jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zulaikha Zulkifli | Jun Jie Tan | Ku Marsilla Ku Ishak | Arjulizan Rusli | Muhammad Khalil Abdullah | Raa Khimi Shuib | Mohamad Danial Shafiq | Zuratul Ain Abdul Hamid
Source: Journal of Applied Polymer Science (review, 2022-02-24)
Field: Materials Science — Biomaterials

Shape memory poly (glycerol sebacate)‐based electrospun fiber scaffolds for tissue engineering applications: A review Abstract Electrospun fiber scaffolds has made a huge progress in tissue engineering applications owing to their continuous morphology with variable chemical, physical, and biological properties. In this review paper, we reviewed the electrospinning technique, its processing parameters and characterizations involved. A focus on shape memory poly (glycerol sebacate) (PGS)‐based electrospun fiber scaffolds for tissue engineering field was explored. The stimuli responsive PGS scaffold has gained much attention due to its ability to self‐expanding, self‐deploying, or self‐fitting. Herein, the electrospinning of shape memory PGS‐based scaffolds, particularly their processing parameters, properties, and characterizations have been reviewed. Besides, other issues concerning the technology constraints, challenges, and future directions are also discussed. Electrospinning ; Materials science ; Tissue engineering ; Scaffold ; Fiber ; Nanotechnology ; Biomedical engineering ; Polymer ; Composite material ; Engineering Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Polymer composites and self-healing
Authors: Zhicheng Jia | Ming Sun | Chengming Ou | Shoujiang Sun | Chunli Mao | Hong Liu | Juan Wang | Manli Li | Shangang Jia | Peisheng Mao
Source: Sensors (article, 2022-10-04)
Field: Chemistry — Analytical Chemistry

Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning Multispectral imaging (MSI) has become a new fast and non-destructive detection method in seed identification. Previous research has usually focused on single models in MSI data analysis, which always employed all features and increased the risk to efficiency and that of system cost. In this study, we developed a stacking ensemble learning (SEL) model for successfully identifying a single seed of sickle alfalfa (<i>Medicago falcata</i>), hybrid alfalfa (<i>M. varia</i>), and alfalfa (<i>M. sativa</i>). SEL adopted a three-layer structure, i.e., level 0 with principal component analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) as models of dimensionality reduction and feature extraction (DRFE); level 1 with support vector machine (SVM), multiple logistic regression (MLR), generalized linear models with elastic net regularization (GLMNET), and eXtreme Gradient Boosting (XGBoost) as basic learners; and level 3 with XGBoost as meta-learner. We confirmed that the values of overall accuracy, kappa, precision, sensitivity, specificity, and sensitivity in the SEL model were all significantly higher than those in basic models alone, based on both spectral features and a combination of morphological and spectral features. Furthermore, we also developed a feature filtering process and successfully selected 5 optimal features out of 33 ones, which corresponded to the contents of chlorophyll, anthocyanin, fat, and moisture in seeds. Our SEL model in MSI data analysis provided a new way for seed identification, and the feature filter process potentially could be used widely for development of a low-cost and narrow-channel sensor. Artificial intelligence ; Multispectral image ; Linear discriminant analysis ; Pattern recognition (psychology) ; Principal component analysis ; Dimensionality reduction ; Support vector machine ; Computer science ; Random forest ; Extreme learning machine ; Feature extraction ; Mathematics ; Sensitivity (control systems) ; Lasso (programming language) ; Machine learning ; Artificial neural network ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mariangela Guastaferro | Lucia Baldino | Stefano Cardea | Ernesto Reverchon
Source: Applied Sciences (article, 2022-12-29)
Field: Chemistry — Spectroscopy

