jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Michael Cowan | Giannis Mpourmpakis
Source: Dalton Transactions (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Towards elucidating structure of ligand-protected nanoclusters Ligand-protected metal nanoclusters (NCs) are organic-inorganic nanostructures, exhibiting high stability at specific "magic size" compositions and tunable properties that make them promising candidates for a wide range of nanotechnology-based applications. Synthesis and characterization of these nanostructures has been achieved with atomic precision, offering great opportunities to study the origin of new physicochemical property emergence at the nanoscale using theory and computation. In this Frontier article, we highlight the recent advances in the field of ligand-protected metal NCs, focusing on stability theories on monometallic and heterometal doped NCs, and NC structure prediction. Furthermore, we discuss current challenges on predicting previously undiscovered NCs and propose future steps to advance the field through applying first principles calculations, machine learning, and data-science-based approaches. Nanoclusters ; Ligand (biochemistry) ; Chemistry ; Crystallography ; Nanotechnology ; Materials science ; Biochemistry ; Receptor Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Kasra Pirzadeh | Ali Asghar Ghoreyshi | Mostafa Rahimnejad | Maedeh Mohammadi
Source: Frontiers of Chemical Science and Engineering (article, 2020-01-16)
Field: Chemistry — Inorganic Chemistry

Optimization of electrochemically synthesized Cu3(BTC)2 by Taguchi method for CO2/N2 separation and data validation through artificial neural network modeling Adsorption ; Taguchi methods ; Electrochemistry ; Selectivity ; Materials science ; Metal-organic framework ; Sorption ; Langmuir adsorption model ; Electrode ; Chemical engineering ; Chemistry ; Physical chemistry ; Catalysis ; Organic chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Zhiwei Liu | Chuantao Zheng | Tianyu Zhang | Yù Zhang | Yiding Wang | Frank K. Tittel
Source: The Analyst (article, 2020-11-09)
Field: Chemistry — Spectroscopy

High-precision methane isotopic abundance analysis using near-infrared absorption spectroscopy at 100 Torr A near-infrared methane (CH<sub>4</sub>) sensor system for carbon isotopic abundance analysis was developed based on laser absorption spectroscopy (LAS). For good thermal stability, two CH<sub>4</sub> absorption lines with a similar low-state energy level were selected to realize relative weak temperature dependence. Wavelet denoising (WD) was employed for a pre-treatment of the direct absorption spectral (DAS) signal to perform a preliminary suppression of high-frequency noise. Due to the abnormal <sup>13</sup>CH<sub>4</sub> profile caused by superimposition of multiple lines, two statistical analysis algorithms including linear regression and neural network prediction were respectively employed on the retrieval of molecule fractions instead of the traditionally used standard absorption line fitting method. Performance assessment and a comparison between the two methods were carried out. Compared with the concentration deducing method based on the maximum absorbance in rough data, the linear regression and the neural network prediction obtained a sensitivity enhancement by ∼2 times and ∼10 times, respectively. A simultaneous measurement of pressure and concentration was performed using the neural network, which indicated a good potential of the technique for multi-parameter analysis using a single LAS-based sensor system. Methane ; Natural abundance ; Infrared spectroscopy ; Spectroscopy ; Analytical Chemistry (journal) ; Abundance (ecology) ; Absorption (acoustics) ; Absorption spectroscopy ; Infrared ; Chemistry ; Torr ; Near-infrared spectroscopy ; Mass spectrometry ; Materials science ; Optics ; Environmental chemistry ; Physics Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: Julien Lam | Saleh Abdul-Al | A. Allouche
Source: Journal of Chemical Theory and Computation (article, 2020-01-31)
Field: Materials Science — Materials Chemistry

Combining Quantum Mechanics and Machine-Learning Calculations for Anharmonic Corrections to Vibrational Frequencies Several methods are available to compute the anharmonicity in semirigid molecules. However, such methods are not yet routinely employed because of their high computational cost, especially for large molecules. The potential energy surface is required and generally approximated by a quartic force field potential based on ab initio calculation, thus limiting this approach to medium-sized molecules. We developed a new, fast, and accurate hybrid quantum mechanics/machine learning (QM/ML) approach to reduce the computational time for large systems. With this novel approach, we evaluated anharmonic frequencies of 37 molecules, thus covering a broad range of vibrational modes and chemical environments. The obtained fundamental frequencies reproduce results obtained using B2PLYP/def2tzvpp with a root-mean-square deviation (RMSD) of 21 cm<sup>-1</sup> and experimental results with a RMSD of 23 cm<sup>-1</sup>. Along with this very good accuracy, the computational time with our hybrid QM/ML approach scales linearly with <i>N</i>, while the traditional full ab initio method scales as <i>N</i><sup>2</sup>, where <i>N</i> is the number of atoms. Anharmonicity ; Quantum ; Physics ; Quantum mechanics ; Computer science ; Statistical physics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Nasser M. Al-Hajri | Abdullah Alghamdi | Zeeshan Tariq | Mohamed Mahmoud
Source: SPE Production & Operations (article, 2020-07-20)
Field: Materials Science — Biomaterials

Scale-Prediction/Inhibition Design Using Machine-Learning Techniques and Probabilistic Approach Summary This paper presents a data-driven methodology to predict calcium carbonate (CaCO3)-scale formation and design its inhibition program in petroleum wells. The proposed methodology integrates and adds to the existing principles of production surveillance, chemistry, machine learning (ML), and probability theory in a comprehensive decision workflow to achieve its purpose. The proposed model was applied on a large and representative field sample to verify its results. The method starts by collecting data such as ionic composition, pH, sample-collection/inspection dates, and scale-formation event. Then, collected data are classified or grouped according to production conditions. Calculation of chemical-scale indices is then made using techniques such as water-saturation level, Langelier saturation index (LSI), Ryznar saturation index (RSI), and Puckorius scaling index (PSI). The ML part of the method starts by dividing the data into training and test sets (80 and 20%, respectively). Classification models such as support-vector machine (SVM), K-nearest neighbors (KNN), gradient boosting, gradient-boosting classifier, and decision-tree classifier are all applied on collected data. Prediction results are then classified into a confusion matrix to be used as inputs for the probabilistic inhibition-design model. Finally, a functional-network (FN) tool is used to predict the formation of scale. The scale-inhibition program design uses a probabilistic model that quantifies the uncertainty associated with each ML method. The scale-prediction capability compared with actual inspection is presented into probability equations that are used in the cost model. The expected financial impact associated with applying any of the ML methods is obtained from defining costs for scale removal and scale inhibition. These costs are factored into the probability equations in a manner that presents incurred costs and saved or avoided expenses expected from field application of any given ML model. The forecasted cost model is built on a base-case method (i.e., current situation) to be used as a benchmark and foundation for the new scale-inhibition program. As will be presented in the paper, the results of applying the preceding techniques resulted in a scale-prediction accuracy of 95% and realized threefold cost-savings figures compared with existing programs. Support vector machine ; Probabilistic classification ; Decision tree ; Gradient boosting ; Data mining ; Machine learning ; Artificial intelligence ; Probabilistic logic ; Computer science ; Naive Bayes classifier ; Classifier (UML) ; Boosting (machine learning) ; Random forest Calcium Carbonate Crystallization and Inhibition ; Petroleum Processing and Analysis ; Fluid Dynamics and Mixing
Authors: Rinkle Juneja | Abhishek K. Singh
Source: Journal of Physics Materials (article, 2020-02-21)
Field: Materials Science — Materials Chemistry

Guided patchwork kriging to develop highly transferable thermal conductivity prediction models Abstract The machine learning models developed on a dataset comprising particular class of materials show poor transferability across different classes. The problem can be partially solved by increasing the variability in the dataset at the cost of prediction accuracy. To develop a model on a highly variable database, we propose a localized regression based patchwork kriging approach for capturing most of the complex details in the data. In this approach, the data is partitioned into smaller regions with shared patches of few datapoints across the neighboring boundaries. Local regression functions are developed in each partition with a constrain to give similar performance at the boundary. Out of 17 different properties tried for partitioning the data, the decomposition with respect to target output κ l gave local models with unprecedented accuracies. The partitioning with respect to κ l , however, requires its estimate for any unknown compound beforehand. To address this, we developed a global model for the entire database. The global model accurately predicts the order of magnitude of κ l for the compounds in the dataset and hence, directs them towards a particular partition for more accurate prediction. We define this stepwise approach as guided patchwork kriging, which can be applied to develop highly accurate transferable prediction models. Kriging ; Transferability ; Partition (number theory) ; Computer science ; Data mining ; Regression ; Variable (mathematics) ; Class (philosophy) ; Support vector machine ; Boundary (topology) ; Machine learning ; Artificial intelligence ; Mathematics ; Statistics Machine Learning in Materials Science ; Thermal properties of materials ; X-ray Diffraction in Crystallography
Authors: Yasamin Zamani | Ali Zareein | Leila Bazli | Reyhaneh NasrAzadani | Mahammod Babar Pasha | Shima Nasibi | Amir Modarresi Chahardehi
Source: Journal of Composites and Compounds (review, 2020-10-01)
Field: Materials Science — Materials Chemistry

Nanodiamond-containing composites for tissue scaﬀolds and surgical implants: A review Due to promising properties such as low toxicity against different cell lines, being highly stable fuorescent without showing photobleaching, and good surface properties, nanodiamonds have gained ever-increasing attention for various biomedical applications including bioimaging and therapeutic applications. Various methods are used for the fabrication of nanostructured diamond, the commonly used of which is the denotation technique. Newer approaches are being practiced for the modifcation and functionalization of their surfaces by different biomolecules suitable for interaction with considered targets. In this review, the scope and recent advancement in the feld of nanodiamonds for biomedical applications particularly their application for nanocomposite scaffold and implants are discussed. Nanodiamond ; Nanotechnology ; Materials science ; Surface modification ; Nanocomposite ; Diamond ; Biomedical engineering ; Composite material ; Engineering ; Mechanical engineering Diamond and Carbon-based Materials Research ; Lubricants and Their Additives ; Metal and Thin Film Mechanics
Authors: Yen‐Ju Wu | Tianzhuo Zhan | Zhufeng Hou | Lei Fang | Yibin Xu
Source: Scientific Data (article, 2020-02-03)
Field: Materials Science — Materials Chemistry

Physical and chemical descriptors for predicting interfacial thermal resistance Heat transfer at interfaces plays a critical role in material design and device performance. Higher interfacial thermal resistances (ITRs) affect the device efficiency and increase the energy consumption. Conversely, higher ITRs can enhance the figure of merit of thermoelectric materials by achieving ultra-low thermal conductivity via nanostructuring. This study proposes a dataset of descriptors for predicting the ITRs. The dataset includes two parts: one part consists of ITRs data collected from 87 experimental papers and the other part consists of the descriptors of 289 materials, which can construct over 80,000 pair-material systems for ITRs prediction. The former part is composed of over 1300 data points of metal/nonmetal, nonmetal/nonmetal, and metal/metal interfaces. The latter part consists of physical and chemical properties that are highly correlated to the ITRs. The synthesis method of the materials and the thermal measurement technique are also recorded in the dataset for further analyses. These datasets can be applied not only to ITRs predictions but also to thermal-property predictions or heat transfer on various material systems. Nonmetal ; Thermal conductivity ; Materials science ; Figure of merit ; Thermal ; Thermal resistance ; Heat transfer ; Thermal conduction ; Thermoelectric effect ; Computer science ; Metal ; Thermodynamics ; Optoelectronics ; Composite material ; Metallurgy ; Physics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Ngoc Lan | Ha T. | Nguyen-Hieu Hoang | Nguyễn Thị Vân Anh | Khanh‐Quang Tran | Evert Jan Meijer | Thuat T. Trinh
Source: The Journal of Physical Chemistry B (article, 2020-10-29)
Field: Chemistry — Inorganic Chemistry

Elucidating the Role of Tetraethylammonium in the Silicate Condensation Reaction from <i>Ab Initio</i> Molecular Dynamics Simulations The understanding of the formation of silicate oligomers in the initial stage of zeolite synthesis is important. The use of organic structure-directing agents (OSDAs) is known to be a key factor in the formation of different silicate species and the final zeolite structure. For example, tetraethylammonium ion (TEA<sup>+</sup>) is a commonly used organic template for zeolite synthesis. In this study, <i>ab initio</i> molecular dynamics (AIMD) simulation is used to provide an understanding of the role of TEA<sup>+</sup> in the formation of various silicate oligomers, ranging from dimer to 4-ring. Calculated free-energy profiles of the reaction pathways show that the formation of a 4-ring structure has the highest energy barrier (97 kJ/mol). The formation of smaller oligomers such as dimer, trimer, and 3-ring has lower activation barriers. The TEA<sup>+</sup> ion plays an important role in regulating the predominant species in solution via its coordination with silicate structures during the condensation process. The kinetics and thermodynamics of the oligomerization reaction indicate a more favorable formation of the 3-ring over the 4-ring structure. The results from AIMD simulations are in line with the experimental observation that TEA<sup>+</sup> favors the 3-ring and double 3-ring in solution. The results of this study imply that the role of OSDAs is not only important for the host-guest interaction but also crucial for controlling the reactivity of different silicate oligomers during the initial stage of zeolite formation. Trimer ; Silicate ; Dimer ; Chemistry ; Ring (chemistry) ; Zeolite ; Molecular dynamics ; Ab initio ; Reactivity (psychology) ; Tetraethylammonium ; Computational chemistry ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis and Oxidation Reactions
Authors: Daniela M. Correia | Erlantz Lizundia | R.M. Meira | Mikel Rincón‐Iglesias | S. Lanceros‐Méndez
Source: Materials (article, 2020-05-15)
Field: Materials Science — Biomaterials

Cellulose Nanocrystal and Water-Soluble Cellulose Derivative Based Electromechanical Bending Actuators This study reports a versatile method for the development of cellulose nanocrystals (CNCs) and water-soluble cellulose derivatives (methyl cellulose (MC), hydroxypropyl cellulose (HPC), and sodium carboxymethyl cellulose (NaCMC)) films comprising the ionic liquid (IL) 2-hydroxy-ethyl-trimethylammonium dihydrogen phosphate ([Ch][DHP]) for actuator fabrication. The influence of the IL content on the morphology and physico-chemical properties of free-standing composite films was evaluated. Independently of the cellulose derivative, the ductility of the films increases upon [Ch][DHP] incorporation to yield elongation at break values of nearly 15%. An increase on the electrical conductivity as a result of the IL incorporation into cellulosic matrices is found. The actuator performance of composites was evaluated, NaCMC/[Ch][DHP] showing the maximum displacement along the x-axis of 9 mm at 8 Vpp. Based on the obtained high electromechanical actuation performance, together with their simple processability and renewable nature, the materials fabricated here represent a step forward in the development of sustainable soft actuators of high practical relevance. Cellulose ; Materials science ; Ionic liquid ; Chemical engineering ; Carboxymethyl cellulose ; Composite material ; Solvent ; Fabrication ; Polymer chemistry ; Nanocrystal ; Organic chemistry ; Sodium ; Nanotechnology ; Chemistry Advanced Cellulose Research Studies ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Theodor S. Holstad | Trygve Magnus Ræder | Donald M. Evans | Didrik R. Småbråten | S. Krohns | Jakob Schaab | Z. Yan | Edith Bourret | Antonius T. J. van Helvoort | Tor Grande | Sverre M. Selbach | Joshua Agar | Dennis Meier
Source: npj Computational Materials (article, 2020-10-28)
Field: Materials Science — Materials Chemistry

Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls Abstract Ferroelectric domain walls are promising quasi-2D structures that can be leveraged for miniaturization of electronics components and new mechanisms to control electronic signals at the nanoscale. Despite the significant progress in experiment and theory, however, most investigations on ferroelectric domain walls are still on a fundamental level, and reliable characterization of emergent transport phenomena remains a challenging task. Here, we apply a neural-network-based approach to regularize local I ( V )-spectroscopy measurements and improve the information extraction, using data recorded at charged domain walls in hexagonal (Er 0.99 ,Zr 0.01 )MnO 3 as an instructive example. Using a sparse long short-term memory autoencoder, we disentangle competing conductivity signals both spatially and as a function of voltage, facilitating a less biased, unconstrained and more accurate analysis compared to a standard evaluation of conductance maps. The neural-network-based analysis allows us to isolate extrinsic signals that relate to the tip-sample contact and separating them from the intrinsic transport behavior associated with the ferroelectric domain walls in (Er 0.99 ,Zr 0.01 )MnO 3 . Our work expands machine-learning-assisted scanning probe microscopy studies into the realm of local conductance measurements, improving the extraction of physical conduction mechanisms and separation of interfering current signals. Ferroelectricity ; Materials science ; Artificial neural network ; Thermal conduction ; Conductance ; Autoencoder ; Computer science ; Biological system ; Nanotechnology ; Condensed matter physics ; Artificial intelligence ; Optoelectronics ; Physics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Electronic and Structural Properties of Oxides
Authors: F. Mohammadi | Marzieh Moeeni | Chengnan Li | Rabah Boukherroub | Sabine Szunerits
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Biomaterials

Interaction of cellulose and nitrodopamine coated superparamagnetic iron oxide nanoparticles with alpha-lactalbumin The adsorption of BLA on the SPIOns, and their non-toxic nature of the bioconjugate make these nanoparticles new model nanostructures for nanomedicine orientated applications. Nanomedicine ; Superparamagnetism ; Nanoparticle ; Adsorption ; Chemistry ; Cellulose ; Chemical engineering ; Bioconjugation ; Nanotechnology ; Materials science ; Organic chemistry ; Physics Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Advanced Drug Delivery Systems
Authors: Ruth Andrew | Natalie Homer
Source: Current Opinion in Endocrine and Metabolic Research (article, 2020-11-18)
Field: Chemistry — Spectroscopy

Mass spectrometry: Future opportunities for profiling and imaging steroids and steroid metabolites Profiling (computer programming) ; Mass spectrometry ; Chromatography ; Chemistry ; Steroid ; Supercritical fluid ; Ion-mobility spectrometry ; Molecularly imprinted polymer ; Supercritical fluid chromatography ; Computational biology ; Hormone ; Nanotechnology ; Gas chromatography ; Computer science ; Materials science ; Biology ; Biochemistry ; Organic chemistry Mass Spectrometry Techniques and Applications ; Hormonal and reproductive studies ; Analytical Chemistry and Chromatography
Authors: M. Torres Arango | Y. Zhang | Chonghang Zhao | R. Li | Gregory S. Doerk | Dmytro Nykypanchuk | Y-c. K. Chen-Wiegart | Andrei Fluerasu | Lutz Wiegart
Source: Materials Today Physics (article, 2020-04-16)
Field: Materials Science — Materials Chemistry

Ink-substrate interactions during 3D printing revealed by time-resolved coherent X-ray scattering Materials science ; Nanotechnology ; Nanoscopic scale ; Inkwell ; Amorphous solid ; Substrate (aquarium) ; Scattering ; 3D printing ; Characterization (materials science) ; Composite material ; Optics Material Dynamics and Properties ; Nonlinear Optical Materials Studies ; Cultural Heritage Materials Analysis