jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Farhang Pazanialenjareghi | Shweta Singh | Fathy Attia | Venkat Munnangi | Emmanuel M. Nsengiyumva | Yang Jiao | Haiqing Lin
Source: Journal of Materials Chemistry A (article, 2025-12-08)
Field: Chemistry — Inorganic Chemistry

Engineering MOFs for thin-film and nanofilm nanocomposite membranes for CO <sub>2</sub> separation This paper critically reviews strategies for molecularly engineering crystalline MOFs to enhance their distribution within polymers and to improve CO 2 /gas separation properties in the resulting thin-film and nanofilm nanocomposite membranes. Nanocomposite ; Membrane ; Polymer ; Polymer nanocomposite ; Gas separation ; Nanoparticle Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Carbon Dioxide Capture Technologies
Authors: Yuling Wang | Chen Zhang | Xin‐Hua Li | Longzhu Xing | M. Lv | Hong-Ju He | Leiqing Pan | Xingqi Ou
Source: Foods (article, 2025-07-07)
Field: Chemistry — Analytical Chemistry

Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour This research implemented a miniaturized near-infrared spectroscopy (NIRS) system integrated with machine learning approaches for the quantitative evaluation of dry gluten content (DGC), wet gluten content (WGC), and the gluten index (GI) in wheat flour in a noninvasive manner. Five different algorithms were employed to mine the relationship between the full-range spectra (900-1700 nm) and three parameters, with support vector regression (SVR) demonstrating the best prediction performance for all gluten parameters (R<sub>P</sub> = 0.9370-0.9430, RMSEP = 0.3450-0.4043%, and RPD = 3.1348-3.4998). Through a comparative evaluation of five wavelength selection techniques, 25-30 optimal wavelengths were identified, enabling the development of optimized SVR models. The improved whale optimization algorithm iWOA-based SVR (iWOA-SVR) model exhibited the strongest predictive capability among the five optimal wavelengths-based models, achieving comparable accuracy to the full-range spectra SVR for all gluten parameters (R<sub>P</sub> = 0.9190-0.9385, RMSEP = 0.3927-0.5743%, and RPD = 3.0424-3.2509). The model's robustness was confirmed through external validation and statistical analyses (<i>p</i> > 0.05 for F-test and <i>t</i>-test). The results highlight the effectiveness of micro-NIRS combined with iWOA-SVR for the nondestructive gluten quality assessment of wheat flour, providing a more valuable reference for expanding the use of NIRS technology and developing portable specialized NIRS equipment for industrial-level applications in the future. Gluten ; Wheat gluten ; Quality (philosophy) ; Computer science ; Algorithm ; Machine learning ; Artificial intelligence ; Food science ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Olga E. Shapovalova | Evgeniia E Barinova | Evgeny E. Priakhin | Anastasia N Zakolpina | Andrey S. Drozdov | Maxim P. Nikitin
Source: Nanoscale (review, 2025-01-01)
Field: Materials Science — Biomaterials

Biocomputing systems based on carbon materials and DNA Biocomputing is an interdisciplinary field at the intersection of biology, informatics, and nanotechnology. Biocomputing nanoplatforms are sensor modules based on biomolecules that detect and process external stimuli as inputs and generate corresponding outputs. These platforms operate according to specific algorithms, often obeying the laws of Boolean algebra. Such systems are typically based on DNA logic interfaces due to their versatility in terms of design, synthesis, and potential input types. While DNA logic gates are typically implemented in homogeneous oligonucleotide solutions, creating various heterogeneous systems using organic or inorganic nanosystems offers advantages in designing and operating such logic systems. This review focuses on applying carbon-based materials, such as graphene, graphene oxide, carbon nanotubes, and carbon quantum dots, to DNA logic gates. For the first time, this article summarizes the entire body of literature on the topic, highlighting how the physicochemical properties of carbon-based materials can benefit the design and functionality of DNA logic gates. The review summarizes the main types of logic gates, describes the mechanisms underlying the interaction between DNA and carbon-based materials, and presents the logic gates described in the literature with respect to input signals. The review demonstrates that the versatility of these systems makes carbon-coupled DNA logic gates a promising platform for biocomputing, nanotechnology, and bioanalysis. Nanotechnology ; Intersection (aeronautics) ; Biomolecule ; Informatics ; Field (mathematics) ; Process (computing) ; Computer science ; Materials science ; Data science ; Engineering ; Mathematics ; Electrical engineering Diatoms and Algae Research ; DNA and Biological Computing ; Advanced biosensing and bioanalysis techniques
