jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Carina G. Baigorria | Luciana Cerioni | Mario Alberto Debes | Ana E. Ledesma | Patricio Alastuey | Mónica Tirado | Sabrina I. Volentini | Viviana A. Rapisarda
Source: Journal of Fungi (article, 2024-11-11)
Field: Materials Science — Materials Chemistry

Antifungal Action of Metallic Nanoparticles Against Fungicide-Resistant Pathogens Causing Main Postharvest Lemon Diseases Postharvest fungal diseases are the main cause of economic losses in lemon production. The continued use of synthetic fungicides to control the diseases favors the emergence of resistant strains, which encourages the search for alternatives. The aim of this study was to assess the efficacy of metallic nanoparticles (NPs) as antifungal agents against local isolates of <i>Penicillium digitatum</i> and <i>Penicillium italicum</i>, each of them in a fungicide-sensitive and -resistant version, and a <i>Geotrichum citri-aurantii</i> isolate. NPs of ZnO, CuO, and Ag were synthesized and characterized by spectroscopy and microscopy, presenting average sizes < 25 nm and spherical shapes. ZnO-NPs did not present antifungal activity at the assayed conditions, while the minimum fungicidal concentrations (MFCs) were 1000 and 10 µg mL<sup>-1</sup> for CuO-NPs and Ag-NPs, respectively. The NPs' antimicrobial action included conidial membrane permeability and strong intracellular disorganization. Moreover, the Ag-NPs reduced green mold incidence on inoculated lemons when applied to the fruit. Taken together, Ag-NPs were effective in inhibiting both fungicide-sensitive and -resistant isolates of the main lemon postharvest pathogens, suggesting their potential use as an alternative approach. Penicillium digitatum ; Fungicide ; Postharvest ; Penicillium italicum ; Pyrimethanil ; Antimicrobial ; Biology ; Horticulture ; Geotrichum ; Penicillium ; Antifungal ; Botany ; Food science ; Microbiology Nanoparticles: synthesis and applications ; Nanocomposite Films for Food Packaging ; Essential Oils and Antimicrobial Activity
Authors: Tao Wang | Yongkuai Chen | Yuyan Huang | Chengxu Zheng | Shuilan Liao | Li Xiao | Jian Zhao
Source: Foods (article, 2024-12-20)
Field: Chemistry — Analytical Chemistry

Prediction of the Quality of Anxi Tieguanyin Based on Hyperspectral Detection Technology Anxi Tieguanyin belongs to the oolong tea category and is one of the top ten most famous teas in China. In this study, hyperspectral imaging (HSI) technology was combined with chemometric methods to achieve the rapid determination of free amino acid and tea polyphenol contents in Tieguanyin tea. Here, the spectral data of Tieguanyin tea samples of four quality grades were obtained via visible near-infrared hyperspectroscopy in the range of 400-1000 nm, and the free amino acid and tea polyphenol contents of the samples were detected. First derivative (1D), normalization (Nor), and Savitzky-Golay (SG) smoothing were utilized to preprocess the original spectrum. The characteristic wavelengths were extracted via principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and the successive projection algorithm (SPA). The contents of free amino acid and tea polyphenol in Tieguanyin tea were predicted by the back propagation (BP) neural network, partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM). The results revealed that the free amino acid content of the clear-flavoured Tieguanyin was greater than that of the strong-flavoured type, that the tea polyphenol content of the strong-flavoured Tieguanyin was greater than that of the clear-flavoured type, and that the content of the first-grade product was greater than that of the second-grade product. The 1D preprocessing improved the resolution and sensitivity of the spectra. When using CARS, the number of wavelengths for free amino acids and tea polyphenols was reduced to 50 and 70, respectively. The combination of 1D and CARS is conducive to improving the accuracy of late modelling. The 1D-CARS-RF model had the highest accuracy in predicting the free amino acid (R<sub>P</sub><sup>2</sup> = 0.940, RMSEP = 0.032, and RPD = 4.446) and tea polyphenol contents (R<sub>P</sub><sup>2</sup> = 0.938, RMSEP = 0.334, and RPD = 4.474). The use of hyperspectral imaging combined with multiple algorithms can be used to achieve the fast and non-destructive prediction of free amino acid and tea polyphenol contents in Tieguanyin tea. Hyperspectral imaging ; Polyphenol ; Chemistry ; Partial least squares regression ; Principal component analysis ; Mathematics ; Food science ; Artificial intelligence ; Computer science ; Statistics ; Biochemistry Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Advanced Chemical Sensor Technologies
Authors: K. Liu | Marcel H. F. Sluiter
Source: Materialia (article, 2024-03-23)
Field: Materials Science — Materials Chemistry