Different Drying Techniques Can Affect the Adsorption Properties of Agarose-Based Gels for Crystal Violet Removal Agarose-based gels were produced either by freeze-drying or by supercritical drying for crystal violet (CV) removal from aqueous solutions. The microporosity features of these structures highly affected the final adsorption properties. In particular, agarose cryogels were characterized by a macroporous and irregular morphology, with a low value of specific surface area (11 ± 6 m2/g) with respect to the nanoporous agarose aerogels (154 ± 12 m2/g). To test the efficacy of CV removal, two different types of adsorption test were performed, i.e., batch-mode and multi-step mode. Operating in the multi-step mode, the adsorption performance was larger both for cryogels and aerogels, since this adsorption method allowed a more effective contact between CV and agarose adsorbent. In particular, using 300 mg of cryogels, a removal efficiency of 74% was achieved; using the same quantity of aerogels, 96% of removal efficiency was reached after eight steps of adsorption. Desorption of CV from aerogels was realized using ascorbic acid and, after regeneration, 93% of removal efficiency was preserved, even after three cycles in multi-step filtration mode. Agarose ; Adsorption ; Crystal violet ; Materials science ; Chemical engineering ; Desorption ; Aqueous solution ; Chromatography ; Nanoporous ; Chemistry ; Nanotechnology ; Organic chemistry Aerogels and thermal insulation ; Freezing and Crystallization Processes ; Fluoride Effects and Removal
Authors: Byung Do Lee | Jin-Woong Lee | Joonseo Park | Min Young Cho | Woon Bae Park | Kee‐Sun Sohn
Source: RSC Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Argyrodite configuration determination for DFT and AIMD calculations using an integrated optimization strategy When constructing a partially occupied model structure for use in density functional theory (DFT) and <i>ab initio</i> molecular dynamics (AIMD) calculations, the selection of appropriate configurations has been a vexing issue. Random sampling and the ensuing low-Coulomb-energy entry selection have been routine. Here, we report a more efficient way of selecting low-Coulomb-energy configurations for a representative solid electrolyte, Li<sub>6</sub>PS<sub>5</sub>Cl. Metaheuristics (genetic algorithm, particle swarm optimization, cuckoo search, and harmony search), Bayesian optimization, and modified deep Q-learning are utilized to search the large configurational space. Ten configuration candidates that exhibit relatively low Coulomb energy values and thereby lead to more convincing DFT and AIMD calculation results are pinpointed along with computational cost savings by the assistance of the above-described optimization algorithms, which constitute an integrated optimization strategy. Consequently, the integrated optimization strategy outperforms the conventional random sampling-based selection strategy. Bayesian optimization ; Random search ; Cuckoo search ; Harmony search ; Particle swarm optimization ; Computer science ; Metaheuristic ; Mathematical optimization ; Density functional theory ; Coulomb ; Selection (genetic algorithm) ; Ab initio ; Algorithm ; Chemistry ; Physics ; Computational chemistry ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Ernesto J. Blancas | José J. Plata | Julia Santana-Andreo | Felipe Lemus-Prieto | Antonio M. Márquez | Javier Fdez. Sanz
Source: Journal of Materials Chemistry A (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Unraveling the role of chemical composition in the lattice thermal conductivity of oxychalcogenides as thermoelectric materials Oxychalcogenides represent a large chemical space with potential application as thermoelectric materials due to their low thermal conductivity. Thermal conductivity ; Thermoelectric effect ; Thermoelectric materials ; Materials science ; Chemical composition ; Lattice (music) ; Thermal ; Thermodynamics ; Composite material ; Physics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Organic and Molecular Conductors Research
Authors: Chi-Huan Tung | Shou-Yi Chang | Hsin‐Lung Chen | Yangyang Wang | Kunlun Hong | Jan‐Michael Y. Carrillo | Bobby G. Sumpter | Yuya Shinohara | Changwoo Do | Wei‐Ren Chen
Source: The Journal of Chemical Physics (article, 2022-04-04)
Field: Chemistry — Organic Chemistry

