jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Roya Sadat Neisan | Noori M. Cata Saady | Carlos Bazán | Sohrab Zendehboudi | Talib M. Albayati
Source: Heliyon (article, 2023-10-29)
Field: Materials Science — Materials Chemistry

Adsorption of copper from water using TiO2-modified activated carbon derived from orange peels and date seeds: Response surface methodology optimization This study evaluated the application and efficiency of modified activated carbon in the removal of copper (Cu) from synthetic aquatic samples. The surface of activated carbon derived from orange peel (AC-OP) and date seeds (AC-DS) have been modified by Titanium dioxide nanoparticles (TiO<sub>2</sub> NPs) (1:10 wt% mixing ratio) and used in a series of experiments designed by Response Surface Methodology (RSM) incorporating Central Composite Design (CCD). The Brunauer-Emmett-Teller (BET) test demonstrated that the modification has increased the surface area of AC-OP from 2.40 to 6.06 m<sup>2</sup> g<sup>-1</sup> and AC-DS from 51.10 to 81.37 m<sup>2</sup> g<sup>-1</sup>. Effects of pH (1-7), ion initial concentration (10-60 mg L<sup>-1</sup>), adsorbent dose (0.5-8 g L<sup>-1</sup>), and contact time (0.4-6 h) have been investigated. The results showed that the optimum conditions for TiO<sub>2</sub>-modified AC-OP (OP-TiO<sub>2</sub>) are pH 5, initial concentration of 24.6 mg L<sup>-1</sup>, adsorbent dose of 4.9 g L<sup>-1</sup><sup>,</sup> and contact time of 3.6 h. The optimum conditions for TiO<sub>2</sub>-modified AC-DS (DS-TiO<sub>2</sub>) are pH 6.4, initial concentration of 21.2 mg L<sup>-1</sup>, adsorbent dose of 5 g L<sup>-1</sup><sup>,</sup> and contact time of 3.0 h. The modified quadratic models represented the results well with regression coefficients of 0.91 and 0.99 for OP-TiO<sub>2</sub> and DS-TiO<sub>2</sub>, respectively. The maximum Cu removal for OP-TiO<sub>2</sub> and DS-TiO<sub>2</sub> were 99.90 % and 97.40 %, and the maximum adsorption capacity was found to be 13.34 and 13.96 mg g<sup>-1</sup>, respectively. Kinetic data have been fitted to pseudo first-order, pseudo second-order, intra-particle diffusion, and Elovich models. The pseudo second-order showed a better fit to the experimental data (R<sup>2</sup> > 98 %). This study demonstrates the successful development of modified activated carbon derived from orange peels and date seeds, modified by TiO<sub>2</sub> nanoparticles, for efficient adsorption of copper ions from water. The findings contribute to understanding the adsorption mechanism and provide valuable insights for designing environmentally friendly adsorbents. Response surface methodology ; Adsorption ; Activated carbon ; Orange (colour) ; Central composite design ; Copper ; Nuclear chemistry ; Titanium dioxide ; Chemistry ; Titanium ; Surface modification ; Contact angle ; Materials science ; Analytical Chemistry (journal) ; Chromatography ; Metallurgy ; Composite material ; Organic chemistry Nanoparticles: synthesis and applications ; Adsorption and biosorption for pollutant removal
Authors: Conor Cleeton | Felipe Lopes Oliveira | Rodrigo Neumann Barros Ferreira | Amir H. Farmahini | Binquan Luan | M. Steiner | Lev Sarkisov
Source: Energy & Environmental Science (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

A process-level perspective of the impact of molecular force fields on the computational screening of MOFs for carbon capture The question we pose in this study is to what extent the ranking of metal organic frameworks (MOFs) for pressure swing adsorption-based carbon capture depends on the choice of the commonly available forcefields. Ranking (information retrieval) ; Metal-organic framework ; Adsorption ; Perspective (graphical) ; Process (computing) ; Carbon fibers ; Swing ; Pressure swing adsorption ; Computer science ; Nanotechnology ; Process engineering ; Materials science ; Chemistry ; Engineering ; Artificial intelligence ; Mechanical engineering ; Organic chemistry ; Algorithm Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Hao Zhou | Shuxiang Zhou | Zilong Hua | Kaustubh Bawane | Tianli Feng
Source: Applied Physics Letters (article, 2023-11-06)
Field: Materials Science — Materials Chemistry