Authors: Ruben S. Dragland | C. Salazar Mejía | Ingvild Hansen | Yosuke Hamasaki | Elvia Anabela Chavez Panduro | Yoshitaka Ehara | Tino Gottschall | Dennis Meier | Jan Schultheiß
Source: Communications Materials (article, 2025-05-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Relation between 4f-magnetism and the low-temperature magnetocaloric effect in multiferroic hexagonal manganites Abstract The magnetocaloric effect enables magnetic refrigeration and plays an important role for cooling at cryogenic temperatures, which is essential for emergent technologies such as hydrogen liquefaction and quantum computing. Here, we study the origin of the low-temperature magnetocaloric effect in multiferroic hexagonal manganites. By conducting direct adiabatic temperature measurements in pulsed magnetic fields exceeding 20 T on different R MnO 3 systems with varying magnetic 4f-moments (i.e., R = Y, Ho, Er, and Tm), we demonstrate significant magnetic-field-driven reversible temperature changes, Δ T ad . Our data show that the effect is predominantly driven by the rare-earth magnetism, scaling with the effective magnetic moment of the R atom. The largest reversible temperature change is observed in HoMnO 3 with Δ T ad of up to 20.1 K, whereas the effect is largely suppressed in YMnO 3 . Our findings demonstrate the importance of the 4f-magnetism for the magnetocaloric effect in hexagonal manganites, which is expected to be relevant for other magnetic oxide systems and their optimization for refrigeration applications at cryogenic temperature. Multiferroics ; Condensed matter physics ; Magnetic refrigeration ; Magnetism ; Hexagonal crystal system ; Materials science ; Ferromagnetism ; Magnetization ; Physics ; Magnetic field ; Ferroelectricity ; Chemistry ; Crystallography ; Optoelectronics ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; Multiferroics and related materials ; Shape Memory Alloy Transformations
Authors: Jyotisana Meena | Sujatha Narayanan Unni
Source: Cureus Journal of Computer Science. (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Effective Multiclass Grapevine Leaves Disease Classification Using Hyperspectral Imaging and Optimized Artificial Neural Network Grapevine farming is one of the most common and economical agricultural practices worldwide. The grapevine crop is affected by various diseases caused by biotic or abiotic stress, which degrade production and quality. Therefore, precise identification of disease is predominant for sustainable viticulture. This article proposes a potential solution for automatically detecting various diseases, damages, and healthy samples. The study develops an optimized artificial neural network (ANN) and trains it with a composite loss function consisting of weighted categorical cross-entropy (WCCE) and label smoothing (LS). The WCCE helps address the class imbalance challenge, whereas LS prevents the model from being overconfident and makes it generalized. The study utilizes the grapevine leaf hyperspectral imaging dataset, which falls under the visible and near infrared (400-900 nm) range and has 204 bands/wavelengths from 87,305 spectra extracted via the region of interest. The 30 signature relevant wavelengths for disease detection, identified using linear discriminant analysis, are significant as they represent the most informative parts of the spectrum for disease detection, thereby helping reduce data redundancy. For further experimentation, the dataset was randomly split into Dataset-A, which contained 70% of the total spectra for a 10-fold cross-validation setting, and the remaining 30% was allocated to Dataset-B, which was reserved as an unseen dataset to evaluate the model's robustness. The optimized ANN performed best among the other classifiers in terms of performance metrics (classification accuracy, precision, recall, and F1-score). For the full range, the optimized ANN achieved 96.37%±0.26% classification accuracy on Dataset-A and 96.47% on Dataset-B. Moreover, for the significant 30 wavelengths, the optimized ANN achieved 95.47%±0.34% classification accuracy on Dataset-A and 95.17% classification accuracy on Dataset-B. The promising results demonstrated that the proposed model can be used as a mass-screening tool for potential diseases to boost the production and quality of the grape crop. Hyperspectral imaging ; Artificial intelligence ; Artificial neural network ; Pattern recognition (psychology) ; Computer science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Miao Lv | Huizhen Feng | Yongxing Jin | Ying Tian