Incompatibility stress at inclined grain boundaries for cubic crystals under hydrostatic stress and uniaxial stress In a material under stress, grain boundaries may give rise to stress discontinuities. The stress state at grain boundaries strongly affects microscopic processes, such as diffusion and segregation, as well as failure initiation, such as fatigue, creep, and corrosion. Here the general condition of incompatibility stress at grain boundaries is studied with a bicrystal model for linear elastic materials. In materials with cubic crystal structures, it is proven that hydrostatic stress does not lead to a stress discontinuity at grain boundaries. For bicrystals with inclined grain boundaries under uniaxial stress, the extreme values of the incompatibility stress as a function of the inclination angle are obtained by a simulated annealing method. A simple criterion is proposed to classify cubic materials into three groups. For cubic crystals with at most moderate anisotropy, the highest incompatibility stress occurs when the grain boundary plane is perpendicular to the uniaxial stress. For highly anisotropic materials, such as alkali metals and polymorphic high-temperature phases, the highest incompatibility stress occurs on grain boundaries with an inclination of about 47o. Materials science ; Grain boundary ; Hydrostatic stress ; Stress (linguistics) ; Grain boundary strengthening ; Composite material ; Annealing (glass) ; Anisotropy ; Perpendicular ; Cubic crystal system ; Stress concentration ; Condensed matter physics ; Geometry ; Fracture mechanics ; Thermodynamics ; Optics ; Microstructure ; Finite element method Microstructure and mechanical properties ; Material Properties and Failure Mechanisms ; Fatigue and fracture mechanics
Authors: Lucy Zhao | Dieter Spiehl | Marion C. Kohnen | Marcelo Ceolı́n | Joanna J. Mikolei | Raheleh Pardehkhorram | Annette Andrieu‐Brunsen
Source: Small (article, 2024-02-13)
Field: Materials Science — Materials Chemistry

Printing of In Situ Functionalized Mesoporous Silica with Digital Light Processing for Combinatorial Sensing Combinatorial sensing is especially important in the context of modern drug development to enable fast screening of large data sets. Mesoporous silica materials offer high surface area and a wide range of functionalization possibilities. By adding structural control, the combination of structural and functional control along all length scales opens a new pathway that permits larger amounts of analytes being tested simultaneously for complex sensing tasks. This study presents a fast and simple way to produce mesoporous silica in various shapes and sizes between 0.27-6 mm by using light-induced sol-gel chemistry and digital light processing (DLP). Shape-selective functionalization of mesoporous silica is successfully carried out either after printing using organosilanes or in situ while printing through the use of functional mesopore template for the in situ functionalization approach. Shape-selective adsorption of dyes is shown as a demonstrator toward shape selective screening of potential analytes. Surface modification ; Mesoporous material ; Mesoporous silica ; Nanotechnology ; Materials science ; Context (archaeology) ; Analyte ; In situ ; Adsorption ; Digital Light Processing ; Computer science ; Chemical engineering ; Chemistry ; Organic chemistry ; Artificial intelligence ; Chromatography ; Catalysis Mesoporous Materials and Catalysis ; Analytical Chemistry and Sensors ; Diatoms and Algae Research
Authors: Lin Du | Francesca Rodino | Yann Thoma | Sandro Carrara
Source: IEEE Sensors Letters (article, 2024-06-24)
Field: Chemistry — Analytical Chemistry

Identification and Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors for Therapeutic Drug Monitoring Electrochemical sensors play a pivotal role in advancing therapeutic drug monitoring (TDM) and personalized therapy. However, the insufficient selectivity of electrochemical sensors based on cytochromes P450 poses a great need for innovative machine learning (ML) integrated with cyclic voltammograms obtained from electrochemical sensors and cyclic voltammetry to construct a fully automated system for accomplishing the precise identification and quantification of multiple drugs in the patients' blood. An effective ML-based method is proposed in this letter with the adaptive transformation of Gaussian mixture model algorithm for Gaussian feature extraction from the redox peak with peculiar characterisation and a novel artificial neural network-based model IdentQuantNet for the drug identification and quantification. The simulated dataset and measured dataset of cyclophosphamide (CP) and ifosfamide (IFO), proposed here as model drugs, are demonstrated to validate the efficacy of our proposed method. Our method can achieve the precision at 100<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> for identification task and the mean absolute error at 4.88 and 6.67 for CP and IFO for the quantification task on the measured dataset. Identification (biology) ; Drug ; Therapeutic drug monitoring ; Computer science ; Medicine ; Pharmacology ; Biology Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods
Authors: Shan Liu
Source: Frontiers in Bioengineering and Biotechnology (review, 2024-10-29)
Field: Materials Science — Biomaterials

Self-assembled lipid-based nanoparticles for chemotherapy against breast cancer Self-assembled lipid-based nanoparticles have been shown to have improved therapeutic efficacy and lower toxic side effects. Breast cancer is a common type of malignant tumor in women. Conventional drugs such as doxorubicin (DOX) have shown low therapeutic efficacy and high drug toxicity in antitumor therapy. This paper surveys research on self-assembled lipid-based nanoparticles by categorizing them under three groups: self-assembled liposomal nanostructures, self-assembled niosomes, and self-assembled lipid-polymer hybrid nanoparticles. Subsequently, the structural features and operating mechanisms of each group are summarized individually along with examples of representative drugs from each group. Doxorubicin ; Nanoparticle ; Liposome ; Breast cancer ; Drug ; Niosome ; Nanotechnology ; Chemotherapy ; Cancer ; Pharmacology ; Materials science ; Chemistry ; Medicine ; Membrane ; Internal medicine ; Biochemistry ; Vesicle Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Lipid Membrane Structure and Behavior
Authors: Yaolin Zhu | Xingze Wang | Meihua Gu | Gang Hu | Wenya Li
Source: Journal of Natural Fibers (article, 2024-02-11)
Field: Chemistry — Analytical Chemistry

