jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel del-Pozo-Bueno | Demie Kepaptsoglou | F. Peiró | Sònia Estradé
Source: Ultramicroscopy (article, 2023-08-03)
Field: Materials Science — Materials Chemistry

Comparative of machine learning classification strategies for electron energy loss spectroscopy: Support vector machines and artificial neural networks Machine Learning (ML) strategies applied to Scanning and conventional Transmission Electron Microscopy have become a valuable tool for analyzing the large volumes of data generated by various S/TEM techniques. In this work, we focus on Electron Energy Loss Spectroscopy (EELS) and study two ML techniques for classifying spectra in detail: Support Vector Machines (SVM) and Artificial Neural Networks (ANN). Firstly, we systematically analyze the optimal configurations and architectures for ANN classifiers using random search and the tree-structured Parzen estimator methods. Secondly, a new kernel strategy is introduced for the soft-margin SVMs, the cosine kernel, which offers a significant advantage over the previously studied kernels and other ML classification strategies. This kernel allows us to bypass the normalization of EEL spectra, achieving accurate classification. This result is highly relevant for the EELS community since we also assess the impact of common normalization techniques on our spectra using Uniform Manifold Approximation and Projection (UMAP), revealing a strong bias introduced in the spectra once normalized. In order to evaluate and study both classification strategies, we focus on determining the oxidation state of transition metals through their EEL spectra, examining which feature is more suitable for oxidation state classification: the oxygen K peak or the transition metal white lines. Subsequently, we compare the resistance to energy loss shifts for both classifiers and present a strategy to improve their resistance. The results of this study suggest the use of soft-margin SVMs for simpler EELS classification tasks with a limited number of spectra, as they provide performance comparable to ANNs while requiring lower computational resources and reduced training times. Conversely, ANNs are better suited for handling complex classification problems with extensive training data. Support vector machine ; Artificial intelligence ; Artificial neural network ; Machine learning ; Hyperparameter optimization ; Normalization (sociology) ; Pattern recognition (psychology) ; Computer science ; Electron energy loss spectroscopy ; Physics ; Optics ; Transmission electron microscopy Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Brian Zutter | Zejie Chen | Luisa Barrera | William Gaieck | Aliya S. Lapp | K. Watanabe | Akihiko Kudo | Daniel V. Esposito | Rohini Bala Chandran | Shane Ardo | A. Alec Talin
Source: ACS Nano (article, 2023-05-10)
Field: Materials Science — Materials Chemistry

Single-Particle Measurements Reveal the Origin of Low Solar-to-Hydrogen Efficiency of Rh-Doped SrTiO<sub>3</sub> Photocatalysts Solar-powered photochemical water splitting using suspensions of photocatalyst nanoparticles is an attractive route for economical production of green hydrogen. SrTiO<sub>3</sub>-based photocatalysts have been intensely investigated due to their stability and recently demonstrated near-100% external quantum yield (EQY) for water splitting using wavelengths below 360 nm. To extend the optical absorption into the visible, SrTiO<sub>3</sub> nanoparticles have been doped with various transition metals. Here we demonstrate that doping SrTiO<sub>3</sub> nanoparticles with 1% Rh introduces midgap acceptor states which reduce the free electron concentration by 5 orders of magnitude, dramatically reducing built-in potentials which could otherwise separate electron-hole (e-h) pairs. Rhodium states also function as recombination centers, reducing the photocarrier lifetime by nearly 2 orders of magnitude and the maximum achievable EQY to 10%. Furthermore, the absence of built-in electric fields within Rh-doped SrTiO<sub>3</sub> nanoparticles suggests that modest e-h separation can be achieved by exploiting a difference in mobility between electrons and holes. Rhodium ; Doping ; Materials science ; Water splitting ; Nanoparticle ; Hydrogen ; Acceptor ; Electron ; Absorption (acoustics) ; Quantum yield ; Optoelectronics ; Electron acceptor ; Chemical physics ; Photochemistry ; Photocatalysis ; Nanotechnology ; Chemistry ; Catalysis ; Optics ; Condensed matter physics ; Physics Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques ; Ga2O3 and related materials
Authors: Nicholas J. Williams | Conor Osborne | Ieuan D. Seymour | Martin Z. Bazant | Stephen J. Skinner
Source: Electrochemistry Communications (article, 2023-03-21)
Field: Materials Science — Materials Chemistry