Source: Photonics (article, 2025-09-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advances in Deep Learning-Driven Metasurface Design and Application in Holographic Imaging Currently, the integration of deep learning technology with metasurface holographic imaging technology has propelled the development of optical imaging. Owing to the precise control of metasurfaces over the characteristics of light waves, holographic imaging technology can produce corresponding three-dimensional images after processing. Therefore, their integration enables the acquisition of high-quality images. The number of articles on metasurface design using neural network-based deep learning methods is increasing day by day; however, reviews on this topic remain scarce. This review introduces the development of neural networks and the relevant content on metasurface design using the four types of networks and the applications of deep learning-designed metasurface holographic imaging technology, thereby enhancing readers’ systematic understanding of such technologies. Holography ; Deep learning ; Artificial neural network ; Optical imaging ; Holographic display ; Imaging science Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Energy Harvesting in Wireless Networks
Authors: Rindy Julianda | Tundo Tundo | Sugeng Sugeng
Source: International Journal Software Engineering and Computer Science (IJSECS) (article, 2025-04-01)
Field: Chemistry — Analytical Chemistry

Chili Type Detection System Using Principal Component Analysis Method Classification of types of chili vegetables is an important aspect in the agricultural industry to increase the efficiency of product management, packaging and distribution. This research aims to implement the Principal Component Analysis (PCA) method in the process of classifying vegetables and types of chilies. PCA is used to reduce the dimensionality of the data and extract the main features that are significant in distinguishing vegetable categories. The research dataset consists of digital images of chili vegetables which are extracted into color, texture and shape attributes. The research results show that PCA is able to significantly improve classification accuracy by minimizing computational complexity. Experiments were carried out with various numbers of principal components in PCA to determine the optimal configuration. In the best configuration, this method achieves classification accuracy of 90%, with PCA effectively reducing data dimensionality by up to 95% without losing important information. In conclusion, this approach has great potential to be implemented in vegetable classification automation systems to support efficiency in agricultural supply chains. Principal component analysis ; Type (biology) ; Component (thermodynamics) ; Computer science ; Mathematics ; Statistics ; Physics ; Biology Spectroscopy and Chemometric Analyses
Authors: Tolutope Adebimpe Oso | Uthman Okikiola Adebayo | Olalekan John Okesanya | Clement Ngele Chukwu | Oluwatobi Babajide Ayelaagbe | Khalifat Boluwatife Obadeyi | Moyosore Esther Ogunmuyiwa-James | A Yahaya | Kabiru Olalekan Tajudeen | Olaoluwa Joseph Oso | Mohamed Mustaf Ahmed | AnjolaJesu Joy Oso | Don Eliseo Lucero‐Prisno
Source: Next Nanotechnology (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Nanotechnology-driven antifungal strategies: A systematic review of mechanisms, therapeutic efficacy, and translational barriers in combating drug-resistant fungal infections The global increase in drug-resistant fungal infections poses an important threat to public health, with conventional antifungal agents facing drawbacks in efficacy, toxicity, and resistance development. This systematic review explores the ability of nanotechnology-based antifungal strategies to address these issues by evaluating 14 experimental studies published between 2014 and 2024. A broad range of nanomaterials, including copper, zinc, silver, selenium, and silica-based nanoparticles, was assessed for their antifungal properties against clinically relevant pathogens such as Candida albicans , Aspergillus spp., and Fusarium . These nanomaterials exhibited potent antifungal activity through reactive oxygen species generation, membrane disruption, enzyme inhibition, and biofilm suppression. Some formulations incorporated advanced delivery features, including stimulus-responsive