jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lang Lü | Wanli Xiong | Can Hu
Source: Scientific Reports (article, 2022-06-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Equivalent magnetic circuit method of estimating iron losses in induction motor spindles The iron losses in the motor of motorized spindles have a significant effect on their heat generation, thermal deformation, and machining accuracy. The equivalent magnetic circuit (EMC) method for estimating iron losses in the spindle motor is proposed, where the magnetic flux density distribution of any cross section inside the spindle motor is assumed as a uniform one. A mechanical loss separation method of no load running combined with a sudden loss of power supply is also proposed. The EMC method is verified by prototype experiment and a different analysis method comparison. The EMC does not need to solve complex electromagnetic fields, and to do 2D or 3D eddy current analysis and the corresponding post-processing. There is only need to perform a simple magnetic circuit calculation. Therefore, it can realize a fast analysis and prediction. The proposed mechanical loss separation method requires only one prototype during a whole testing process. There is no need for any other same prototype and a coupling device. It is simpler, and can eliminate the braking torque and electromagnetic losses of the spindle motor. Magnetic circuit ; Eddy current ; Torque ; Coupling (piping) ; Control theory (sociology) ; Power (physics) ; Electromagnetic interference ; Magnetic flux ; Computer science ; Equivalent circuit ; Induction motor ; Automotive engineering ; Mechanical engineering ; Magnet ; Electrical engineering ; Physics ; Engineering ; Magnetic field ; Voltage Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Non-Destructive Testing Techniques
Authors: Yong Hao | Xiyan Li | Chengxiang Zhang | Zuxiang Lei
Source: Frontiers in Nutrition (article, 2022-10-18)
Field: Chemistry — Analytical Chemistry

Research on construction method and validity mechanism of robust analysis model in snow peach quality detection based on visible-near infrared spectroscopy Visible-near infrared (Vis-NIR) spectra analysis method is widely used in the quality grading of bulk fruits with its rapid, non-destructive, diverse detection modes and flexible modular integration scheme. However, during the online grading of fruits, the random mechanized way of dropping fruit onto the conveyor belt method and the open detection environment led to more spectral abnormal samples, which affect the accuracy of the detection. In this paper, the soluble solids content (SSC) of snow peach is quantitatively analyzed by static and online detection methods. Several spectral preprocessing methods including Norris-Williams Smoothing (NWS), Savitzky-Golay Smoothing (SGS), Continuous Wavelet Derivative (CWD), Multivariate Scattering Correction (MSC), and Variable Sorting for Normalization (VSN) are adopted to eliminate spectral rotation and translation errors and improve the signal-to-noise ratio. Monte Carlo Uninformative Variable Elimination (MCUVE) method is used for the selection of optimal characteristic modeling variables. Partial Least Squares Regression (PLSR) is used to model and analyze the preprocessed spectra and the spectral variables optimized by MCUVE, and the effectiveness of the method is evaluated. Sparse Partial Least Squares Regression (SPLSR) and Sparse Partial Robust M Regression (SPRMR) are used for the construction of robust models. The results showed that the SGS preprocessing method can effectively improve the analysis accuracy of static and online models. The MCUVE method can realize the extraction of stable characteristic variables. The SPRMR model based on SGS preprocessing method and the effective variables has the optimal analysis results. The analysis accuracy of snow peach static model is slightly better than that of online analytical model. Through the test results of the PLSR, SPLSR and SPRMR models by the artificially adding noise test method, it can be seen that the SPRMR method eliminates the influence of abnormal samples on the model during the modeling process, which can effectively improve the anti-noise ability and detection reliability. Partial least squares regression ; Smoothing ; Computer science ; Preprocessor ; Artificial intelligence ; Mathematics ; Pattern recognition (psychology) ; Computer vision ; Machine learning Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing in Agriculture
Authors: Rongnian Tang | Xiaowei Li | Chuang Li | Kaixuan Jiang | Wenfeng Hu | Jingjin Wu
Source: Electronics (article, 2022-06-22)
Field: Chemistry — Analytical Chemistry