Small angle scattering of diblock copolymers profiled by machine learning We outline a machine learning strategy for quantitively determining the conformation of AB-type diblock copolymers with excluded volume effects using small angle scattering. Complemented by computer simulations, a correlation matrix connecting conformations of different copolymers according to their scattering features is established on the mathematical framework of a Gaussian process, a multivariate extension of the familiar univariate Gaussian distribution. We show that the relevant conformational characteristics of copolymers can be probabilistically inferred from their coherent scattering cross sections without any restriction imposed by model assumptions. This work not only facilitates the quantitative structural analysis of copolymer solutions but also provides the reliable benchmarking for the related theoretical development of scattering functions. Scattering ; Copolymer ; Gaussian ; Statistical physics ; Univariate ; Matrix (chemical analysis) ; Computer science ; Materials science ; Physics ; Multivariate statistics ; Optics ; Polymer ; Machine learning ; Nuclear magnetic resonance ; Quantum mechanics Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; NMR spectroscopy and applications
Authors: Xueyan Zhu | De-yu Shen | Ruipeng Wang | Yili Zheng | Shuchai Su | Fengjun Chen
Source: Foods (article, 2022-11-25)
Field: Chemistry — Analytical Chemistry

Maturity Grading and Identification of Camellia oleifera Fruit Based on Unsupervised Image Clustering Maturity grading and identification of <i>Camellia oleifera</i> are prerequisites to determining proper harvest maturity windows and safeguarding the yield and quality of Camellia oil. One problem in <i>Camellia oleifera</i> production and research is the worldwide confusion regarding the grading and identification of <i>Camellia oleifera</i> fruit maturity. To solve this problem, a <i>Camellia oleifera</i> fruit maturity grading and identification model based on the unsupervised image clustering model DeepCluster has been developed in the current study. The proposed model includes the following two branches: a maturity grading branch and a maturity identification branch. The proposed model jointly learns the parameters of the maturity grading branch and maturity identification branch and used the maturity clustering assigned from the maturity grading branch as pseudo-labels to update the parameters of the maturity identification branch. The maturity grading experiment was conducted using a training set consisting of 160 <i>Camellia oleifera</i> fruit samples and 2628 <i>Camellia oleifera</i> fruit digital images collected using a smartphone. The proposed model for grading <i>Camellia oleifera</i> fruit samples and images in training set into the following three maturity levels: unripe (47 samples and 883 images), ripe (62 samples and 1005 images), and overripe (51 samples and 740 images). Results suggest that there was a significant difference among the maturity stages graded by the proposed method with respect to seed oil content, seed soluble protein content, seed soluble sugar content, seed starch content, dry seed weight, and moisture content. The maturity identification experiment was conducted using a testing set consisting of 160 <i>Camellia oleifera</i> fruit digital images (50 unripe, 60 ripe, and 50 overripe) collected using a smartphone. According to the results, the overall accuracy of maturity identification for <i>Camellia oleifera</i> fruit was 91.25%. Moreover, a Gradient-weighted Class Activation Mapping (Grad-CAM) visualization analysis reveals that the peel regions, crack regions, and seed regions were the critical regions for <i>Camellia oleifera</i> fruit maturity identification. Our results corroborate a maturity grading and identification application of unsupervised image clustering techniques and are supported by additional physical and quality properties of maturity. The current findings may facilitate the harvesting process of <i>Camellia oleifera</i> fruits, which is especially critical for the improvement of Camellia oil production and quality. Camellia oleifera ; Grading (engineering) ; Camellia ; Horticulture ; Maturity (psychological) ; Cluster analysis ; Mathematics ; Botany ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Nuts composition and effects ; Horticultural and Viticultural Research
Authors: Rodrigo A. Moreira | Stefan A. L. Weber | Adolfo B. Poma
Source: Molecules (article, 2022-02-01)
Field: Materials Science — Biomaterials