Application of Unsupervised Feature Selection in Cashmere and Wool Fiber Recognition Suitable features are the key to identifying cashmere and wool fibers, and feature selection is an important step in classification.Existing supervised feature selection methods need to consider the information between fiber features and class labels.Aiming at making up for this deficiency, we propose an unsupervised feature selection method based on k-means clustering, which overcome the difficulty that fiber feature class labels are either unavailable or costly to obtain.Firstly, the subset of fiber features that have been normalized are clustered by the k-means clustering algorithm to obtain the total number of clusters, and the clustering effect is evaluated by the DB Index criterion.Next, the DB value of each feature subset, the correlation of features and the total number of the clustering are considered as the judgment criteria to select the optimal feature subset.Finally, the optimal subset of features obtained by unsupervised feature selection algorithms is fed into a support vector machine for automatic identification and classification of the two fibers.The experimental results show that the method achieves a high recognition rate of 97.25%.It is verified that the unsupervised feature selection method based on k-means clustering is effective for the recognition of cashmere and wool. Wool ; Selection (genetic algorithm) ; Fiber ; Pattern recognition (psychology) ; Feature (linguistics) ; Feature selection ; Artificial intelligence ; Computer science ; Materials science ; Composite material Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Wood and Agarwood Research
Authors: Giovanni De Matteis | Francesco Giglio | Antonio Moro
Source: Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2024-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Complete integrability and equilibrium thermodynamics of biaxial nematic systems with discrete orientational degrees of freedom We study a discrete version of a biaxial nematic liquid crystal model with external fields via an approach based on the solution of differential identities for the partition function. In the thermodynamic limit, we derive the free energy of the model and the associated closed set of equations of state involving four order parameters, proving the integrability and exact solvability of the model. The equations of state are specified via a suitable representation of the orientational order parameters, which imply two-order parameter reductions in the absence of external fields. A detailed exact analysis of the equations of state reveal a rich phase diagram where isotropic versus uniaxial versus biaxial phase transitions are explicitly described, including the existence of triple and tricritical points. Results on the discrete models are qualitatively consistent with their continuum analogue. This observation suggests that, in more general settings, discrete models may be used to capture and describe phenomena that also occur in the continuum for which exact equations of state in closed form are not available. Liquid crystal ; Degrees of freedom (physics and chemistry) ; Thermodynamics ; Extended irreversible thermodynamics ; Physics ; Classical mechanics ; Statistical physics ; Non-equilibrium thermodynamics ; Condensed matter physics Liquid Crystal Research Advancements ; Nonlinear Dynamics and Pattern Formation ; Molecular spectroscopy and chirality
Authors: Manon Perrignon | Mathieu Emily | Mélanie Münch | Romain Jeantet | Thomas Croguennec
Source: International Dairy Journal (article, 2024-11-22)
Field: Chemistry — Analytical Chemistry

Machine learning for predicting industrial performance: Example of the dry matter content of emmental-type cheese Controlling the dry matter content of cheese is essential to defining the performance of cheese production. For Emmental-type cheese, dry matter content has to be above but as close as possible to a minimal value that is defined by legislation. The means for achieving the target dry matter content was mostly left to the discretion of the cheese experts, who target a dry matter objective based on his expert knowledge and the deviation of cheese production. To date, the prediction of performance indicators, such as cheese dry matter content, can help cheesemakers to improve their production performance. Several Machine Learning models and classical statistical methods were compared to predict the dry matter of Emmental cheese for a set of data coming from one selected cheese industry. The Random Forest method emerged as the most effective model (RMSE = 0.28 and R 2 = 0.67). The weight of variables in explaining the variability of cheese dry matter content was also calculated, helping cheese experts to interpret the model and apply corrective actions to improve cheese production performance. The ability to predict cheese dry matter content and understand its variability from cheese manufacturing data offer new perspectives for the cheese industry. This method can be transferred to other indicators and assist in decision-making to enhance industry performance. Dry matter ; Food science ; Dairy industry ; Chemistry ; Biology ; Animal science Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Probiotics and Fermented Foods
Authors: Chi-Huan Tung | Shou-Yi Chang | Sidney Yip | Yangyang Wang | Jan‐Michael Y. Carrillo | Bobby G. Sumpter | Yuya Shinohara | Changwoo Do | Wei‐Ren Chen
Source: The Journal of Chemical Physics (article, 2024-03-06)
Field: Materials Science — Materials Chemistry

Viscoelastic relaxation and topological fluctuations in glass-forming liquids A method for characterizing the topological fluctuations in liquids is proposed. This approach exploits the concept of the weighted gyration tensor of a collection of particles and permits the definition of a local configurational unit (LCU). The first principal axis of the gyration tensor serves as the director of the LCU, which can be tracked and analyzed by molecular dynamics simulations. Analysis of moderately supercooled Kob-Andersen mixtures suggests that orientational relaxation of the LCU closely follows viscoelastic relaxation and exhibits a two-stage behavior. The slow relaxing component of the LCU corresponds to the structural, Maxwellian mechanical relaxation. Additionally, it is found that the mean curvature of the LCUs is approximately zero at the Maxwell relaxation time with the Gaussian curvature being negative. This observation implies that structural relaxation occurs when the configurationally stable and destabilized regions interpenetrate each other in a bicontinuous manner. Finally, the mean and Gaussian curvatures of the LCUs can serve as reduced variables for the shear stress correlation, providing a compelling proof of the close connection between viscoelastic relaxation and topological fluctuations in glass-forming liquids. Viscoelasticity ; Relaxation (psychology) ; Materials science ; Topology (electrical circuits) ; Classical mechanics ; Physics ; Composite material ; Mathematics ; Psychology ; Social psychology ; Combinatorics Material Dynamics and Properties ; Theoretical and Computational Physics ; Glass properties and applications
Authors: Wei Liu | Xinrong Xu | Jiaqing Zhang | Zhong Yu | Xiang Li | Yanming Ding
Source: Fire (article, 2024-01-12)
Field: Materials Science — Materials Chemistry

