Marthaler | Peter Schmitteckert
Source: ArXiv.org (preprint, 2025-08-08)
Field: Chemistry — Spectroscopy

Can a Quantum Computer Simulate Nuclear Magnetic Resonance Spectra Better than a Classical One? The simulation of the spectra measured in nuclear magnetic resonance (NMR) spectroscopy experiments is a computationally non-trivial problem which, due to its natural interpretation as a quantum spin problem, maps in a straightforward way to a quantum computer. As such, it represents a problem for which such a device may provide some practical advantage over traditional computing methods. In order to understand the extent to which such problems may indeed provide examples of useful quantum advantage, it is important to understand the limitations of existing classical simulation methods. In this work, we benchmark our own classical solver designed to study such problems. This solver uses a clustering approximation to achieve a resource scaling which is linear in the total number of nuclear spins in a given molecule, for a fixed cluster size. The success of such an approximation would present a stark repudiation to the common claim that such problems require an exponential scaling of resources, the very claim which makes simulating an NMR spectra a candidate for quantum advantage. Our benchmarking results indicate that our approximation performs well throughout, and even somewhat beyond, the more typical experimental regimes. We discuss what implications this may have for future efforts to demonstrate quantum advantage in the context of NMR. Quantum computer ; Quantum ; Context (archaeology) ; Scaling ; Spins ; Spectral line ; Benchmark (surveying) ; Solver ; Quantum algorithm Advanced NMR Techniques and Applications
Authors: Sofiane Ferchichi | Nida Sheibat‐Othman | Maud Rey‐Bayle | Vincent Monteil
Source: Analytica Chimica Acta (article, 2025-10-07)
Field: Chemistry — Analytical Chemistry

Removal of solvent effect from Raman and NIR spectra for real-time monitoring of dissolved polypropylene content across multi-solvent systems during dissolution-based recycling Polypropylene ; Dissolution ; Raman spectroscopy ; Solvent ; Analytical Chemistry (journal) ; Polymer ; Orthogonalization Spectroscopy and Chemometric Analyses ; Recycling and Waste Management Techniques ; Water Quality Monitoring and Analysis
Authors: Van Nghia Ha | Young-Woo Youn | Hyeon-Soo Choi | Hong Nhung Nguyen | Yong‐Hwa Kim
Source: Energies (article, 2025-06-11)
Field: Materials Science — Materials Chemistry

Self-Supervised Asynchronous Federated Learning for Diagnosing Partial Discharge in Gas-Insulated Switchgear Deep learning-based models have achieved considerable success in partial discharge (PD) fault diagnosis for power systems, enhancing grid asset safety and improving reliability. However, traditional approaches often rely on centralized training, which demands significant resources and fails to account for the impact of noisy operating conditions on Intelligent Electronic Devices (IEDs). In a gas-insulated switchgear (GIS), PD measurement data collected in noisy environments exhibit diverse feature distributions and a wide range of class representations, posing significant challenges for trained models under complex conditions. To address these challenges, we propose a Self-Supervised Asynchronous Federated Learning (SSAFL) approach for PD diagnosis in noisy IED environments. The proposed technique integrates asynchronous federated learning with self-supervised learning, enabling IEDs to learn robust pattern representations while preserving local data privacy and mitigating the effects of resource heterogeneity among IEDs. Experimental results demonstrate that the proposed SSAFL framework achieves overall accuracies of 98% and 95% on the training and testing datasets, respectively. Additionally, for the floating class in IED 1, SSAFL improves the F1-score by 5% compared to Self-Supervised Federated Learning (SSFL). These results indicate that the proposed SSAFL method offers greater adaptability to real-world scenarios. In particular, it effectively addresses the scarcity of labeled data, ensures data privacy, and efficiently utilizes heterogeneous local resources. Switchgear ; Partial discharge ; Asynchronous communication ; Asynchronous learning ; Computer science ; Electrical engineering ; Engineering ; Reliability engineering ; Telecommunications ; Voltage ; Mathematics ; Mathematics education High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Beata Polak | Adrianna Kryska | Kamila Jaglińska | Adam Traczuk | Barbara Barwinek
Source: Scientific Reports (article, 2025-04-28)
Field: Chemistry — Spectroscopy