Application of finite Gaussian process distribution of relaxation times on SOFC electrodes Electrochemical impedance spectroscopy (EIS) is a powerful tool in characterisation of processes in electrochemical systems, allowing us to elucidate the resistance and characteristic frequency of physical properties such as reaction and transport rates. The essence of EIS is the relationship between current and potential at a given frequency. However, it is often the case that we do not understand the electrochemical system well enough to fit a meaningful physical model to EIS data. The distribution of relaxation times (DRT) calculation assumes an infinite series of relaxation processes distributed over a characteristic timescale. The DRT calculation may identify the number of processes occurring, as well as their respective resistivity and characteristic timescale, and may resolve processes which have relatively similar timescales. Using a nonparametric tool known as Gaussian process (GP) regression, we showcase a method of finding a unique solution to the ill-posed DRT problem by optimising kernel hyperparameters as opposed to ad-hoc regularisation. In this work, we use finite GP regression under inequality constraints (fGP) to analysed EIS data generated by a (Ni/CGO|CGO|YSZ|Reference Cathode) solid-oxide fuel cell in a gas mixture of 0.5 bar H2/0.5 bar H2O and at a temperature of 600 °C. By varying the current density, we can characterise the current-voltage relationship of the electrode and shed light on the reaction mechanism governing charge transfer at the solid-gas interface. Our findings also show that even at relatively high current densities (±600mAcm-2) the electrode process is limited by charge transfer. Dielectric spectroscopy ; Cathode ; Electrode ; Gaussian ; Current (fluid) ; Solid oxide fuel cell ; Materials science ; Relaxation (psychology) ; Chemistry ; Statistical physics ; Electrochemistry ; Thermodynamics ; Physics ; Anode ; Computational chemistry ; Physical chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Spectroscopy and Chemometric Analyses
Authors: Robbie J. Iuliucci | Joshua D. Hartman | Gregory J. O. Beran
Source: The Journal of Physical Chemistry A (article, 2023-03-20)
Field: Chemistry — Spectroscopy

Do Models beyond Hybrid Density Functionals Increase the Agreement with Experiment for Predicted NMR Chemical Shifts or Electric Field Gradient Tensors in Organic Solids? <i>Ab initio</i> predictions of chemical shifts and electric field gradient (EFG) tensor components are frequently used to help interpret solid-state nuclear magnetic resonance (NMR) experiments. Typically, these predictions employ density functional theory (DFT) with generalized gradient approximation (GGA) functionals, though hybrid functionals have been shown to improve accuracy relative to experiment. Here, the performance of a dozen models beyond the GGA approximation are examined for the prediction of solid-state NMR observables, including meta-GGA, hybrid, and double-hybrid density functionals and second-order Møller-Plesset perturbation theory (MP2). These models are tested on organic molecular crystal data sets containing 169 experimental <sup>13</sup>C and <sup>15</sup>N chemical shifts and 114 <sup>17</sup>O and <sup>14</sup>N EFG tensor components. To make these calculations affordable, gauge-including projector augmented wave (GIPAW) Perdew-Burke-Ernzerhof (PBE) calculations with periodic boundary conditions are combined with a local intramolecular correction computed at the higher level of theory. Within the context of typical NMR property calculations performed on a static, DFT-optimized crystal structure, the benchmarking finds that the double-hybrid DFT functionals produce errors versus experiment that are no smaller than those of hybrid functionals in the best cases, and they can be larger. MP2 errors versus experiment are even bigger. Overall, no practical advantages are found for using any of the tested double-hybrid functionals or MP2 to predict experimental solid-state NMR chemical shifts and EFG tensor components for routine organic crystals, especially given the higher computational cost of those methods. This finding likely reflects error cancellation benefiting the hybrid functionals. Improving the accuracy of the predicted chemical shifts and EFG tensors relative to experiment would probably require more robust treatments of the crystal structures, their dynamics, and other factors. Chemical shift ; Field (mathematics) ; Electric field gradient ; Physics ; Materials science ; Statistical physics ; Electric field ; Nuclear magnetic resonance ; Mathematics ; Quantum mechanics ; Pure mathematics Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Molecular spectroscopy and chirality
Authors: Jeevithan Elango | Camilo Zamora‐Ledezma | José Eduardo Maté Sánchez de Val
Source: Journal of Composites Science (article, 2023-09-13)
Field: Materials Science — Biomaterials