Thermal Decomposition Process of Fireproof Sealant Measured with Thermogravimetric and Fourier Transform Infrared Spectroscopy Analysis and Estimated Using Shuffled Complex Evolution Fireproof sealing technology is widely used in industrial, commercial, and other public buildings, so the performance of fireproof sealing materials in high temperatures or fire environments must be taken into account as an important factor. Fireproof sealant is considered to be a highly effective adhesive for sealing and fireproofing purposes. To explore its thermal decomposition mechanism and estimate its pyrolysis behaviors, a series of thermogravimetric experiments from 10 K/min to 60 K/min coupled with Fourier transform infrared spectroscopy analysis technology were performed. The results indicated that the thermal decomposition of the fireproof sealant could be divided into three reactions: the degradation of ammonium polyphosphate, melamine, and acrylic acid. In addition, the pyrolysis behavior of the fireproof sealant was compared under two kinds of atmosphere (nitrogen and air). Furthermore, the initial kinetic parameters in the nitrogen atmosphere were calculated based on model-free methods including the Friedman, KAS, and Starink methods. The average activation energy of three reactions obtained by the three methods was 108.32 kJ/mol, 200.46 kJ/mol, and 177.10 kJ/mol, respectively, while these obtained parameters were hard to regenerate, the thermogravimetric curves were accurately based on the established pyrolysis reaction scheme, with the existence of clear deviations. Therefore, a global heuristic optimization algorithm, Shuffled Complex Evolution (SCE), was selected to optimize 14 parameters (including activation energies and the pre-exponential factors) and the optimized pyrolysis results agreed well with the experimental data, even at the extra heating rate, with the correlation coefficient for the mass loss and mass loss rate being reaching up to 0.9943 and 0.9019, respectively. The study indicated that the SCE algorithm showed an appropriate potential to estimate the pyrolysis behavior of an unknown thermogravimetric experiment group. Thermogravimetric analysis ; Thermal decomposition ; Materials science ; Pyrolysis ; Sealant ; Fourier transform infrared spectroscopy ; Decomposition ; Chemical engineering ; Composite material ; Chemistry ; Organic chemistry Thermal and Kinetic Analysis ; Flame retardant materials and properties ; Fire dynamics and safety research
Authors: Milica Karadžić Banjac | Strаhinjа Kоvаčеvić | Sanja Podunavac-Kuzmаnоvić
Source: Processes (article, 2024-03-14)
Field: Chemistry — Analytical Chemistry

Ongoing Multivariate Chemometric Approaches in Bioactive Compounds and Functional Properties of Foods—A Systematic Review In this review, papers published in the chemometrics field were selected in order to gather information and conduct a systematic review regarding food science and technology; more precisely, regarding the domain of bioactive compounds and the functional properties of foods. More than 50 papers covering different food samples, experimental techniques and chemometric techniques were selected and presented, focusing on the chemometric methods used and their outcomes. This study is one way to approach an overview of the current publications related to this subject matter. The application of the multivariate chemometrics approach to the study of bioactive compounds and the functional properties of foods can open up even more in coming years, since it is fast-growing and highly competitive research area. Multivariate statistics ; Chemometrics ; Multivariate analysis ; Computer science ; Biochemical engineering ; Machine learning ; Engineering Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Meat and Animal Product Quality