High performance thin layer chromatography with eluents containing SDS aided by UV- and Raman spectroscopy The modification of non-polar silica gel plates by aqueous-organic solvent containing sodium dodecyl sulphate was investigated. The changes that occurred in the sorbent were examined using the Raman spectroscopy technique. The abovementioned technique was also involved in the investigation of carbamazepine-SDS complexes. The approach of applying the Raman spectroscopy into the investigation of the chromatographic behavior of SDS, carbamazepine and RP-18 W sorbent was presented for the first time. The effect of sodium dodecyl sulphate, SDS, concentration on the chosen drug used in neurodegenerative disorder (sulpiride, olanzapine, carbamazepine, trazodone, clomipramine, and pridinol) retention has been investigated. Such examination for these medicines has been presented for the first time for thin-layer chromatography system. Modified the mobile phase composition has resulted in the separation of the mixture containing all compounds. To confirm micellar forming, critical micelle concentration for SDS in the applied mobile phase was determined using two techniques, i.e., conductometric and spectrophotometric with azorubine as the indicator. The effect of surfactant on the solute zone shape by means of tailing and asymmetry factors has been determined. For four of the six investigated compounds, these factors have been close to 1.0. The separation efficiency measured by the height of the theoretical plate varies from 39 to 73 μm depending on the solute investigated. The method has been optimised regarding the quantitative analysis of investigated compounds, opening up new possibilities for drug analysis. In terms of the LOD values are in the range of 0.22 µg/spot to 1.67 µg/spot, whereas LOQ values are in the range of 0.66 µg/spot to 5.07 µg/spot for olanzapine and trazodone, respectively. Furthermore, the method was also applied for the quantitative analysis of the investigated compounds in pharmaceutical preparations. Such proceedings for RP-18 W TLC system containing SDS was shown for the first time. The new method has better or comparable concentration ranges regarding some of investigated compounds to those presented previously. Additionally, for the first time, a strategy involving various techniques (TLC chromatography supported by UV- and Raman spectroscopy and conductometry) for complex analysis of the separation system was presented, inspiring potential future research directions. Raman spectroscopy ; Chromatography ; Thin-layer chromatography ; Ultraviolet visible spectroscopy ; Chemistry ; Spectroscopy ; Materials science ; Optics ; Organic chemistry ; Physics Analytical Chemistry and Chromatography ; Analytical chemistry methods development ; Spectroscopy and Chemometric Analyses
Authors: Hirokazu Makuta | H. Machida | Teruhiko Fujiwara
Source: MATERIALS TRANSACTIONS (article, 2025-01-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Coercivity and Microstructure Dependence on B Content in ThMn&lt;sub&gt;12&lt;/sub&gt;-Type Sm-Zr-Fe-Co-Ti-Cu-B Alloys The effect of B content was studied on the crystalline structures, magnetic properties, and microstructures of (Sm0.80Zr0.20)8.6(Fe0.71Co0.20Ti0.08Cu0.01)91.4−xBx (x = 0.8, 2.0, 5.0, and 8.0) melt-spun alloys with a ThMn12-type structure (1–12 phase) to achieve high saturation magnetization (μ0Ms) and large intrinsic coercivity (Hcj). With increasing x, the amorphous forming ability improved in the as-quenched alloys, and the average crystallite size decreased in the annealed alloys. The x = 5.0 alloy annealed at 1173 K for 60 min exhibited the largest Hcj of 454 kA/m and the μ0Ms was estimated to be 1.29 T. In addition, the x = 5.0 alloy exhibited demagnetization curve without a two-step shape, which indicates a low volume fraction of soft magnetic phases. The scanning transmission electron microscopy revealed that 1–12 phase grains have sizes in the range of 30–100 nm in the x = 5.0 alloy after annealing at 1173 K. Fine Ti-B precipitates were also observed. The reduction in the 1–12 phase grain size is attributed to the suppression of crystal nucleation and the crystal growth caused by the amorphous formation in the as-quenched alloy, and the grain boundary pinning by the Ti-B inhibiting the grain growth. The grain sizes in the x = 5.0 alloy were smaller than the estimated critical single-domain diameter of 190 nm, which contributes to the increase in Hcj. Materials science ; Microstructure ; Coercivity ; Metallurgy ; Crystallography ; Chemistry ; Condensed matter physics Magnetic Properties of Alloys ; Rare-earth and actinide compounds ; Metallurgical and Alloy Processes
Authors: Michael Heredia-Pérez | Diego A. Álvarez | Daniel Bedoya-Ruíz
Source: Archives of Computational Methods in Engineering (review, 2025-07-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A State-of-the-Art Review of the Bouc-Wen Class Model of Hysteresis: Origin, Evolution and Current State Abstract This paper presents a comprehensive review of the Bouc-Wen model for hysteresis, analyzing its historical development, mathematical formulations, and capacity to represent complex behaviors such as asymmetry, degradation, pinching, and two newly identified response patterns: s-shaped and flag-shaped curves. The phenomenological nature of the model is examined, highlighting the absence of a rigorous mathematical and physical foundation in many of its variations. A systematic survey of the available literature is conducted to assess the physical compatibility of the Bouc-Wen formulation and its derivatives, identifying cases where modifications align with fundamental mechanical principles and others that remain purely empirical. Additionally, trends, challenges, and gaps in existing implementations are discussed, providing insights into the model’s evolution over the past two decades. Commentary on biaxial developments, stochastic dynamic implementation, and parameter identification of the model is presented. The review concludes by outlining potential research directions, emphasizing the need for enhanced physical consistency, improved parameter identification strategies, broader applications in structural and mechanical systems, and further generalization of the model through multi-representation capability. Current (fluid) ; State (computer science) ; Hysteresis ; Class (philosophy) ; Computer science ; Mathematics ; Physics ; Artificial intelligence ; Algorithm ; Condensed matter physics ; Thermodynamics Magnetic Properties and Applications ; Fluid Dynamics and Vibration Analysis ; Force Microscopy Techniques and Applications
Authors: Radosław Gruska | Alina Kunicka‐Styczyńska | Magdalena Molska
Source: Molecules (article, 2025-05-24)
Field: Chemistry — Analytical Chemistry

Application of Fourier Transform Near-Infrared Spectroscopy and Chemometrics for Quantitative Analysis of Milk of Lime (MOL) Used in the Sugar Industry Milk of lime (MOL), a suspension of calcium oxide and calcium hydroxide, is vital in the purification of sugar beet and cane juices. This study evaluates the application of Fourier Transform Near-Infrared (FT-NIR) spectroscopy combined with chemometric models-Partial Least Squares (PLS) and Principal Component Regression (PCR)-for rapid, non-destructive assessment of key MOL parameters: density, total lime content, calcium oxide availability, and sucrose content. Ninety samples were analyzed using both wet chemistry and FT-NIR. The predictive performance was assessed using the coefficient of determination (R<sup>2</sup>). High predictive accuracy was observed for density (PLS: R<sup>2</sup> = 0.8274; PCR: R<sup>2</sup> = 0.8795) and calcium oxide availability (PLS: R<sup>2</sup> = 0.9035; PCR: R<sup>2</sup> = 0.9115). Total lime content showed moderate accuracy (PLS: R<sup>2</sup> = 0.7748; PCR: R<sup>2</sup> = 0.7983), while sucrose content exhibited low predictive power (PLS: R<sup>2</sup> = 0.2312; PCR: R<sup>2</sup> = 0.3747). The weak performance was noted for %CaO (PLS: R<sup>2</sup> = 0.4893; PCR: R<sup>2</sup> = 0.2409), likely due to spectral overlap and matrix complexity. Despite these challenges, FT-NIR remains a viable, reagent-free method for monitoring MOL, with the potential to enhance process control in the sugar industry. Future work should focus on refining calibration strategies and addressing spectral interferences to improve predictive accuracy for complex matrices. Partial least squares regression ; Chemometrics ; Lime ; Chemistry ; Calcium oxide ; Principal component regression ; Fourier transform infrared spectroscopy ; Coefficient of determination ; Sugar ; Analytical Chemistry (journal) ; Near-infrared spectroscopy ; Principal component analysis ; Chromatography ; Food science ; Mathematics ; Materials science ; Chemical engineering Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Robert D. Leone
Source: Journal of Information Systems Engineering & Management (article, 2025-04-12)
Field: Chemistry — Organic Chemistry

Analysis of Processes for Olive Oil Mill Wastewater Treatment The management and reuse of Olive Oil Mill Wastewater (OOMW) represent a critical challenge and opportunity for promoting sustainable practices in the agro-industrial sector. While OOMW is traditionally viewed as an environmental burden, it also contains valuable organic compounds with potential for recovery and reuse. This study adopts a bibliometric approach supported by information systems to analyze current research trends and emerging technologies for OOMW treatment. By integrating data from the Scopus database and visualizing results with VOSviewer, the analysis identifies thematic clusters and interdisciplinary connections, revealing a growing interest in the digital transformation of wastewater management. Key findings highlight the increasing adoption of smart technologies—such as Internet of Things (IoT) sensors, machine learning algorithms, and real-time decision-support systems—for monitoring, optimizing, and valorizing OOMW. These digital tools not only enhance treatment efficiency but also enable dynamic resource allocation and support circular economy strategies. The study concludes that the convergence between environmental technologies and information systems is reshaping the future of OOMW management, paving the way for intelligent, sustainable, and data-driven solutions in the olive oil industry. Olive oil ; Mill ; Wastewater ; Pulp and paper industry ; Environmental science ; Sewage treatment ; Waste management ; Chemistry ; Engineering ; Environmental engineering ; Food science Edible Oils Quality and Analysis
Authors: Habib Amiri Savadroodbari | Milad Razbin | Mohsen Rezahasani | Majid Safar Johari
Source: Scientific Reports (article, 2025-10-30)
Field: Materials Science — Polymers and Plastics

Multi-objective optimization of speed frame parameters for polyester spun yarn using artificial intelligence and grey relational analysis Systematic approaches to process optimization are essential for enhancing operational efficiency in industrial manufacturing, particularly in the textile sector. This study investigates and compares the effectiveness of Artificial Intelligence (AI) based modeling and optimization techniques with Grey Relational Analysis (GRA) in determining the optimal operational parameters of the speed frame for producing 100% polyester spun yarn. The key parameters analyzed include twist, break draft, spacer size, and overhang, with Response Surface Methodology (RSM) employed to design the experiments and ensure sufficient variability in the input space. Yarn quality was evaluated using the Grey Relational Grade (GRG), calculated through GRA based on two critical response variables: yarn evenness (CVm%) and imperfection index (IPI). A predictive model based on an Artificial Neural Network (ANN) was developed to estimate the GRG, and its performance was compared to that of the RSM model. Optimization was then conducted using both GRA and the Genetic Algorithm (GA) to assess the comparative efficiency of a multi-criteria decision-making approach and an AI-based optimization technique. The results demonstrated that the ANN model achieved significantly higher prediction accuracy (R<sup>2</sup> ≈ 1) compared to the RSM model (R<sup>2</sup> ≈ 0.65). Furthermore, the GA-based optimization outperformed GRA, resulting in improved yarn quality. The optimal parameter settings identified were twist = 23 TPM, break draft = 1.26, spacer size = 5.1 mm, and overhang = 3.5 mm. These findings highlight the superior performance and reliability of AI-based methods over conventional statistical and multi-criteria decision-making approaches, emphasizing their potential to advance intelligent optimization in textile manufacturing. Grey relational analysis ; Artificial neural network ; Yarn ; Process (computing) ; Frame (networking) ; Genetic algorithm ; Reliability (semiconductor) ; Weaving Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Advanced Sensor and Energy Harvesting Materials