Natural vs Synthetic Polymers: How Do They Communicate with Cells for Skin Regeneration—A Review Modern research has evolved several approaches toward skin regeneration and one of the novel concerns is the use of polymer-based systems due to their excellent beneficial properties to the skin. Several polymers, such as cellulose, hyaluronan, alginate, chitosan, collagen, fibrin and fibroin, have been tested and have proven the benefits for skin regeneration, and most of them are derived from either polysaccharide- or protein-based materials. In order to understand the mode of action, several researchers investigated the cell–matrix interaction and possible signaling mechanism in skin regeneration. Not only the signaling mechanism but also the mode of cell communication determines the application of polysaccharide- and protein-based polymers in practice. Based on the above significance, this review disclosed the recent findings to compile a possible method of communication between cells and polymers derived from polysaccharide-based (such as cellulose, hyaluronan, chitosan, alginate, agar, and xanthan gum) and protein-based (such as collagen, gelatin, fibrin, and silk fibroin) materials along with other polymers, such as poly(vinyl alcohol), polyglycolide or poly(glycolic acid), or poly(lactic acid) in skin regeneration. Accordingly, this review addresses the fundamental concept of cell–matrix communication, which helps us to understand the basis of the polymer’s functions in the biomedical field. Fibroin ; Regeneration (biology) ; Chitosan ; Polymer ; Gelatin ; Extracellular matrix ; Materials science ; Polysaccharide ; Polymer science ; Tissue engineering ; Chemistry ; Biomedical engineering ; Biochemistry ; SILK ; Cell biology ; Biology ; Composite material Silk-based biomaterials and applications ; Wound Healing and Treatments ; Electrospun Nanofibers in Biomedical Applications
Authors: Bojana Ranković | Ryan‐Rhys Griffiths | Henry B. Moss | Philippe Schwaller
Source: Digital Discovery (article, 2023-11-02)
Field: Materials Science — Materials Chemistry

Bayesian optimisation for additive screening and yield improvements – beyond one-hot encoding Cost-effective Bayesian optimisation screening of 720 additives on four complex reactions, achieving substantial yield improvements over baselines using chemical reaction representations beyond one-hot encoding. Encoding (memory) ; Yield (engineering) ; Bayesian probability ; Computer science ; Biochemical engineering ; Machine learning ; Artificial intelligence ; Engineering ; Materials science Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques ; Advanced Photocatalysis Techniques
Authors: Junsong Zhu | Baosheng Sun | Jianrong Cai | Yongjian Xu | Feng Lu | Haile Ma
Source: Quality Assurance and Safety of Crops & Foods (article, 2023-07-01)
Field: Chemistry — Analytical Chemistry

Inspection and classification of wheat quality using image processing Wheat plays an important role in our daily life and industrial production. Several computer vision approaches have been proposed for classifying wheat quality, but there were some methods focusing on the problem of cohesive wheats while image processing. In this paper, we designed a single kernel guide groove to separate the cohesive wheats, which could simplify the algorithm of image processing and improve the accuracy rate of classification. For the method followed while recording the data, the image information must be converted into digital information, and the results are provided using appropriate image processing algorithms. Image preprocessing steps such as binarization, image enhancement, image segmentation, and morphological processing were used to reduce noise. For image segmentation, we proposed the following new segmentation methods: (1) extracting wheat region by converting image to H channel and (2) watershed algorithm based on Euclidean distance transformation. For the classification model, 22 features of 7 different qualities of wheat were inputted in the Back Propagation (BP) neural network and Support Vector Machine (SVM) model, and the overall correct classification rates were determined to be 91% and 97% for SVM and BP neural network, respectively. The BP neural network was more suitable for wheat appearance quality detection. Artificial intelligence ; Computer science ; Support vector machine ; Artificial neural network ; Pattern recognition (psychology) ; Image processing ; Preprocessor ; Image segmentation ; Digital image processing ; Computer vision ; Kernel (algebra) ; Digital image ; Segmentation ; Image (mathematics) ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Kritesh Kumar Gupta | T. Mukhopadhyay | S. Dey
Source: Applied Surface Science (article, 2023-01-27)
Field: Materials Science — Materials Chemistry