Martini 3 Model of Cellulose Microfibrils: On the Route to Capture Large Conformational Changes of Polysaccharides High resolution data from all-atom molecular simulations is used to parameterize a Martini 3 coarse-grained (CG) model of cellulose I allomorphs and cellulose type-II fibrils. In this case, elementary molecules are represented by four effective beads centred in the positions of O2, O3, C6, and O6 atoms in the D-glucose cellulose subunit. Non-bonded interactions between CG beads are tuned according to a low statistical criterion of structural deviation using the Martini 3 type of interactions and are capable of being indistinguishable for all studied cases. To maintain the crystalline structure of each single cellulose chain in the microfibrils, elastic potentials are employed to retain the ribbon-like structure in each chain. We find that our model is capable of describing different fibril-twist angles associated with each type of cellulose fibril in close agreement with atomistic simulation. Furthermore, our CG model poses a very small deviation from the native-like structure, making it appropriate to capture large conformational changes such as those that occur during the self-assembly process. We expect to provide a computational model suitable for several new applications such as cellulose self-assembly in different aqueous solutions and the thermal treatment of fibrils of great importance in bioindustrial applications. Cellulose ; Fibril ; Aqueous solution ; Molecule ; Materials science ; Molecular dynamics ; Chain (unit) ; Crystallography ; Chemistry ; Polysaccharide ; Chemical physics ; Chemical engineering ; Computational chemistry ; Physics ; Organic chemistry ; Biochemistry Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Lignin and Wood Chemistry
Authors: Chad M. Parish
Source: Microscopy and Microanalysis (article, 2022-03-03)
Field: Materials Science — Materials Chemistry

Cluster Analysis of Combined EDS and EBSD Data to Solve Ambiguous Phase Identifications A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material. Electron backscatter diffraction ; Cluster (spacecraft) ; Crystal (programming language) ; Diffraction ; Merge (version control) ; Hexagonal crystal system ; Crystal structure ; Spectroscopy X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Youngjae Kim | Aniket Tekawade | Sang Soo Lee | Paul Fenter
Source: Geochimica et Cosmochimica Acta (article, 2022-11-15)
Field: Materials Science — Biomaterials

Morphological and crystallographic controls in the replacement of calcite and aragonite by cerussite and otavite Calcite ; Aragonite ; Dissolution ; Abiogenic petroleum origin ; Chemistry ; Mineralogy ; Geology ; Carbonate ; Chemical engineering ; Inorganic chemistry ; Crystallography ; Physical chemistry Calcium Carbonate Crystallization and Inhibition ; Mine drainage and remediation techniques ; Geochemistry and Elemental Analysis
Authors: Alexis Alvear‐Jiménez | Irene Zabala Gutiérrez | Yingli Shen | Gonzalo Villaverde | Laura Lozano-Chamizo | Pablo Guardia | Miguel Tinoco | Beatriz García-Pinel | José Prados | Consolación Melguizo | Juan Manuel López‐Romero | Daniel Jaque | Marco Filice | Rafael Contreras‐Cáceres
Source: Pharmaceutics (article, 2022-01-17)
Field: Materials Science — Materials Chemistry

Electrospraying as a Technique for the Controlled Synthesis of Biocompatible PLGA@Ag2S and PLGA@Ag2S@SPION Nanocarriers with Drug Release Capability Ag<sub>2</sub>S nanoparticles are near-infrared (NIR) probes providing emission in a specific spectral range (~1200 nm), and superparamagnetic iron oxide nanoparticles (SPION) are colloidal systems able to respond to an external magnetic field. A disadvantage of Ag<sub>2</sub>S NPs is the attenuated luminescent properties are reduced in aqueous media and human fluids. Concerning SPION, the main drawback is the generation of undesirable clusters that reduce particle stability. Here, we fabricate biocompatible hybrid nanosystems combining Ag<sub>2</sub>S NPs and SPION by the electrospraying technique for drug delivery purposes. These nanostructures are composed of poly(lactic-co-glycolic acid) (PLGA) as the polymeric matrix in connection with both Ag<sub>2</sub>S NPs and SPIONs. Initially, we fabricate a hybrid colloidal nanosystem composed of Ag<sub>2</sub>S NPs in connection with PLGA (PLGA@Ag<sub>2</sub>S) by three different routes, showing good photoluminescent (PL) properties with relatively high average decay times. Then, we incorporate SPIONs, obtaining a PLGA polymeric matrix containing both Ag<sub>2</sub>S NPs and SPION (PLGA@Ag<sub>2</sub>S@SPION). Interestingly, in this hybrid system, the location of Ag<sub>2</sub>S NPs and SPIONs depends on the synthesis route performed during electrospraying. After a detailed characterization, we demonstrate the encapsulation and release capabilities, obtaining the kinetic release using a model chemotherapeutic drug (maslinic acid). Finally, we perform in vitro cytotoxicity assays using drug-loaded hybrid systems against several tumor cell lines. PLGA ; Nanocarriers ; Nanotechnology ; Nanoparticle ; Materials science ; Drug delivery ; Controlled release ; Superparamagnetism ; Chemical engineering Advanced Nanomaterials in Catalysis ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Tatiana Konstantinova | Phillip M. Maffettone | Bruce Ravel | Stuart I. Campbell | Andi Barbour | Daniel Olds
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning enabling high-throughput and remote operations at large-scale user facilities Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Throughput ; Computer science ; Scale (ratio) ; Human–computer interaction ; Operating system ; Geography Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced X-ray and CT Imaging
Authors: Jian‐Hui Jiang | Shuang Lü | Yulou Ouyang | Jie Chen
Source: Nanomaterials (article, 2022-08-18)
Field: Materials Science — Materials Chemistry