release and enhanced bioavailability. Green synthesis approaches, using plant- or marine-derived organisms, presented the eco-friendly potential for nanoparticle fabrication. Despite promising in vitro efficacy and broad-spectrum activity, key translational challenges remain. These include a lack of in vivo validation, concerns about toxicity and nanoparticle stability, formulation complexity, and limited scalability. The findings suggest that nanotechnology provides a powerful and adaptable platform to mitigate antifungal resistance. However, future success lies in comprehensive preclinical evaluations, standardized testing protocols, and regulatory frameworks to harness clinical translation. This review emphasizes the urgent need for interdisciplinary collaboration to realize the therapeutic potential of nanotechnology in mitigating drug-resistant fungal infections. Antifungal ; Candida albicans ; Translational research ; Aspergillus ; Systematic review ; Fungal pathogen ; Candida infections Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Lahbib Abenghal | Adrien Ratier | Hamid Lamoudan | Dan Belosinschi | François Brouillette
Source: Polymers (article, 2025-07-24)
Field: Materials Science — Biomaterials

Thermodynamic Aspects of Ion Exchange Properties of Bio-Resins from Phosphorylated Cellulose Fibers Phosphorylated cellulose is proposed as a bio-resin for the removal of heavy metals, as a substitute for synthetic polymer-based materials. Phosphorylation is carried out using kraft pulp fibers as the cellulose source, with phosphate esters and urea as reactants to prevent significant fiber degradation. Herein, phosphorylated fibers, with three types of counterions (sodium, ammonium, or hydrogen), are used in adsorption trials involving four individual metals: nickel, copper, cadmium, and lead. The Langmuir isotherm model is applied to determine the maximum adsorption capacities at four different temperatures (10, 20, 30, and 50 °C), enabling the calculation of the Gibbs free energy (<i>ΔG</i>), entropy (<i>ΔS</i>), and enthalpy (<i>ΔH</i>) of adsorption. The results show that the adsorption capacity of phosphorylated fibers is equal or even higher than that of commercially available resins (1.7-2.9 vs. 2.4-2.6 mmol/g). However, the nature of the phosphate counterion plays an important role in the adsorption capacity, with the alkaline form showing a superior ion exchange capacity than the hybrid form and acid form (2.7-2.9 vs. 2.3-2.7 vs. 1.7-2.5 mmol/g). The thermodynamic analysis indicates the spontaneous (<i>ΔG</i> = (-)16-(-)30 kJ/mol) and endothermic nature of the adsorption process with positive changes in enthalpy (0.45-15.47 kJ/mol) and entropy (0.07-0.14 kJ/mol·K). These results confirm the high potential of phosphorylated lignocellulosic fibers for ion exchange applications, such as the removal of heavy metals from process or wastewaters. Adsorption ; Gibbs free energy ; Cellulose ; Enthalpy ; Chemistry ; Ion exchange ; Inorganic chemistry ; Counterion ; Nuclear chemistry ; Chemical engineering ; Organic chemistry ; Ion ; Thermodynamics Advanced Cellulose Research Studies ; Adsorption and biosorption for pollutant removal ; Phosphorus and nutrient management
Authors: Alexey G. Gerbst | Bozhena S. Komarova | A. N. Vlasenko | Nikolay E. Nifantiev
Source: ACS Omega (article, 2025-01-06)
Field: Chemistry — Organic Chemistry

Modeling of the Carbohydrate Oxacarbenium Ionic Intermediates of Glycosylation Reactions with Explicit Account for Protective Group Effects O-Protected oxacarbenium ions are key intermediates of glycosylation reactions. The knowledge of their conformational preferences is crucial for choosing the correct blocking group pattern to achieve the required stereochemical outcome. This article describes a computational study of several glucosyl oxacarbenium cations. The primary aim was to address the challenge of modeling oxacarbenium structures with all explicit O-blocking groups present instead of their simplified models. There exists no physical method to directly measure the energy of such structures. Therefore, the DLPNO-CCSD(T) method was used as a reference, which is considered to give the most exact results, however, without the possibility of geometry optimizations. Three DFT methods were tried to compare their values to those computed with DLPNO-CCSD(T). Finally, the B3LYP-D3 combination is suggested as the best recommendation for future studies of complex carbohydrate reaction intermediates with explicit protective groups. Possible reasons for the relative stability of different conformers of glycosyl cations are discussed in terms of SCF and electron correlation energies. The results of the B3LYP-D3 method show a good correlation with several model glycosylation reactions. Chemistry ; Conformational isomerism ; Computational chemistry ; Glycosylation ; Group (periodic table) ; Glycosyl ; Blocking (statistics) ; Ionic bonding ; Molecule ; Ion ; Stereochemistry ; Computer science ; Organic chemistry Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Catalysis for Biomass Conversion
