jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bilel Hadrich
Source: Contemporary Mathematics (article, 2025-06-18)
Field: Chemistry — Analytical Chemistry

Design of Experiments Applications Review: Food Processing Engineering as an Example Field The relationship between food processing engineering and the various properties and qualities of the final product is both evident and significant for industrial companies and customers alike. However, understanding the mechanisms underlying this relationship can be effectively achieved through a powerful and efficient mathematical and statistical methodology known as Design of Experiments (DoE), which aids in optimizing quality while minimizing cost. DoE encompasses various types, each offering distinct advantages. As a versatile tool, it is not only straightforward and practical but also highly insightful. This review begins with an overview of DoE to illustrate its applicability and relevance to the engineering field in general and to food processing engineering in particular. Its crucial roles are also discussed in both process scale-up and ensuring regulatory compliance and standards. Furthermore, this review presents a rundown of published research studies that have employed DoE, highlighting how this methodology has been applied to address various challenges in food processing engineering. These studies-like many others utilizing DoE-focus on investigating the relationship between specific food properties (considered dependent variables or responses) and the independent variables, which represent the tested factors. Finally, the future of DoE in food processing engineering is explored, with particular attention to the new opportunities enabled by emerging technologies such as Artificial Intelligence, Machine Learning, and real-time process monitoring Relevance (law) ; Computer science ; Design of experiments ; Field (mathematics) ; Process (computing) ; Product (mathematics) ; Engineering design process ; Food processing ; Management science ; Risk analysis (engineering) ; Industrial engineering ; Systems engineering ; Engineering ; Mechanical engineering ; Mathematics Spectroscopy and Chemometric Analyses ; Optimal Experimental Design Methods ; Viral Infectious Diseases and Gene Expression in Insects
Authors: Alexander Ciupa
Source: RSC Advances (review, 2025-01-01)
Field: Chemistry — Spectroscopy

Five-membered heterocycles as promising platforms for molecular logic gate construction The field of molecular logic gates began thirty years ago with the early pioneers de Silva <i>et al.</i> (A. P. de Silva, H. Q. N. Gunaratne and C. P. McCoy, <i>Nature</i>, 1993, <b>364</b>, 42) laying the foundation for modern molecular-scale switches and devices. Recent reports of lab-on-a-molecule and molecular calculators (Moleculators) demonstrate the potential of this bottom-up approach. Five-membered heterocycles were central to the first reported logic gates in 1994 and remain valuable scaffolds in the present day. This review provides an overview of logic gate design using Boolean logic, introduces the work of the first pioneers, and highlights recently reported five-membered heterocycle logic gates. Logic gate ; AND gate ; Foundation (evidence) ; Nanotechnology ; Computer science ; Materials science ; Engineering ; Electrical engineering ; History Molecular Sensors and Ion Detection ; Molecular Junctions and Nanostructures ; Porphyrin and Phthalocyanine Chemistry
Authors: Farhan Sheth | Ishika Chatter | Manvendra Jasra | Gireesh Kumar | Richa Sharma
Source: Plant Methods (article, 2025-07-27)
Field: Chemistry — Analytical Chemistry