Estimation of Total Nitrogen Content in Rubber Plantation Soil Based on Hyperspectral and Fractional Order Derivative Soil total nitrogen (TN) is a vital nutrient element that affects the growth and rubber production of rubber trees. Especially in the coastal environment, soil nutrients will show significant differences. Using hyperspectral technology to detect soil nitrogen ion content in the offshore environment can provide technical support for nutrient management. Preprocessing hyperspectral data is a crucial step in accurate spectral model estimation. At the same time, it is considered that the traditional first-order and second-order derivatives are easily unbalanced between the signal-to-noise ratio, resulting in the loss of adequate information. Therefore, this work focuses on the feasibility of fractional order derivative (FOD) combined with partial least squares regression (PLSR) to estimate its TN content. By collecting soil samples from rubber plantations, the TN content of the soil samples was determined, and the spectral reflectance was measured. The FOD of the original spectrum was preprocessed with an interval of 0.2, and 11 spectral curves were obtained. Then, successive projections algorithm (SPA) was used to extract spectral features, and partial least squares regression (PLSR) models of soil TN content were established. The research results show that compared with the traditional integer derivative, FOD has a tremendous advantage in balancing spectral information and noise and can provide more abundant characteristic variables, which helps establish a more robust estimation model. In the range of orders 0–2, the model established by the 1.8-order is the best. Under that circumstance, the determination coefficients of validation (R2v) is 0.649, and the ratio of the performance to deviation (RPD) is 1.72. Combined with FOD, it is feasible and practical to establish an accurate and rapid estimation model of soil TN content, which can provide an important reference for large-scale detection of soil TN content in rubber plantations. Hyperspectral imaging ; Partial least squares regression ; Mathematics ; Natural rubber ; Statistics ; Noise (video) ; Soil science ; Environmental science ; Computer science ; Chemistry ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Remote Sensing in Agriculture
Authors: J. Dubois | Didier Fraix‐Burnet | J. Moultaka | P. Sharma | D. Burgarella
Source: Astronomy and Astrophysics (article, 2022-05-17)
Field: Chemistry — Analytical Chemistry

Unsupervised classification of CIGALE galaxy spectra Aims. Our study aims at providing deeper insight into the power and limitation of an unsupervised classification algorithm (called Fisher-EM) on spectra of galaxies. This algorithm uses a Gaussian mixture in a discriminative latent subspace. To this end, we investigate the capacity of this algorithm to segregate the physical parameters used to generate mock spectra and the influence of the noise on the classification. Methods. With the code CIGALE and different values for nine input parameters characterising the stellar population, we simulated a sample of 11 475 optical spectra of galaxies containing 496 monochromatic fluxes. The statistical model and the optimum number of clusters are given in Fisher-EM by the integrated completed likelihood (ICL) criterion. We repeated the analyses several times to assess the robustness of the results. Results. Two distinct classifications can be distinguished in the case of the noiseless spectra. The classification with more than 13 clusters disappears when noise is added, while the classification with 12 clusters is very robust against noise down to a signal-to-noise ratio (S/N) of 3. At S / N = 1, the optimum is 5 clusters, but the classification is still compatible with the previous classification. The distribution of the parameters used for the simulation shows an excellent discrimination between classes. A higher dispersion both in the spectra within each class and in the parameter distribution leads us to conclude that despite a much higher ICL, the classification with more than 13 clusters in the noiseless case is not physically relevant. Conclusions. This study yields two conclusions that are valid at least for the Fisher-EM algorithm. Firstly, the unsupervised classification of spectra of galaxies is both reliable and robust to noise. Secondly, such analyses are able to extract the useful physical information contained in the spectra and to build highly meaningful classifications. In an epoch of data-driven astrophysics, it is important to trust unsupervised machine-learning approaches that do not require training samples that are unavoidably biased. Mixture model ; Pattern recognition (psychology) ; Discriminative model ; Robustness (evolution) ; Spectral line ; Subspace topology ; Galaxy ; Gaussian ; Computer science ; Artificial intelligence ; Mathematics ; Statistics ; Astrophysics ; Physics Spectroscopy and Chemometric Analyses ; Blind Source Separation Techniques ; Gaussian Processes and Bayesian Inference
Authors: Jie Peng | W. Ryan Deskins | Anter El‐Azab
Source: Computational Materials Science (article, 2022-05-21)
Field: Materials Science — Materials Chemistry

Monte-Carlo modeling of phonon thermal transport using DFT-based anisotropic dispersion relations over the full Brillouin zone Phonon ; Boltzmann equation ; Brillouin zone ; Thermal conductivity ; Anisotropy ; Condensed matter physics ; Phonon scattering ; Monte Carlo method ; Relaxation (psychology) ; Dispersion (optics) ; Physics ; Scattering ; Dispersion relation ; Isotropy ; Materials science ; Optics ; Quantum mechanics ; Mathematics Thermal properties of materials ; Thermal Expansion and Ionic Conductivity ; Nuclear Materials and Properties
Authors: Keir Adams | Connor W. Coley
Source: arXiv (Cornell University) (preprint, 2022-10-06)
Field: Materials Science — Materials Chemistry

Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design Shape-based virtual screening is widely employed in ligand-based drug design to search chemical libraries for molecules with similar 3D shapes yet novel 2D chemical structures compared to known ligands. 3D deep generative models have the potential to automate this exploration of shape-conditioned 3D chemical space; however, no existing models can reliably generate valid drug-like molecules in conformations that adopt a specific shape such as a known binding pose. We introduce a new multimodal 3D generative model that enables shape-conditioned 3D molecular design by equivariantly encoding molecular shape and variationally encoding chemical identity. We ensure local geometric and chemical validity of generated molecules by using autoregressive fragment-based generation with heuristic bonding geometries, allowing the model to prioritize the scoring of rotatable bonds to best align the growing conformational structure to the target shape. We evaluate our 3D generative model in tasks relevant to drug design including shape-conditioned generation of chemically diverse molecular structures and shape-constrained molecular property optimization, demonstrating its utility over virtual screening of enumerated libraries. Chemical space ; Computer science ; Encoding (memory) ; Heuristic ; Virtual screening ; Biological system ; Generative model ; Artificial intelligence ; Generative grammar ; Drug discovery ; Chemistry ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Jannis Born | Matteo Manica
Source: arXiv (Cornell University) (preprint, 2022-02-01)
Field: Materials Science — Materials Chemistry

Regression Transformer enables concurrent sequence regression and generation for molecular language modelling Despite significant progress of generative models in the natural sciences, their controllability remains challenging. One fundamentally missing aspect of molecular or protein generative models is an inductive bias that can reflect continuous properties of interest. To that end, we propose the Regression Transformer (RT), a novel method that abstracts regression as a conditional sequence modeling problem. This introduces a new paradigm of multitask language models which seamlessly bridge sequence regression and conditional sequence generation. We thoroughly demonstrate that, despite using a nominal-scale training objective, the RT matches or surpasses the performance of conventional regression models in property prediction tasks of small molecules, proteins and chemical reactions. Critically, priming the same model with continuous properties yields a highly competitive conditional generative model that outperforms specialized approaches in a substructure-constrained, property-driven molecule generation benchmark. Our dichotomous approach is facilitated by a novel, alternating training scheme that enables the model to decorate seed sequences by desired properties, e.g., to optimize reaction yield. In sum, the RT is the first report of a multitask model that concurrently excels at predictive and generative tasks in biochemistry. This finds particular application in property-driven, local exploration of the chemical or protein space and could pave the road toward foundation models in material design. The code to reproduce all experiments of the paper is available at: https://github.com/IBM/regression-transformer Computer science ; Regression ; Artificial intelligence ; Machine learning ; Generative model ; Generative grammar ; Mathematics ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Ewelina Weidner | Grzegorz Wójcik | Dorota Kołodyńska | Teofil Jesionowski | Filip Ciesielczyk
Source: Journal of Environmental Management (article, 2022-09-24)
Field: Chemistry — Inorganic Chemistry

Insight into the removal of vanadium ions from model and real wastewaters using surface grafted zirconia-based adsorbents: Batch experiments, equilibrium and mechanism study This study concerns the fabrication of CTAB- and N,N-dimethyltetradecylamine-grafted zirconia and evaluation of their ability to adsorb vanadium ions. The effectiveness of ZrO<sub>2</sub> functionalization and the different nature of the modifiers used were confirmed by differences in the porosity (ZrO<sub>2</sub>: S<sub>BET</sub> = 347 m<sup>2</sup>/g; ZrO<sub>2</sub>-CTAB: S<sub>BET</sub> = 375 m<sup>2</sup>/g, ZrO<sub>2</sub>-NH<sup>+</sup>: S<sub>BET</sub> = 155 m<sup>2</sup>/g), types of functional groups, and isoelectric points (the ZrO<sub>2</sub> and CTAB-modified samples have IEPs = 3.8 and 3.9, ZrO<sub>2</sub>-NH<sup>+</sup> has IEP = 7.1) of the prepared adsorbents. The designed materials were tested in batch adsorption experiments involving the removal of vanadium ions from model wastewaters at various process parameters, among which pH proved to be the most important. Based on equilibrium and kinetic evaluations, it was proved that the sorption of V(V) ions followed pseudo-second-order and intraparticle diffusion models, and the data were better fitted to the Langmuir model, suggesting the following order of the sorbents in terms of favorability for V(V) ion adsorption: ZrO<sub>2</sub>-NH<sup>+</sup> > ZrO<sub>2</sub> > ZrO<sub>2</sub>-CTAB. The estimated maximum monolayer capacity of ZrO<sub>2</sub>-NH<sup>+</sup> for V(V) (87.72 mg/g) was the highest among the tested materials. Additionally, it was confirmed that adsorption of V(V) ions onto synthesized materials is a heterogeneous, exothermic, and spontaneous reaction, as evidenced by the calculated values of thermodynamic parameters. The key goals included the transfer of experimental findings obtained using model solutions to the adsorption of V(V) ions from solutions arising from the leaching process of spent catalysts. The highest adsorption efficiencies of 70.8% and 47.5% were recorded for the ZrO<sub>2</sub>-NH<sup>+</sup> material in acidic solution; this may be related to the protonization of -NH<sup>+</sup> groups, which favors the sorption of V(V) ions. Based on desorption tests as well as the results of infrared and X-ray photoelectron spectroscopy, irrespective of the process conditions, the physical nature of the adsorbent/adsorbate interaction was confirmed. Adsorption ; Vanadium ; Sorption ; Langmuir adsorption model ; Chemistry ; Cubic zirconia ; Endothermic process ; Langmuir ; Catalysis ; Desorption ; Inorganic chemistry ; Chemical engineering ; Materials science ; Physical chemistry ; Organic chemistry ; Ceramic Radioactive element chemistry and processing ; Metal Extraction and Bioleaching ; Extraction and Separation Processes
Authors: Dounya Merah | Ulrich Maschke | Nouria Bouchikhi | Houcine Ziani Chérif | Lamia Bedjaoui
Source: Polymer Bulletin (article, 2022-11-07)
Field: Chemistry — Organic Chemistry