Authors: Hernán Chávez Thielemann | Harm J. M. Caelers | Leon E. Govaert | J.A.W. van Dommelen
Source: Journal of Applied Polymer Science (article, 2025-07-14)
Field: Materials Science — Polymers and Plastics

Micromechanical Modeling of the Elasto‐Viscoplastic Stress–Strain Response of <i>α</i>‐<scp>iPP</scp> ABSTRACT The mechanical properties of semi‐crystalline polymers are dictated by the individual characteristics of crystalline and amorphous phases and the complex interplay between them. Processing conditions can strongly influence micro‐structural features such as crystallinity and orientation of crystalline structures, thereby changing the polymer behavior. In the present study, we employ a micromechanical composite inclusion model to obtain a direct relation between the microstructure and the resulting properties for alpha isotactic polypropylene (α‐iPP). The model uses a dedicated constitutive law for each constituent phase, thus capturing the elasto‐viscoplastic phenomena of both the isotropic amorphous and anisotropic crystalline phases. As such, it is intrinsically capable of covering the macro‐mechanical effects of texture evolution caused by crystalline slip. The model parameters for crystalline and amorphous phases of isotropic α ‐iPP are identified by comparing the model output with experimental results of uniaxial compression and tension tests at different strain rates and temperatures. Finally, the long‐term response of α ‐iPP in uniaxial creep loading is predicted and compared to experimental results. Viscoplasticity ; Stress–strain curve ; Composite material ; Materials science ; Strain (injury) ; Stress (linguistics) ; Structural engineering ; Deformation (meteorology) ; Finite element method ; Constitutive equation ; Engineering Polymer crystallization and properties ; Mechanical Behavior of Composites ; Composite Material Mechanics
Authors: Amisha Bharti | Vinod Gurjar
Source: International Journal For Multidisciplinary Research (review, 2025-05-27)
Field: Chemistry — Analytical Chemistry

Hyphenated Technique Used for the Analysis of Pharmaceutical Impurities: A Comprehensive Review The term "Hyphenated Technique" describes a set of analytical techniques that improve pharmaceutical impurity identification and characterization. This comprehensive review uses several hyphenated techniques; The review of hyphenated techniques includes various techniques which are used nowadays for analysis. One of the powerful tools of impurity profile is like Chromatographic techniques like GC, LC etc., which are used for the separation and spectroscopic techniques such as NMR, MS, and IR used for the identification of impurities in pharmaceutical analysis are covered in this thorough overview found as necessary steps to measure for this compounds at extremely low concentrations. Such methods can increase sensitivity, specificity, and resolution, so that patterns of sophisticated impurities in drug substances can be indeed detected. The review would also include issues like regulation as well as challenges in the development of new methods and importance of precise impurity profiling with respect to safety and efficacy in pharmaceuticals. It certainly demonstrates a distinct hyphenated approaches towards modem pharmaceutical quality control and regulatory compliance. Thus, it is in the light of this review that the significance of hyphenated techniques in ensuring drug safety, efficacy and compliance comes out through the current innovations. Nanogram quantities of impurities could otherwise go unnoticed by conventional analytical techniques as hyphenated methods permit their detection. With combined mass spectrometry and chromatography, resolution is improved, high-throughput analysis enhanced, and even trace impurities are quantitated with greater accuracy at concentrations that may be extremely low. These techniques also clarify the impurity structure, which is important in identifying the potential hazards and how it impacts safety and stability with regards to patient care and the drug. Chromatography ; Chemistry Analytical Methods in Pharmaceuticals