How Hydrodynamic Phonon Transport Determines the Convergence of Thermal Conductivity in Two-Dimensional Materials The phonon Boltzmann transport equation combined with first-principles calculation has achieved great success in exploring the lattice thermal conductivity (κ) of various materials. However, the convergence of the predicted κ is a critical issue, leading to quite scattered results recorded in the literature, even for the same material. In this paper, we explore the origin for the convergence of thermal conductivity in two-dimensional (2D) materials. Two kinds of typical 2D materials, graphene and silicene, are studied, and the bulk silicon is also compared as a control system for a three-dimensional material. The effect of the cutoff radius (rc) in the third-order interatomic force constants on κ is studied for these three materials. It is found that that κ of these three materials exhibits diverse convergence behaviors with respect to rc, which coincides very well with the strength of hydrodynamic phonon transport. By further analyzing the phonon lifetime and scattering rates, we reveal that the dominance of the normal scattering process gives rise to the hydrodynamic phonon transport in both graphene and silicene, which results in long-range interaction and a large lifetime of low-frequency flexural acoustic phonons, while the same phenomenon is absent in bulk silicon. Our study highlights the importance of long-range interaction associated with hydrodynamic phonon transport in determining the thermal conductivity of 2D materials. Phonon ; Thermal conductivity ; Boltzmann equation ; Graphene ; Condensed matter physics ; Silicene ; Materials science ; Scattering ; Phonon scattering ; Physics ; Nanotechnology ; Thermodynamics ; Quantum mechanics ; Composite material Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: Jun-Hwi So | Sung Yong Joe | Seon Ho Hwang | Soon Jung Hong | Seung Hyun Lee
Source: Journal of Animal Science and Technology (review, 2022-09-01)
Field: Chemistry — Analytical Chemistry

Current advances in detection of abnormal egg: a review Internal and external defects of eggs should be detected to prevent cross-contamination of intact eggs by abnormal eggs during storage. Emerging detection technologies for abnormal eggs were introduced as an alternative to human inspection. The advanced technologies could rapidly detect abnormal eggs. Abnormal egg detection technologies using acoustic response, machine vision, and spectroscopy have been commercialized in the poultry industry. Non-destructive egg quality assessment methods meanwhile could preserve the value of eggs and improve detection efficiency. In order to improve detection efficiency, it is essential to select a proper algorithm for classifying the types of abnormal eggs. This review deals with the performance of the detection technologies for various types of abnormal eggs in recently published resources. In addition, the discriminant methods and detection algorithms of abnormal eggs reported in the published literature were investigated. Although the majority of the studies were conducted on a laboratory scale, the developed detection technologies for internal and external defects in eggs were technically feasible to obtain the excellent detection accuracy. To apply the developed detection technologies to the poultry industry, it is necessary to achieve the detection rates required from the industry. Computer science ; Biochemical engineering ; Engineering Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies