jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Charles Lloyd Yeboah Amuah | Ernest Teye | Francis Padi Lamptey | Kwasi Nyandey | Jerry Opoku-Ansah | Peter Osei‐Wusu Adueming
Source: Journal of Spectroscopy (article, 2019-08-06)
Field: Chemistry — Analytical Chemistry

Feasibility Study of the Use of Handheld NIR Spectrometer for Simultaneous Authentication and Quantification of Quality Parameters in Intact Pineapple Fruits The potential of predicting maturity using total soluble solids (TSS) and identifying organic from inorganic pineapple fruits based on near-infrared (NIR) spectra fingerprints would be beneficial to farmers and consumers alike. In this study, a portable NIR spectrometer and chemometric techniques were combined to simultaneously identify organically produced pineapple fruits from conventionally produced ones (thus organic and inorganic) and also predict total soluble solids. A total of 90 intact pineapple fruits were scanned with the NIR spectrometer while a digital refractometer was used to measure TSS from extracted pineapple juice. After attempting several preprocessing techniques, multivariate calibration models were built using principal component analysis (PCA), K -nearest neighbor (KNN), and linear discriminant analysis (LDA) to identify the classes (organic and conventional pineapple fruits) while partial least squares regression (PLSR) method was used to determine TSS of the fruits. Among the identification techniques, the MSC-PCA-LDA model accurately identified organic from conventionally produced fruits at 100% identification rate. For quantification of TSS, the MSC-PLSR model gave Rp = 0.851 and RMSEC = 0.950 °Brix, and Rc = 0.854 and RMSEP = 0.842 °Brix at 5 principal components in the calibration set and prediction set, respectively. The results generally indicated that portable NIR spectrometer coupled with the appropriate chemometric tools could be employed for rapid nondestructive examination of pineapple quality and also to detect pineapple fraud due to mislabeling of conventionally produced fruits as organic ones. This would be helpful to farmers, consumers, and quality control officers. Partial least squares regression ; Linear discriminant analysis ; Principal component analysis ; Spectrometer ; Chemometrics ; Near-infrared spectroscopy ; Calibration ; Brix ; Mathematics ; Chemistry ; Food science ; Chromatography ; Sugar ; Statistics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Water Quality Monitoring and Analysis
Authors: Lygeri Papaoikonomou | Konstantinos Labanaris | Kyriakos Kaderides | Athanasia M. Goula
Source: Environmental Science and Pollution Research (article, 2019-12-21)
Field: Chemistry — Organic Chemistry

Adsorption–desorption of phenolic compounds from olive mill wastewater using a novel low-cost biosorbent Adsorption ; Sorbent ; Chemistry ; Freundlich equation ; Langmuir ; Desorption ; Wastewater ; Sorption ; Langmuir adsorption model ; Phenols ; Particle size ; Chromatography ; Nuclear chemistry ; Organic chemistry ; Environmental engineering ; Environmental science Edible Oils Quality and Analysis ; Saffron Plant Research Studies ; Dye analysis and toxicity
Authors: Irene Fernández‐Barahona | María Muñoz-Hernando | Fernando Herranz
Source: Molecules (review, 2019-03-28)
Field: Materials Science — Biomaterials

Microwave-Driven Synthesis of Iron-Oxide Nanoparticles for Molecular Imaging Here, we present a comprehensive review on the use of microwave chemistry for the synthesis of iron-oxide nanoparticles focused on molecular imaging. We provide a brief introduction on molecular imaging, the applications of iron oxide in biomedicine, and traditional methods for the synthesis of these nanoparticles. The review then focuses on the different examples published where the use of microwaves is key for the production of nanoparticles. We study how the different parameters modulate nanoparticle properties, particularly for imaging applications. Finally, we explore principal applications in imaging of microwave-produced iron-oxide nanoparticles. Nanoparticle ; Nanotechnology ; Iron oxide nanoparticles ; Molecular imaging ; Microwave ; Materials science ; Iron oxide ; Oxide ; Microwave imaging ; Computer science Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Iron oxide chemistry and applications
Authors: Simon Johansson | Amol Thakkar | Thierry Kogej | Esben Jannik Bjerrum | Samuel Genheden | Tomas Bastys | Christos Kannas | Alexander Schliep | Hongming Chen | Ola Engkvist
Source: Drug Discovery Today Technologies (review, 2019-12-01)
Field: Materials Science — Materials Chemistry

AI-assisted synthesis prediction Computer science ; Throughput ; Artificial intelligence ; Robotics ; Machine learning ; Biochemical engineering ; Engineering ; Robot ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Changliang Liu | Zijian Zhao | Houqian Gao | Iman Rostami | Qing You | Xinru Jia | Chen Wang | Ling Zhu | Yanlian Yang
Source: Nanotheranostics (article, 2019-01-01)
Field: Materials Science — Polymers and Plastics

Enhanced blood-brain-barrier penetrability and tumor-targeting efficiency by peptide-functionalized poly(amidoamine) dendrimer for the therapy of gliomas Glioblastoma is one of the most common primary tumor types of central nervous system (CNS) with high malignance and lethality. Although many treatment options are currently available, the therapy of brain cancers remains challenging because of blood-brain-barrier (BBB) which prevents most of the chemotherapeutics into the CNS. In this work, a poly(amidoamine) dendrimer-based carrier was fabricated and modified with angiopep-2 (Ang2) peptide that has been demonstrated to bind to low density lipoprotein receptor-relative protein-1 (LRP1) on the endothelial cells of BBB and could therefore induce BBB penetration of the carrier. To improve tumor-targeting effect towards the glioma sites, the dendrimer was simultaneously functionalized with an epidermal growth factor receptor (EGFR)-targeting peptide (EP-1) which was screened from a "one-bead one-compound" (OBOC) combinatorial library. EP-1 peptide was demonstrated to have high affinity and specificity to EGFR at both the molecular and cellular levels. The dual-targeting dendrimer exhibited outstanding BBB penetrability and glioma targeting efficiency both <i>in vitro</i> and <i>in vivo</i>, which strikingly enhanced the anti-gliomas effect of the drugs and prolonged the survival of gliomas-bearing mice. These results show the potential of the dual-targeting dendrimer-based carrier in the therapy of gliomas through enhancing BBB penetrability and tumor targeting. Dendrimer ; Blood–brain barrier ; Glioma ; Cancer research ; Poly(amidoamine) ; LRP1 ; Chemistry ; In vivo ; Peptide ; Amidoamine ; Brain tumor ; Epidermal growth factor receptor ; Receptor ; Medicine ; Biology ; Central nervous system ; Biochemistry ; Pathology ; LDL receptor ; Lipoprotein ; Internal medicine Dendrimers and Hyperbranched Polymers ; RNA Interference and Gene Delivery ; Nanoparticle-Based Drug Delivery
Authors: Charlette M. Parnell | Bijay P. Chhetri | T. B. Mitchell | Fumiya Watanabe | Ganesh K. Kannarpady | Ambar B. RanguMagar | Huajun Zhou | Karrer M. Alghazali | Alexandru S. Biris | Anindya Ghosh
Source: Scientific Reports (article, 2019-04-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Simultaneous Electrochemical Deposition of Cobalt Complex and Poly(pyrrole) Thin Films for Supercapacitor Electrodes Supercapacitors are beneficial as energy storage devices and can obtain high capacitance values greater than conventional capacitors and high power densities compared to batteries. However, in order to improve upon the overall cost, energy density, and charge-discharge rates, the electrode material of supercapacitors needs to be fine-tuned with an inexpensive, high conducting source. We prepared a Co(III) complex and polypyrrole (PPy) composite thin films (CoN<sub>4</sub>-PPy) that was electrochemically deposited on the surface of a glassy carbon working electrode. Cyclic voltammetry studies indicate the superior performance of CoN<sub>4</sub>-PPy in charge storage in acidic electrolyte compared to alkaline and organic solutions. The CoN<sub>4</sub>-PPy material generated the highest amount of specific capacitance (up to 721.9 F/g) followed by Co salt and PPy (Co-PPy) material and PPy alone. Cyclic performance studies showed the excellent electrochemical stability of the CoN<sub>4</sub>-PPy film in the acidic medium. Simply electrochemically depositing an inexpensive Co(III) complex with a high electrically conducting polymer of PPy delivered a superior electrode material for supercapacitor applications. Therefore, the results indicate that novel thin films derived from Co(III) metal complex and PPy can store a large amount of energy and maintain high stability over many cycles, revealing its excellent potential in supercapacitor devices. Supercapacitor ; Polypyrrole ; Materials science ; Cyclic voltammetry ; Capacitance ; Electrode ; Electrochemistry ; Electrolyte ; Thin film ; Conductive polymer ; Energy storage ; Chemical engineering ; Glassy carbon ; Polymer ; Nanotechnology ; Composite material ; Chemistry ; Polymerization ; Power (physics) Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Junze Chen | Qinglang Ma | Xue‐Jun Wu | Liuxiao Li | Jiawei Liu | Hua Zhang
Source: Nano-Micro Letters (review, 2019-10-15)
Field: Materials Science — Materials Chemistry

Wet-Chemical Synthesis and Applications of Semiconductor Nanomaterial-Based Epitaxial Heterostructures Semiconductor nanomaterial-based epitaxial heterostructures with precisely controlled compositions and morphologies are of great importance for various applications in optoelectronics, thermoelectrics, and catalysis. Until now, various kinds of epitaxial heterostructures have been constructed. In this minireview, we will first introduce the synthesis of semiconductor nanomaterial-based epitaxial heterostructures by wet-chemical methods. Various architectures based on different kinds of seeds or templates are illustrated, and their growth mechanisms are discussed in detail. Then, the applications of epitaxial heterostructures in optoelectronics, catalysis, and thermoelectrics are described. Finally, we provide some challenges and personal perspectives for the future research directions of semiconductor nanomaterial-based epitaxial heterostructures. Heterojunction ; Epitaxy ; Nanomaterials ; Semiconductor ; Materials science ; Nanotechnology ; Optoelectronics Quantum Dots Synthesis And Properties ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Pezhman Mohammadi | Christopher Jonkergouw | Grégory Beaune | Peter Engelhardt | Ayaka Kamada | Jaakko V. I. Timonen | Tuomas P. J. Knowles | Merja Penttilä | Markus B. Linder
Source: Journal of Colloid and Interface Science (article, 2019-10-16)
Field: Materials Science — Biomaterials

Controllable coacervation of recombinantly produced spider silk protein using kosmotropic salts Recent developments suggest that the phase transition of natural and synthetic biomacromolecules represents an important and ubiquitous mechanism underlying structural assemblies toward the fabrication of high-performance materials. Such a transition results in the formation of condensed liquid droplets, described as condensates or coacervates. Being able to effectively control the assembly of such entities is essential for tuning the quality and their functionality. Here we describe how self-coacervation of genetically engineered spidroin-inspired proteins can be preceded by a wide range of kosmotropic salts. We studied the kinetics and mechanisms of coacervation in different conditions, from direct observation of initial phase separation to the early stage of nucleation/growth and fusion into large fluid assemblies. We found that coacervation induced by kosmotropic salts follows the classical nucleation theory and critically relies on precursor clusters of few weak-interacting protein monomers. Depending on solution conditions and the strength of the supramolecular interaction as a function of time, coacervates with a continuum of physiochemical properties were observed. We observed similar characteristics in other protein-based coacervates, which include having a spherical-ellipsoid shape in solution, an interconnected bicontinuous network, surface adhesion, and wetting properties. Finally, we demonstrated the use of salt-induced self-coacervates of spidroin-inspired protein as a cellulosic binder in dried condition. Coacervate ; Spider silk ; Nucleation ; Chemistry ; Chemical engineering ; Materials science ; Nanotechnology ; SILK ; Chromatography ; Organic chemistry ; Composite material Silk-based biomaterials and applications ; Insect and Arachnid Ecology and Behavior ; Surface Modification and Superhydrophobicity
Authors: Lennart R. S. Huizing | Shane R. Ellis | Bart Beulen | Florian P.Y. Barré | Paul B. Kwant | Rob J. Vreeken | Ron M. A. Heeren
Source: Clinical mass spectrometry (article, 2019-01-29)
Field: Chemistry — Spectroscopy