Extreme sensitivity of higher-order interatomic force constants and thermal conductivity to the energy surface roughness of exchange-correlation functionals In this Letter, we report that the fourth-order interatomic force constants (4th-IFCs) are significantly sensitive to the energy surface roughness of exchange-correlation (XC) functionals in density functional theory calculations. This sensitivity, which is insignificant for the second- (2nd-) and third-order (3rd-) IFCs, varies for different functionals in different materials and can cause misprediction of thermal conductivity by several times of magnitude. As a result, when calculating the 4th-IFCs using the finite difference method, the atomic displacement needs to be taken large enough to overcome the energy surface roughness, in order to accurately predict phonon lifetime and thermal conductivity. We demonstrate this phenomenon on a benchmark material (Si), a high-thermal conductivity material (BAs), and a low thermal conductivity material (NaCl). For Si, we find that the LDA, PBE, and PBEsol XC functionals are all smooth to the 2nd- and 3rd-IFCs but all rough to the 4th-IFCs. This roughness can lead to a prediction of nearly one order of magnitude lower thermal conductivity. For BAs, all three functionals are smooth to the 2nd- and 3rd-IFCs, and only the PBEsol XC functional is rough for the 4th-IFCs, which leads to a 40% underestimation of thermal conductivity. For NaCl, all functionals are smooth to the 2nd- and 3rd-IFCs but rough to the 4th-IFCs, leading to a 70% underprediction of thermal conductivity at room temperature. With these observations, we provide general guidance on the calculation of 4th-IFCs for an accurate thermal conductivity prediction. Thermal conductivity ; Materials science ; Conductivity ; Surface roughness ; Surface finish ; Interatomic potential ; Condensed matter physics ; Thermodynamics ; Chemistry ; Physics ; Molecular dynamics ; Composite material ; Computational chemistry ; Physical chemistry Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Brendan McBennett | Albert Beardo | Emma E. Nelson | Begoña Abad Mayor | Travis D. Frazer | Amitava Adak | Yuka Esashi | Baowen Li | Henry C. Kapteyn | Margaret M. Murnane | Joshua L. Knobloch
Source: Nano Letters (article, 2023-03-07)
Field: Materials Science — Materials Chemistry

Universal Behavior of Highly Confined Heat Flow in Semiconductor Nanosystems: From Nanomeshes to Metalattices Nanostructuring on length scales corresponding to phonon mean free paths provides control over heat flow in semiconductors and makes it possible to engineer their thermal properties. However, the influence of boundaries limits the validity of bulk models, while first-principles calculations are too computationally expensive to model real devices. Here we use extreme ultraviolet beams to study phonon transport dynamics in a 3D nanostructured silicon <i>metalattice</i> with deep nanoscale feature size and observe dramatically reduced thermal conductivity relative to bulk. To explain this behavior, we develop a predictive theory wherein thermal conduction separates into a geometric <i>permeability</i> component and an intrinsic <i>viscous</i> contribution, arising from a new and universal effect of nanoscale confinement on phonon flow. Using experiments and atomistic simulations, we show that our theory applies to a general set of highly confined silicon nanosystems, from metalattices, nanomeshes, porous nanowires, to nanowire networks, of great interest for next-generation energy-efficient devices. Phonon ; Nanowire ; Thermal conduction ; Materials science ; Thermal conductivity ; Silicon ; Semiconductor ; Nanoscopic scale ; Nanotechnology ; Thermal ; Porous medium ; Condensed matter physics ; Porosity ; Optoelectronics ; Physics ; Thermodynamics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Advanced Thermoelectric Materials and Devices
Authors: Letícia de Oliveira Carneiro | Paulo Carteri Coradi | Dágila Melo Rodrigues | Roney Eloy Lima | Larissa Pereira Ribeiro Teodoro | Rosana Santos de Moraes | Paulo Eduardo Teodoro | Marcela Trojahn Nunes | Marisa Menezes Leal | Lhais Rodrigues Lopes | Tiago Arabites Vendrusculo | Jean Carlos Robattini | Anderson Henrique Soares | Nairiane dos Santos Bilhalva
Source: AgriEngineering (article, 2023-07-04)
Field: Chemistry — Analytical Chemistry

Characterizing and Predicting the Quality of Milled Rice Grains Using Machine Learning Models Physical classification is the procedure adopted by the rice unloading, delivery, storage, and processing units for the commercial characterization of the quality of the grains. This step occurs mostly by the conventional method, which demands more time and specialized labor, and the results are subjective since the evaluation is visual. In order to make the operation faster, more accurate, and less dependent, non-destructive technologies and computational intelligence can be applied to characterize grain quality. Therefore, this study aimed to characterize and predict the quality of whole, processed rice grains, as well as classify any defects present. This was achieved by sampling from the upper and lower points of four silo dryers with capacities of up to 40,000 sacks. The grain samples had moisture contents of 16%, 17%, 18%, and 19% and were subjected to drying-aeration until reaching 12% moisture content (w.b.). Near-infrared spectroscopy technology and Machine Learning algorithm models (Artificial Neural Networks, decision tree algorithms Quinlan’s algorithm, Random Tree, REPTree, and Random Forest) were employed for this purpose. By analyzing Pearson’s correlation statistics, a strong negative correlation (R2 = 0.98) was found between moisture content and the yield of whole grains. Conversely, a strong positive correlation (R2 = 0.97) was observed between moisture content and classified physical defects across the various characterized physicochemical constituents. These findings indicate the effectiveness of near-infrared spectroscopy technology. The Random Tree model (RandT) successfully predicted the grain quality outcomes and is therefore recommended as the model of choice, obtained Pearson’s correlation coefficient (r = 0.96), mean absolute error (MAE = 0.017), and coefficient of determination (R2 = 0.92). The results obtained here reveal that the combination of near-infrared spectroscopy technology and Machine Learning algorithm models is an excellent non-destructive alternative to manual physical classification for characterizing the physicochemical quality of whole and defective rice grains. Pearson product-moment correlation coefficient ; Random forest ; Machine learning ; Silo ; Water content ; Correlation coefficient ; Artificial intelligence ; Decision tree ; Quality (philosophy) ; Computer science ; Sampling (signal processing) ; Agricultural engineering ; Mathematics ; Algorithm ; Statistics ; Engineering ; Mechanical engineering ; Computer vision ; Physics ; Geotechnical engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; GABA and Rice Research
Authors: Myriam M. Chaumeil | James A. Bankson | Kevin M. Brindle | Shdema Epstein | Ferdia A. Gallagher | Martin Grashei | Caroline Guglielmetti | Joshua Kaggie | Kayvan R. Keshari | Stephan Knecht | Christoffer Laustsen | Andreas B. Schmidt | Daniel B. Vigneron | Yi‐Fen Yen | Franz Schilling
Source: Molecular Imaging and Biology (article, 2023-12-26)
Field: Chemistry — Spectroscopy

New Horizons in Hyperpolarized 13C MRI Hyperpolarization techniques significantly enhance the sensitivity of magnetic resonance (MR) and thus present fascinating new directions for research and applications with in vivo MR imaging and spectroscopy (MRI/S). Hyperpolarized <sup>13</sup>C MRI/S, in particular, enables real-time non-invasive assessment of metabolic processes and holds great promise for a diverse range of clinical applications spanning fields like oncology, neurology, and cardiology, with a potential for improving early diagnosis of disease, patient stratification, and therapy response assessment. Despite its potential, technical challenges remain for achieving clinical translation. This paper provides an overview of the discussions that took place at the international workshop "New Horizons in Hyperpolarized <sup>13</sup>C MRI," in March 2023 at the Bavarian Academy of Sciences and Humanities, Munich, Germany. The workshop covered new developments, as well as future directions, in topics including polarization techniques (particularly focusing on parahydrogen-based methods), novel probes, considerations related to data acquisition and analysis, and emerging clinical applications in oncology and other fields. Nuclear magnetic resonance ; New horizons ; Magnetic resonance imaging ; Medicine ; Radiology ; Physics Advanced NMR Techniques and Applications ; Atomic and Subatomic Physics Research ; Solid-state spectroscopy and crystallography
Authors: Preethi | M. Gunasekaran | J. Rajesh Banu
Source: Frontiers in Energy Research (article, 2023-01-13)
Field: Materials Science — Biomaterials

Indexing energy and cost of the pretreatment for economically efficient bioenergy generation The growing necessity for energy worldwide has led to the hunt for an interminable solution in the form of sustainable energy generation. To accomplish sustainability, these problems can be resolved using renewable waste biomass, which is readily accessible and low priced. Moreover, the ecological issue due to the disposal of this waste biomass into the environment is also counteracted by the use of this biomass for energy generation along with the substantial solid reduction for disposal. The presence of complex biopolymers in biomass, which hasten the hydrolysis step during energy generation, was enhanced by the application of a pretreatment method. The efficiency of the pretreatment methods was enhanced by maintaining the cost and energy usage since the commercialization of this method is largely limited. The major economic drivers are based on solid concentration and, thus, lead to higher capital costs. This study reveals the wide assortment of current progression in pretreatment techniques for treating waste biomass with special focus on combined and phase-separated pretreatment. Additionally, it converses the advantages and limitations of pretreatment methods. This pivotal investigation brings about the cost- and energy-effective conversion solution that paves the way for a sustainable energy system. Biomass (ecology) ; Bioenergy ; Renewable energy ; Capital cost ; Commercialization ; Sustainability ; Environmental science ; Efficient energy use ; Flexibility (engineering) ; Waste management ; Biochemical engineering ; Biofuel ; Environmental economics ; Business ; Engineering ; Ecology ; Economics biodegradable polymer synthesis and properties ; Anaerobic Digestion and Biogas Production ; Extraction and Separation Processes
Authors: Sara Mostafapour | Thomas Dörfer | Ralf Heinke | Petra Rösch | Jürgen Popp | Thomas Bocklitz
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2023-07-01)
Field: Chemistry — Analytical Chemistry

Investigating the effect of different pre-treatment methods on Raman spectra recorded with different excitation wavelengths Raman reference libraries can be used for identification of components in unknown samples as Raman spectroscopy offers fingerprint information of the measured samples. Since Raman libraries often contain many different and/or highly similar spectra, it is important that the spectra are a reliable fingerprint for each compound. However, Raman spectra are highly sensitive to the experimental conditions, and the Raman spectra will change in different conditions even though the same sample is measured. Raman data pre-treatment minimizes the differences between Raman spectra arising from different experimental conditions. In this study, different combinations of pre-treatment methods are used to quantify the effect of each pre-treatment step in minimizing the differences between Raman spectra of the same sample in different experimental conditions, e.g., different excitation wavelengths. These different pre-treatment processes are evaluated for six solvents. The spectra differences between spectra recorded with three excitation wavelengths (532 nm, 633 nm, and 830 nm) are evaluated by angular difference index and the influence on a classification model is tested. The angular difference index of each spectrum after every data pre-treatment step shows a decreasing behavior. It could be demonstrated that wavenumber calibration has the largest effect on the differences between the Raman spectra. However, ω<sup>4</sup> correction doesn't have a significate effect in this dataset. The classification results show that the prediction accuracy is improving by doing data pre-treatment. In the dataset obtained in 633 nm a lower amount of pre-treatment steps is needed but in the dataset 830 nm more pre-treatment steps are needed for a high accuracy. The result shows that the choice of an optimal pre-treatment method or combination of methods strongly influences the analysis results, but is far from straightforward, since it depends on the characteristics of the data set and the goal of data analysis. Raman spectroscopy ; Wavelength ; Spectral line ; Fingerprint (computing) ; Excitation ; Calibration ; Excitation wavelength ; Analytical Chemistry (journal) ; Wavenumber ; Coherent anti-Stokes Raman spectroscopy ; Materials science ; Chemistry ; Raman scattering ; Optics ; Statistics ; Chromatography ; Mathematics ; Artificial intelligence ; Computer science ; Physics ; Optoelectronics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Yue Sun | Xiaodong Zhai | Xiaobo Zou | Jiyong Shi | Xiaowei Huang | Zhihua Li
Source: Foods (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

A Ratiometric Fluorescent Sensor Based on Silicon Quantum Dots and Silver Nanoclusters for Beef Freshness Monitoring A ratiometric fluorescent sensor with hydrogen sulfide (H<sub>2</sub>S) and methanthiol (CH<sub>3</sub>SH) sensitivity was developed to real-time monitor beef freshness. A silicon quantum dots (SiQD) and silver nanoclusters (AgNC) complex, namely SiQD-AgNC, was used as the dual emission fluorescence materials. Due to the fluorescence resonance energy transfer (FRET) effect between SiQD and AgNC, when the fluorescence of AgNC (610 nm) was quenched by H<sub>2</sub>S or CH<sub>3</sub>SH, the fluorescence of SiQD (468 nm) recovered, resulting in an increase of the fluorescent intensity ratio (<i>I</i><sub>468</sub>/<i>I</i><sub>610</sub>). <i>I</i><sub>468</sub>/<i>I</i><sub>610</sub> showed a linear relationship with the H<sub>2</sub>S concentration within the concentration range of 1.125-17 μM, with a limit of detection (LOD) value of 53.6 nM. Meanwhile, <i>I</i><sub>468</sub>/<i>I</i><sub>610</sub> presented two linear relationships with the CH<sub>3</sub>SH concentration within the concentration range of 1.125-17 μM and 23.375-38.25 μM, respectively, with a LOD value of 56.5 nM. The SiQD-AgNC complex was coated on a polyvinylidene fluoride (PVDF) film to form a portable SiQD-AgNC/PVDF film sensor. This film showed purplish red-to-cyan color changes in response to H<sub>2</sub>S and CH<sub>3</sub>SH, with LOD values of 224 nM and 233 nM to H<sub>2</sub>S and CH<sub>3</sub>SH, respectively. When the film was used to monitor beef freshness at 4 °C, its fluorescent color gradually changed from purplish red to cyan. Hence, this study presented a new ratiometric fluorescent sensor for intelligent food packaging. Fluorescence ; Cyan ; Nanoclusters ; Detection limit ; Materials science ; Quantum dot ; Analytical Chemistry (journal) ; Förster resonance energy transfer ; Silicon ; Optoelectronics ; Chemistry ; Nanotechnology ; Optics ; Chromatography Carbon and Quantum Dots Applications ; Mercury impact and mitigation studies ; Nanocluster Synthesis and Applications
Authors: Sitan Ye | Haiyong Weng | Lirong Xiang | Liangquan Jia | Jinchai Xu
Source: Molecules (article, 2023-07-13)
Field: Chemistry — Analytical Chemistry

Synchronously Predicting Tea Polyphenol and Epigallocatechin Gallate in Tea Leaves Using Fourier Transform–Near-Infrared Spectroscopy and Machine Learning Tea polyphenol and epigallocatechin gallate (EGCG) were considered as key components of tea. The rapid prediction of these two components can be beneficial for tea quality control and product development for tea producers, breeders and consumers. This study aimed to develop reliable models for tea polyphenols and EGCG content prediction during the breeding process using Fourier Transform-near infrared (FT-NIR) spectroscopy combined with machine learning algorithms. Various spectral preprocessing methods including Savitzky-Golay smoothing (SG), standard normal variate (SNV), vector normalization (VN), multiplicative scatter correction (MSC) and first derivative (FD) were applied to improve the quality of the collected spectra. Partial least squares regression (PLSR) and least squares support vector regression (LS-SVR) were introduced to establish models for tea polyphenol and EGCG content prediction based on different preprocessed spectral data. Variable selection algorithms, including competitive adaptive reweighted sampling (CARS) and random forest (RF), were further utilized to identify key spectral bands to improve the efficiency of the models. The results demonstrate that the optimal model for tea polyphenols calibration was the LS-SVR with R<sub>p</sub> = 0.975 and RPD = 4.540 based on SG-smoothed full spectra. For EGCG detection, the best model was the LS-SVR with R<sub>p</sub> = 0.936 and RPD = 2.841 using full original spectra as model inputs. The application of variable selection algorithms further improved the predictive performance of the models. The LS-SVR model for tea polyphenols prediction with R<sub>p</sub> = 0.978 and RPD = 4.833 used 30 CARS-selected variables, while the LS-SVR model build on 27 RF-selected variables achieved the best predictive ability with R<sub>p</sub> = 0.944 and RPD = 3.049, respectively, for EGCG prediction. The results demonstrate a potential of FT-NIR spectroscopy combined with machine learning for the rapid screening of genotypes with high tea polyphenol and EGCG content in tea leaves. Partial least squares regression ; Polyphenol ; Random forest ; Artificial intelligence ; Support vector machine ; Epigallocatechin gallate ; Mathematics ; Feature selection ; Smoothing ; Biological system ; Computer science ; Chemistry ; Algorithm ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Advanced Chemical Sensor Technologies