Probing the molecular-level energy absorption mechanism and strategic sequencing of graphene/Al composite laminates under high-velocity ballistic impact of nano-projectiles Motivated by recent discoveries concerning the extreme superiority of multilayer graphene in terms of kinetic energy dissipation compared to conventional monolithic materials, this article investigates the ballistic performance and physics-informed strategic sequencing of graphene-reinforced aluminum laminates under the influence of random disorder based on extensive molecular-level simulations of high-velocity impact. It is unraveled that strategic sequencing of graphene layers within the aluminum matrix can significantly enhance kinetic energy absorption, while preventing complete penetration. However, the reinforcement of bilayer graphene increases the projectile's post-impact residual velocity due to high magnitude of stress wave release provided by the reinforcement. We have further mitigated this effect to a significant extent by increasing the effective thickness of Al laminates. Based on the insights gained by a series of molecular-level simulations, we have proposed hybrid multifunctional laminates by coupling two individual configurations with high energy absorption and nopenetration, respectively. By strategically providing higher graphene concentration near target surfaces, up to 90.77% of the kinetic energy can be absorbed. The findings of this study would be crucially useful in materializing the bottom-up multi-scale design pathway for producing graphene-reinforced Al composites to develop a novel class of functional barrier material-based engineered surfaces with improved nano-scale ballistic performance. Graphene ; Projectile ; Materials science ; Ballistic limit ; Dissipation ; Molecular dynamics ; Composite material ; Kinetic energy ; Ballistic impact ; Composite number ; Nanotechnology ; Physics ; Computational chemistry ; Chemistry Graphene research and applications ; High-Velocity Impact and Material Behavior ; Boron and Carbon Nanomaterials Research
Authors: Mingtang Zeng | Yao Wang | Maozhu Liu | Yuxun Wei | Jie Wen | Yu-Chen Zhang | Tao Chen | Nianyu He | Ping Fan | Xinhua Dai
Source: International Journal of Nanomedicine (review, 2023-11-01)
Field: Materials Science — Materials Chemistry

Potential Efficacy of Herbal Medicine-Derived Carbon Dots in the Treatment of Diseases: From Mechanism to Clinic Carbon dots (CDs), a crucial component of nanomaterials, are zero-dimensional nanomaterials with carbon as the backbone structure and smaller than 10 nm. Due to their beneficial characteristics, they are widely used in biomedical fields such as biosensors, drug delivery, bio-imaging, and interactions with DNA. Interestingly, a novel type of carbon dot, generated by using herbal medicines as synthetic raw materials, has emerged as the most recent incomer in the family of CDs with the extensive growth in the number of materials selected for carbon dots synthesis. Herbal medicine-derived carbon dots (HM-CDs) have been employed in the biomedical industry, and are rapidly emerging as "modern nanomaterials" due to their unique structures and exceptional capabilities. Emerging trends suggest that their specific properties can be used in bleeding disorders, gastrointestinal disorders, inflammation-related diseases, and other common intractable diseases including cancer, menopausal syndrome, central nervous system disorders, and pain of various forms and causes. In addition, HM-CDs have been found to have organ-protective and antioxidant properties, as evidenced by extensive studies. This research provides a more comprehensive understanding of the biomedical applications of HM-CDs for the aforementioned disorders and investigates the intrinsic pharmacological activities and mechanisms of these HM-CDs to further advance their clinical applications. Nanotechnology ; Nanomaterials ; Mechanism (biology) ; Drug delivery ; Carbon fibers ; Medicine ; Materials science ; Composite number Carbon and Quantum Dots Applications ; Curcumin's Biomedical Applications ; Graphene and Nanomaterials Applications
Authors: Di Wang | Fengyuan Zhao | Rui Wang | Junwei Guo | Cihai Zhang | Huimin Liu | Yongsheng Wang | Guohao Zong | Le Zhao | Weihua Feng
Source: Frontiers in Plant Science (article, 2023-05-12)
Field: Chemistry — Analytical Chemistry