Development and evaluation of matrix application techniques for high throughput mass spectrometry imaging of tissues in the clinic Matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging (MSI) is a sensitive label-free technique that can be used to study a wide variety of clinical phenotypes. In this context, MSI offers huge diagnostic potential by supporting decision making in the determination of personalized treatment strategies. However, improvements in throughput and robustness are still needed before it finds a place in routine application. While the field has seen tremendous improvements in the throughput of data acquisition, robust and high-throughput sample preparation methods compatible with these acquisition methods need to be developed. To address this challenge, we have developed several methods to reduce the matrix application time to less than 5 min, while maintaining sensitivity and reproducibility. Workflows incorporating these methods provide a pipeline analysis time for MSI sample preparation and acquisition of less than 30 min. The reduced time for these analyses will contribute towards the integration of MSI into routine molecular pathology for clinical diagnostics. Computer science ; Mass spectrometry imaging ; Workflow ; Throughput ; Pipeline (software) ; Context (archaeology) ; Robustness (evolution) ; Mass spectrometry ; Chemistry ; Chromatography ; Database Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Ion-surface interactions and analysis
Authors: Robert A. Hoyt | M. M. Montemore | Ioanna Fampiou | Wei Chen | Georgios A. Tritsaris | Efthimios Kaxiras
Source: Journal of Chemical Information and Modeling (article, 2019-03-21)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of H Adsorption Energies on Ag Alloys Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, we present over 5000 DFT calculations of H adsorption energies on dilute Ag alloys and describe a general machine learning approach to rapidly predict H adsorption energies for new Ag alloy structures. We find that random forests provide accurate predictions and that the best features are combinations of traditional chemical and structural descriptors. Further analysis of our model errors and the underlying forest kernel reveals unexpected finite-size electronic structure effects: embedded dopant atoms can display counterintuitive behavior such as nonmonotonic trends as a function of composition and high sensitivity to dopants far from the adsorbing H atom. We explain these behaviors with simple tight-binding Hamiltonians and d-orbital densities of states. We also use variations among forest leaves to predict the uncertainty of predictions, which allows us to mitigate the effects of larger errors. Adsorption ; Counterintuitive ; Atom (system on chip) ; Dopant ; Catalysis ; Materials science ; Binding energy ; Chemical physics ; Computational chemistry ; Statistical physics ; Chemistry ; Thermodynamics ; Physical chemistry ; Computer science ; Atomic physics ; Physics ; Quantum mechanics ; Doping ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Yuhao Li | Yangyang Kong | Jinlin Peng | Chuanbin Yu | Zhi Li | Penghui Li | Liu Yun-ya | Cun‐Fa Gao | Rong Wu
Source: Journal of Materiomics (article, 2019-03-28)
Field: Materials Science — Materials Chemistry

Rapid identification of two-dimensional materials via machine learning assisted optic microscopy A combination of Fresnel law and machine learning method is proposed to identify the layer counts of 2D materials. Three indexes, which are optical contrast, red-green-blue, total color difference, are presented to illustrate and simulate the visibility of 2D materials on Si/SiO2 substrate, and the machine learning algorithms, which are k-mean clustering and k-nearest neighbors, are employed to obtain thickness database of 2D material and test the optical images of 2D materials via red-green-blue index. The results show that this method can provide fast, accurate and large-area property of 2D material. With the combination of artificial intelligence and nanoscience, this machine learning assisted method eases the workload and promotes fundamental research of 2D materials. Keywords: Two-dimensional materials, Optical contrast, Total color difference, Red-green-blue, K-means clustering, K-nearest neighbors (k-NN) Visibility ; Materials science ; Cluster analysis ; Artificial intelligence ; Computer science ; Contrast (vision) ; Layer (electronics) ; Substrate (aquarium) ; Optics ; Machine learning ; Pattern recognition (psychology) ; Algorithm ; Nanotechnology 2D Materials and Applications ; Graphene research and applications ; Ga2O3 and related materials
Authors: Eric A. Walker | Joshua Kammeraad | Jonathan Goetz | Michael T. Robo | Ambuj Tewari | Paul M. Zimmerman
Source: Journal of Chemical Information and Modeling (article, 2019-08-05)
Field: Materials Science — Materials Chemistry

Learning To Predict Reaction Conditions: Relationships between Solvent, Molecular Structure, and Catalyst Reaction databases provide a great deal of useful information to assist planning of experiments but do not provide any interpretation or chemical concepts to accompany this information. In this work, reactions are labeled with experimental conditions, and network analysis shows that consistencies within clusters of data points can be leveraged to organize this information. In particular, this analysis shows how particular experimental conditions (specifically solvent) are effective in enabling specific organic reactions (Friedel-Crafts, Aldol addition, Claisen condensation, Diels-Alder, and Wittig), including variations within each reaction class. Network analysis shows data points for reactions tend to break into clusters that depend on the catalyst and chemical structure. This type of clustering, which mimics how a chemist reasons, is derived directly from the network. Therefore, the findings of this work could augment synthesis planning by providing predictions in a fashion that mimics human chemists. To numerically evaluate solvent prediction ability, three methods are compared: network analysis (through the <i>k</i>-nearest neighbor algorithm), a support vector machine, and a deep neural network. The most accurate method in 4 of the 5 test cases is the network analysis, with deep neural networks also showing good prediction scores. The network analysis tool was evaluated by an expert panel of chemists, who generally agreed that the algorithm produced accurate solvent choices while simultaneously being transparent in the underlying reasons for its predictions. Computer science ; Speciality chemicals ; Artificial neural network ; Artificial intelligence ; Cluster analysis ; Machine learning ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Bilal Chehaibou | Michaël Badawi | Tomáš Bučko | Timur Bazhirov | Dario Rocca
Source: Journal of Chemical Theory and Computation (article, 2019-10-15)
Field: Materials Science — Materials Chemistry

Computing RPA Adsorption Enthalpies by Machine Learning Thermodynamic Perturbation Theory Correlated quantum-chemical methods for condensed matter systems, such as the random phase approximation (RPA), hold the promise of reaching a level of accuracy much higher than that of conventional density functional theory approaches. However, the high computational cost of such methods hinders their broad applicability, in particular for finite-temperature molecular dynamics simulations. We propose a method that couples machine learning techniques with thermodynamic perturbation theory to estimate finite-temperature properties using correlated approximations. We apply this approach to compute the enthalpies of adsorption in zeolites and show that reliable estimates can be obtained by training a machine learning model with as few as 10 RPA energies. This approach paves the way to the broader use of computationally expensive quantum-chemical methods to predict the finite-temperature properties of condensed matter systems. Perturbation theory (quantum mechanics) ; Density functional theory ; Adsorption ; Computer science ; Quantum chemical ; Statistical physics ; Quantum ; Perturbation (astronomy) ; Random phase approximation ; Quantum chemistry ; Thermodynamic integration ; Applied mathematics ; Molecular dynamics ; Molecule ; Physics ; Chemistry ; Mathematics ; Computational chemistry ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: Shenghong Ju | Junichiro Shiomi
Source: Nanoscale and Microscale Thermophysical Engineering (article, 2019-02-19)
Field: Materials Science — Materials Chemistry

Materials Informatics for Heat Transfer: Recent Progresses and Perspectives With the advances in materials and integration of electronics and thermoelectrics, the demand for novel crystalline materials with ultimate high/low thermal conductivity is increasing. However, search for optimal thermal materials is a challenge due to the tremendous degrees of freedom in the composition and structure of crystal compounds and nanostructures, and thus empirical search would be exhausting. Materials informatics, which combines the simulation/experiment with machine learning, is now gaining great attention as a tool to accelerate the search of novel thermal materials. In this review, we discuss recent progress in developing materials informatics (MI) for heat transport: the exploration of crystals with high/low-thermal conductivity via high-throughput screening, and nanostructure design for high/low-thermal conductance using the Bayesian optimization and Monte Carlo tree search. The progresses show that the MI methods are useful for designing thermal functional materials. We end by addressing the remaining issues and challenges for further development. Materials science ; Informatics ; Heat transfer ; Materials informatics ; Nanotechnology ; Engineering physics ; Systems engineering ; Thermodynamics ; Health informatics ; Engineering ; Engineering informatics ; Medicine ; Physics Machine Learning in Materials Science ; Injection Molding Process and Properties ; Thermal properties of materials