Herbify: an ensemble deep learning framework integrating convolutional neural networks and vision transformers for precise herb identification Herbs have historically been central to medicinal practices, representing one of the earliest forms of therapeutic intervention. While synthetic drugs are often highly effective in treating acute conditions, their use is frequently accompanied by adverse side effects. In addition, the growing dependence on synthetic pharmaceuticals has raised concerns regarding affordability, thereby fostering a renewed interest in herbal medicine as a cost-effective and holistic alternative. In response to this need, the current study introduces a computer vision framework for accurate herb identification. A novel dataset, Herbify, was compiled from two different herb datasets and refined through rigorous cleaning, preprocessing, and quality control procedures. The resulting dataset underwent standardization via the Preprocessing Algorithm for Herb Detection (PAHD), producing a refined dataset of 6104 images, representing 91 distinct herb species, with an average of about 67 images per species. Utilizing transfer learning, the research harnessed pre-trained Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), then integrated these models into an ensemble framework that leverages the unique strengths of each architecture. Experimental results indicate that EfficientNet v2-Large achieved a noteworthy F₁-score of 99.13%, while the ensemble of EfficientNet v2-Large and ViT-Large/16, termed EfficientL-ViTL, attained an even higher F₁-score of 99.56%. Additionally, the research also introduces 'Herbify' application, an AI-driven framework designed to identify herbs using the developed model. By directly tackling the principal obstacles in herb identification, the proposed system achieves a highly accurate and operationally viable classification mechanism. The experimental outcomes showcase top-tier performance in herb identification and emphasize the transformative potential of AI-based solutions in supporting botanical applications. Convolutional neural network ; Transformer ; Computer science ; Artificial intelligence ; Identification (biology) ; Deep learning ; Artificial neural network ; Machine learning ; Engineering ; Electrical engineering ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Huachuan Du | Ruomeng Qiu | Xianwen Lou | Stef A. H. Jansen | Hiroaki Sai | Yuyang Wang | Albert J. Markvoort | E. W. Meijer | Samuel I. Stupp
Source: Nature Communications (article, 2025-05-24)
Field: Materials Science — Biomaterials

Mapping in situ the assembly and dynamics in aqueous supramolecular polymers Supramolecular polymers, bonded through directional non-covalent interactions, closely mimic dynamic behaviors of biological nanofibers. However, the complexity of assembly pathways makes it highly challenging to unravel the nature of supramolecular dynamics in aqueous environments. Here we introduce a precise combinatorial titration methodology to probe in situ the assembly of peptide amphiphiles (PAs). This approach reveals a binary assembly mechanism governed by equilibrium between spheroidal micelles and β-sheet polymers. Weakening hydrogen bonding shifts the equilibrium towards micelles and decreases the internal structural order of filamentous polymers, promoting supramolecular dynamics. Extending this methodology to two-component copolymerization systems, we find a surprising tendency to form blocky nanostructures with reduced internal phase separation as the mismatch in peptide sequence decreases. Interestingly, while well-mixed copolymers acquire different dynamics, mismatched ones retain the characteristic supramolecular motion of their homopolymer counterparts. These critical insights into supramolecular dynamics offer strategies to tailor the dynamic functions of supramolecular nanomaterials. Supramolecular chemistry ; Supramolecular polymers ; Polymer ; Micelle ; Self-assembly ; Materials science ; Copolymer ; Amphiphile ; Hydrogen bond ; Aqueous solution ; Nanotechnology ; Supramolecular assembly ; Molecular dynamics ; Nanomaterials ; Nanostructure ; Chemical physics ; Chemistry ; Crystallography ; Molecule ; Computational chemistry ; Organic chemistry ; Crystal structure Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Supramolecular Chemistry and Complexes
Authors: Anjana Vinod | Arvindha Babu Diraviam | Manivel Raja Muthuvel | W. Madhuri
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rare earth-free magnetocaloric material Fe<sub>82</sub>Hf<sub>6</sub>Zr<sub>7</sub>B<sub>4</sub>Cu<sub>1</sub> for high-temperature applications The magnetocaloric effect (MCE) has garnered much attention in recent years, especially for rare earth (RE)-free magnetic materials. The increasing focus on these materials arises from their prospective uses in cryogenic magnetic cooling and elevated temperature environments. The study conducts a systematic experimental examination of a novel magnetocaloric material, Fe<sub>82</sub>Hf<sub>6</sub>Zr<sub>7</sub>B<sub>4</sub>Cu<sub>1</sub> ribbons, primarily aimed at characterising their structural, magnetic, and magnetocaloric properties. X-ray diffraction (XRD) analysis confirmed the successful incorporation of hafnium in the Fe site of the Fe-Zr-B-Cu matrix. Furthermore, the magnetic properties of the ribbon were also investigated, yielding a Curie transition temperature of 678 K and a magnetic entropy change of 0.448 J kg<sup>-1</sup> K<sup>-1</sup> at 2.0 T. Notably, the relative cooling power and refrigeration capacity were determined to be 11.87 J kg<sup>-1</sup> and 14.0 J kg<sup>-1</sup>, respectively, highlighting the potential of the material for high-temperature magnetocaloric applications. These findings collectively demonstrate that the novel Fe<sub>82</sub>Hf<sub>6</sub>Zr<sub>7</sub>B<sub>4</sub>Cu<sub>1</sub> ribbon exhibits promising magnetocaloric properties, rendering it a suitable candidate for further investigation and potential applications in high-temperature magnetocaloric devices. Magnetic refrigeration ; Rare earth ; Materials science ; Condensed matter physics ; Metallurgy ; Magnetization ; Physics ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Magnetic Properties of Alloys ; Inorganic Chemistry and Materials
Authors: Angela Joe | Maria Tănase | Cătălina Călin | Elena-Emilia Oprescu | Ionuț Banu | Dorin Bomboș | Stanca Cuc
Source: Polymers (article, 2025-05-13)
Field: Materials Science — Biomaterials

Pyrolyzed Biomass Filler for PLA-Based Food Packaging Poly(lactic acid) (PLA) is a biodegradable thermoplastic polymer used in various applications, including food packaging, 3D printing, textiles, and biomedical devices. Nevertheless, it presents several limitations, such as high hydrophobicity, low gas barrier properties, UV sensitivity, and brittleness. To overcome this issue, in this study, biochar (BC) produced through pyrolysis of bio-mass waste was incorporated (1 wt.%, 2wt.%, and 3 wt.%-PLA 1, PLA 2, and PLA 3) to enhance thermal and mechanical properties of PLA composites. The impact of pyrolysis temperature on the kinetic parameters, physicochemical characteristics, and structural properties of banana and orange peels for use as biochar added to PLA was investigated. The biomass waste such as banana and orange peels were characterized by proximal analysis and thermogravimetric analysis (TGA); meanwhile, the PLA composites were characterized by tensile straight, TGA, differential scanning calorimetry (DSC), scanning electron microscopy (SEM), and atomic force microscopy (AFM). The results indicated that the presence of biochar improved hygroscopic characteristics and Tg temperature from 62.98 °C for 1 wt.% to 80.29 °C for 3 wt.%. Additionally, it was found that the tensile strength of the composites increased by almost 30% for PLA 3 compared with PLA 1. The Young's modulus also increased from 194.334 MPa for PLA1 to 388.314 MPa for PLA3. However, the elongation decreased from 14.179 (PLA 1) to 7.240 mm (PLA3), and the maximum thermal degradation temperature shifted to lower temperatures ranging from 366 °C for PLA-1 to 345 °C for PLA-3 samples, respectively. From surface analysis, it was observed that the surface of these samples was relatively smooth, but small microcluster BC aggregates were visible, especially for the PLA 3 composite. In conclusion, the incorporation of biochar into PLA is a promising method for enhancing material performance while maintaining environmental sustainability by recycling biomass waste. Filler (materials) ; Pyrolysis ; Biomass (ecology) ; Food packaging ; Materials science ; Waste management ; Composite material ; Pulp and paper industry ; Food science ; Chemistry ; Agronomy ; Engineering biodegradable polymer synthesis and properties ; Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging
Authors: Rajat Suhag | Lucrezia Angeli | Matteo Scampicchio | Giovanna Ferrentino
Source: Comprehensive Reviews in Food Science and Food Safety (review, 2025-07-01)
Field: Chemistry — Organic Chemistry

From Quantity to Reactivity: Advancing Kinetic‐Based Antioxidant Testing Methods for Natural Compounds and Food Applications Traditional antioxidant testing methods primarily focus on quantifying antioxidant capacity but fail to capture their reactivity and effectiveness over time. Kinetic-based methods provide a deeper understanding of antioxidant performance by assessing reaction rates and inhibition mechanisms. This review discusses the advanced kinetic-based antioxidant testing methods, including the integration of kinetic modeling in traditional assays like 1,1-diphenyl-2-picrylhydrazyl (DPPH) and oxygen radical absorbance capacity, along with inhibited autoxidation methods based on isothermal calorimetry, oxygen uptake, and differential photocalorimetry (DPC). The principles, advantages, and limitations of these methods are discussed, along with their applications. Additionally, challenges related to instrumentation, standardization, and practical implementation are highlighted. Various kinetic-based antioxidant testing methods have been developed, each with its own set of advantages and limitations. Despite these differences, all these methods share one significant advantage: the ability to provide detailed kinetic information on antioxidant behavior. Continuous monitoring of antioxidant reactivity enables a deeper understanding of how these compounds function in inhibiting oxidation. Furthermore, most of these methods allow testing in real food-based oxidizable substrates, enhancing their relevance for food applications. Although techniques such as oxygen uptake and DPC may be limited by throughput capacity, methods like isothermal calorimetry and oxidizable substrate monitoring offer high-throughput capabilities, making them suitable for large-scale screening. This review presents a range of kinetic-based antioxidant testing methods that can be chosen and applied according to specific experimental requirements and convenience, providing flexibility to address various food-related oxidation challenges. Antioxidant ; Isothermal titration calorimetry ; Biochemical engineering ; Chemistry ; DPPH ; Oxygen radical absorbance capacity ; Autoxidation ; Computer science ; Process engineering ; Biochemistry Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Xinyi Bai | Libo Xu | Kang Li | Guangbao Zhang | Mengjun Zhang | Yi Huang
Source: Microbial Cell Factories (article, 2025-09-30)
Field: Materials Science — Biomaterials

Unlocking efficient polyhydroxyalkanoate production by Gram-positive Priestia megaterium using waste-derived feedstocks Polyhydroxyalkanoates (PHA) are sustainable alternatives to conventional plastics due to biodegradability and biocompatibility. However, most PHA-producing strains are Gram-negative, which co-produce endotoxins that limit their applicability in high-quality biomedical fields. Additionally, industrial-scale PHA production is hindered by high costs, with feedstocks accounting for half the total expenses. In this study, a Gram-positive strain, GM-4, was isolated and evaluated for industrial potential. This strain achieved a dry cell weight (DCW) of 5.4 g/L and a PHA content of 63% with glucose, exhibiting the highest production rates at the genus level. GM-4 could efficiently utilize sugarcane molasses and corn steep liquor, yielding 13.60 g/L DCW and 9.84 g/L PHA, which represents one of the highest PHA production observed from a wild bacterial strain utilizing waste-derived feedstocks at the flask scale. This feedstock combination significantly enhanced biomass growth and PHA production by 2.6-fold and 3.1-fold, respectively, offering economic and environmental benefits. The produced PHA was determined as polyhydroxybutyrate with excellent material properties through comprehensive characterization. Whole-genome analysis clarified the metabolic pathways that convert diverse substrates into PHA. These findings position GM-4 as a promising candidate for sustainable and cost-effective PHA production, with potential for biomedical and other applications. Polyhydroxyalkanoates ; Polyhydroxybutyrate ; Raw material ; Biomass (ecology) ; Biodegradation ; Bioplastic ; Production (economics) ; Industrial microbiology ; Industrial biotechnology biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Graphene and Nanomaterials Applications
Authors: Bhagwat Singh Chouhan | Sirsendu Ghosal | K M Rohith | S. K. Ray | P. K. Giri | Amir Ahmad | Gagan Kumar
Source: Scientific Reports (article, 2025-07-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultra-broadband actively tunable terahertz modulator based on multi-stacked metamaterial The flexible and tunable metasurfaces employing phase transition materials hold significance in developing terahertz photonic components for applications in 6G and beyond. This research article introduces ultra-broadband terahertz amplitude modulation caused by the metal-insulator phase transition in a vanadium dioxide thin film. Utilizing a multi-stacked metamaterial (MM) on a flexible substrate, the proposed design offers an ultra-broadband bandwidth. The high-quality vanadium dioxide film, capable of undergoing metal-insulator phase transition with temperature, is grown on a quartz substrate. Flexible multi-stacked MM, separately fabricated in a clean room, is transferred onto the VO<sub>2</sub> film. Terahertz time-domain spectroscopy reveals an ultra-broadband FWHM of 1.02 THz and a high modulation depth of 55% as the temperature of the integrated MM increases from 50 to 90 °C. A transmission line model is proposed to understand the resonance broadening observed through simulation and experiments. This work showcases a flexible and actively tunable ultra-broadband modulator developed via the facile polyimide-mediated peel-off technique of the MM and its transfer onto the phase transition material. The study unlocks avenues to develop versatile, flexible, tunable terahertz photonics components for applications in wireless communication systems. Terahertz radiation ; Broadband ; Metamaterial ; Terahertz metamaterials ; Optoelectronics ; Computer science ; Telecommunications ; Materials science ; Physics ; Optics ; Far-infrared laser Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Antenna Design and Analysis
Authors: Nicola Rossberg | Rekha Gautam | Katarzyna Komolibus | Barry O’Sullivan | Andrea Visentin
Source: Diagnostics (article, 2025-08-18)
Field: Chemistry — Analytical Chemistry