A Lightweight convolutional neural network for nicotine prediction in tobacco by near-infrared spectroscopy The content of nicotine, a critical component of tobacco, significantly influences the quality of tobacco leaves. Near-infrared (NIR) spectroscopy is a widely used technique for rapid, non-destructive, and environmentally friendly analysis of nicotine levels in tobacco. In this paper, we propose a novel regression model, Lightweight one-dimensional convolutional neural network (1D-CNN), for predicting nicotine content in tobacco leaves using one-dimensional (1D) NIR spectral data and a deep learning approach with convolutional neural network (CNN). This study employed Savitzky-Golay (SG) smoothing to preprocess NIR spectra and randomly generate representative training and test datasets. Batch normalization was used in network regularization to reduce overfitting and improve the generalization performance of the Lightweight 1D-CNN model under a limited training dataset. The network structure of this CNN model consists of four convolutional layers to extract high-level features from the input data. The output of these layers is then fed into a fully connected layer, which uses a linear activation function to output the predicted numerical value of nicotine. After the comparison of the performance of multiple regression models, including support vector regression (SVR), partial least squares regression (PLSR), 1D-CNN, and Lightweight 1D-CNN, under the preprocessing method of SG smoothing, we found that the Lightweight 1D-CNN regression model with batch normalization achieved root mean square error (RMSE) of 0.14, coefficient of determination (<i>R</i> <sup>2</sup>) of 0.95, and residual prediction deviation (RPD) of 5.09. These results demonstrate that the Lightweight 1D-CNN model is objective and robust and outperforms existing methods in terms of accuracy, which has the potential to significantly improve quality control processes in the tobacco industry by accurately and rapidly analyzing the nicotine content. Convolutional neural network ; Normalization (sociology) ; Overfitting ; Computer science ; Smoothing ; Artificial intelligence ; Pattern recognition (psychology) ; Residual ; Mean squared error ; Partial least squares regression ; Mathematics ; Algorithm ; Artificial neural network ; Statistics ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ana Ligero | M. Calero | M.A. Martín‐Lara | G. Blázquez | Rafael R. Solís | Antonio Pérez
Source: Journal of CO2 Utilization (article, 2023-06-17)
Field: Materials Science — Polymers and Plastics

Fixed-bed CO2 adsorption onto activated char from the pyrolysis of a non-recyclable plastic mixture from real urban residues The potential use of activated char obtained from the pyrolysis of a mixture of non-recyclable plastics for the adsorption of CO2 in fixed-bed columns has been explored. The rejected fraction of plastics collected in a municipal solid treatment plant was pyrolyzed and the resulting char residue was activated to develop a porous carbonaceous material. The dynamic behavior of CO2 adsorption was assessed by the breakthrough curves obtained in continuous column tests. Among all the available models, the curves were successfully fitted to the dose-response model. The effect of adsorption temperature (15–45 ºC), the inlet CO2 concentration (10–40 %, vol.), and the adsorbent length of the bed (loadings, 1–2 g) on the efficiency of the process was evaluated by the surface response methodology applying an adaptative neural fuzzy inference system (ANFIS). A temperature rise exerted a negative effect on the adsorption capacity due to the physisorption properties of the process, the fed CO2 concentration displayed a positive effect and the fixed-bed length did not play a remarkable influence. Adsorption ; Physisorption ; Char ; Pyrolysis ; Chemical engineering ; Materials science ; Activated carbon ; Porosity ; Chromatography ; Composite material ; Chemistry ; Organic chemistry Polymer Foaming and Composites ; Carbon Dioxide Capture Technologies ; Municipal Solid Waste Management