Characterization of swelling behavior and elastomer properties of acrylate polymers containing 2-ethylhexyl and isobornyl esters Swelling ; Polymer chemistry ; Copolymer ; Materials science ; Polymerization ; Solvent ; Glass transition ; Toluene ; Differential scanning calorimetry ; Hildebrand solubility parameter ; Polymer ; Chemistry ; Organic chemistry ; Composite material Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Polymer crystallization and properties
Authors: Abeer El Shahawy | Mahmoud F. Mubarak | Merna El Shafie | Hesham Abdulla
Source: RSC Advances (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Adsorption of Mn( <scp>ii</scp> ) ions from wastewater using an AgNPs/GO/chitosan nanocomposite material Water contaminated with heavy metal ions is extremely poisonous and threatens living organisms. Therefore, scientists place a premium on removing heavy metal ions from water that has already been contaminated. Removing metal ions from water typically involves the use of nanomaterials. Chitosan was made by extracting it from shrimp shells and combining it with a 3 : 1 ratio of synthetically produced AgNPs/GO. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) with transmission electron microscopy (TEM), and X-ray diffraction were used to investigate an AgNPs/GO/chitosan nanocomposite (XRD). A number of studies must be run to determine the optimal pH, adsorbent quantity, retention period, stirring speed, temperature, and initial concentration. The studies were conducted in a variety of ways. The isotherms of Langmuir, Freundlich, and Dubinin-Radushkevich were utilized. The industrial wastewater was used in the column adsorption experiment, and the flow rates and column bed heights were varied. An optimum contact time, pH, and adsorbent dosage for Mn(ii) were determined. At 30 minutes, pH 6, and 0.05 grams of Mn(ii) adsorbent per 100 ml, with agitation at 250 rpm, room temperature of 30 °C, and an initial concentration of 40 ppm, the best conditions were discovered. A positive correlation coefficient finding (<i>R</i> <sup>2</sup> = 0.925) indicates a good fit for Mn, according to equilibrium studies (II). The pseudo-second-order active model was connected to data that suited the pseudo-first and pseudo-second-order models. In the intra-particle diffusion model, the mechanism must proceed through four phases before equilibrium is reached. In an industrial adsorbent column, the adsorbent was put to the test. The periodicity test demonstrates that the nanocomposite's adsorption capability can be recovered by washing it with 0.1 M HCl. Mn(ii) adsorbed on AgNPs/GO/Chitosan after four cycles was only 20%, insufficient for additional adsorption tests. The repeated cycles that led to the partial loss of the adsorbate may have reduced the adsorbent material's efficacy. Chitosan ; Adsorption ; Metal ions in aqueous solution ; Nanocomposite ; Wastewater ; Chemistry ; Contaminated water ; Metal ; Ion ; Heavy metals ; Chemical engineering ; Sewage treatment ; Nuclear chemistry ; Environmental chemistry ; Materials science ; Nanotechnology ; Environmental science ; Environmental engineering ; Organic chemistry Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Advanced Nanomaterials in Catalysis
Authors: Maria João Enes da Silva | Rolf S. Postma | Leon Lefferts | Aayan Banerjee | Jimmy Faria
Source: Chemical Engineering Journal (article, 2022-12-06)
Field: Chemistry — Organic Chemistry

The onset of mass transport limitations triggers the stimulus responsiveness of polymer coated catalysts A series of Pd/Al2O3 catalysts coated with N-isopropylacrylamide polymer (p-NIPAM) brushes with increasing particle size of the support were prepared to study the interplay between mass transport limitations and the ability of the thermo-responsive catalysts to modify reactivity during reaction. Nitrite hydrogenation in water was chosen as probe reaction, which is a key step in the reduction of nitrites and nitrates from drinking water. The results show that the apparent activation energy decreases on increasing temperature above the LCST, i.e. the temperature where the polymer undergoes a phase change, for catalyst with particle sizes between 38 and 100 μm, alluding to a significant increase in mass transfer limitations. In sharp contrast, both p-NIPAM modified catalysts with smaller particle size and uncoated catalysts do not show this change in activation energy with temperature. Detailed multi-physics mass transport and reaction modelling indicated that the transport of the limiting reagent, hydrogen in this case, is severely reduced when the p-NIPAM collapses at temperatures above its LCST. It is concluded that effective reaction control using stimulus responsive polymers requires the system to be close to the mass transport limitation regime, to maximize the effect of the conformation change of the polymer on the catalyst performance and achieve sharp and reversible transitions from active to inactive. Lower critical solution temperature ; Catalysis ; Chemistry ; Polymer ; Reagent ; Mass transfer ; Mass transport ; Particle size ; Chemical engineering ; Activation energy ; Reaction rate ; Physical chemistry ; Organic chemistry ; Chromatography Nanomaterials for catalytic reactions ; Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques
Authors: My V. Nguyen | Hung Ngoc Nguyen | Tuyet A. Nguyen | Khang M. V. Nguyen
Source: RSC Advances (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

Engineering of appropriate pore size combined with sulfonic functionalization in a Zr-MOF with reo topology for the ultra-high removal of cationic malachite green dye from an aqueous medium A Zr-based metal–organic framework with reo topology, denoted as Reo-MOF-1, was fabricated through a solvothermal method capable of efficiently removing the cationic MG dye from an aqueous medium. Malachite green ; Cationic polymerization ; Surface modification ; Aqueous medium ; Materials science ; Chemical engineering ; Aqueous solution ; Polymer chemistry ; Chemistry ; Organic chemistry ; Adsorption ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Nanomaterials in Catalysis