Explainable AI-Based Feature Selection Approaches for Raman Spectroscopy <b>Background</b>: Raman Spectroscopy is a non-invasive technique capable of characterising tissue constituents and detecting conditions such as cancer with high accuracy. Machine learning techniques can automate this task and discover relevant data patterns. However, the high-dimensional, multicollinear nature of Raman data makes their deployment and explainability challenging. A model's transparency and ability to explain decision pathways have become crucial for medical integration. Consequently, an effective method of feature-reduction while minimising information loss is sought. <b>Methods</b>: Two new feature selection methods for Raman spectroscopy are introduced. These methods are based on explainable deep learning approaches, considering Convolutional Neural Networks and Transformers. Their features are extracted using GradCam and attention scores, respectively. The performance of the extracted features is compared to established feature selection approaches across four classifiers and three datasets. <b>Results</b>: We compared the proposed method against established feature selection approaches over three real-world datasets and different compression levels. Comparable accuracy levels were obtained using only 10% of features. Model-based approaches are the most accurate. Using Convolutional Neural Networks and Random Forest-assigned feature importance performs best when maintaining between 5-20% of features, while LinearSVC with L1 penalisation leads to higher accuracy when selecting only 1% of them. The proposed Convolutional Neural Networks-based GradCam approach has the highest average accuracy. <b>Conclusions</b>: No approach is found to perform best in all scenarios, suggesting that multiple alternatives should be assessed in each application. Feature selection ; Raman spectroscopy ; Selection (genetic algorithm) ; Computer science ; Artificial intelligence ; Feature (linguistics) ; Pattern recognition (psychology) ; Physics ; Optics ; Philosophy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Annabel R. Basford | Aaron Hero Bernardino | Paula C. P. Teeuwen | Benjamin D. Egleston | Joshua Humphreys | Kim E. Jelfs | Jonathan R. Nitschke | Imogen A. Riddell | Rebecca L. Greenaway
Source: Angewandte Chemie International Edition (article, 2025-04-07)
Field: Chemistry — Inorganic Chemistry

Development of an Automated Workflow for Screening the Assembly and Host–Guest Behavior of Metal‐Organic Cages Towards Accelerated Discovery Metal-organic cages (MOCs) are a class of self-assembled materials with promising applications in chemical purifications, sensing, and catalysis. Their potential is, however, hampered by challenges in the targeted design of MOCs with desirable properties. MOC discovery is thus often reliant on trial-and-error approaches and brute-force manual screening, which are time-consuming, costly, and material-intensive. Translating the synthesis and property screening of MOCs to an automated workflow is therefore attractive, to both accelerate discovery and provide the datasets crucial for data-led approaches to accelerate MOC discovery and to realize their targeted properties for specific applications. Here, an automated workflow for the streamlined assembly and property screening of MOCs was developed, incorporating automated high-throughput screening of variables pertinent to MOC synthesis, data curation and automated analysis, and development of a host-guest assay to rapidly assess binding behavior. Computational modelling supplemented this automated experimental workflow for post priori rationalization of experimental outcomes. This study lays the groundwork for future large-scale MOC screening: from a relatively modest screen of 24 precursor combinations under one set of reaction conditions, 3 clean MOC species were identified, and subsequent screening of their host-guest behavior highlighted trends in binding and the identification of potential applications in molecular separations. Workflow ; Computer science ; Rationalization (economics) ; Identification (biology) ; Host (biology) ; Nanotechnology ; Biochemical engineering ; Database ; Engineering ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Dendrimers and Hyperbranched Polymers ; Machine Learning in Materials Science