jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ang Wu | Juanhua Zhu | Taiyong Ren
Source: Computers & Electrical Engineering (article, 2019-11-14)
Field: Chemistry — Analytical Chemistry

Detection of apple defect using laser-induced light backscattering imaging and convolutional neural network Convolutional neural network ; Preprocessor ; Artificial intelligence ; Computer science ; Laser ; Similarity (geometry) ; Pattern recognition (psychology) ; Computer vision ; Artificial neural network ; Image (mathematics) ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mingfei Pan | Zongjia Yin | Kaixin Liu | Xiaoling Du | Huilin Liu | Shuo Wang
Source: Nanomaterials (review, 2019-09-17)
Field: Materials Science — Materials Chemistry

Carbon-Based Nanomaterials in Sensors for Food Safety Food safety is one of the most important and widespread research topics worldwide. The development of relevant analytical methods or devices for detection of unsafe factors in foods is necessary to ensure food safety and an important aspect of the studies of food safety. In recent years, developing high-performance sensors used for food safety analysis has made remarkable progress. The combination of carbon-based nanomaterials with excellent properties is a specific type of sensor for enhancing the signal conversion and thus improving detection accuracy and sensitivity, thus reaching unprecedented levels and having good application potential. This review describes the roles and contributions of typical carbon-based nanomaterials, such as mesoporous carbon, single- or multi-walled carbon nanotubes, graphene and carbon quantum dots, in the construction and performance improvement of various chemo- and biosensors for various signals. Additionally, this review focuses on the progress of applications of this type of sensor in food safety inspection, especially for the analysis and detection of all types of toxic and harmful substances in foods. Food safety ; Nanotechnology ; Carbon nanotube ; Nanomaterials ; Risk analysis (engineering) ; Carbon fibers ; Computer science ; Materials science ; Biochemical engineering ; Business ; Engineering ; Medicine Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Eric Jonas | Stefan Kühn
Source: Journal of Cheminformatics (article, 2019-08-06)
Field: Chemistry — Spectroscopy

Rapid prediction of NMR spectral properties with quantified uncertainty Accurate calculation of specific spectral properties for NMR is an important step for molecular structure elucidation. Here we report the development of a novel machine learning technique for accurately predicting chemical shifts of both [Formula: see text] and [Formula: see text] nuclei which exceeds DFT-accessible accuracy for [Formula: see text] and [Formula: see text] for a subset of nuclei, while being orders of magnitude more performant. Our method produces estimates of uncertainty, allowing for robust and confident predictions, and suggests future avenues for improved performance. Computer science ; Statistical physics ; Algorithm ; Data mining ; Physics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Protein Structure and Dynamics
Authors: Francesca Luzi | Elisa Pannucci | Luca Santi | J. M. Kenny | Luigi Torre | Roberta Bernini | Débora Puglia
Source: Polymers (article, 2019-12-03)
Field: Materials Science — Biomaterials

Gallic Acid and Quercetin as Intelligent and Active Ingredients in Poly(vinyl alcohol) Films for Food Packaging Gallic acid (GA) and quercetin (QC) were used as active ingredients in poly(vinyl alcohol) (PVA) film formulations obtained by solvent casting process. The effect of two different percentages (5 and 10 % wt.) on morphological behavior, thermal stability, optical, mechanical, and release properties of PVA were investigated, while migration with food stimulants and antioxidant properties were tested taking into account the final application as food packaging systems. The results showed how different dispersability in PVA water solutions gave different results in term of deformability (mean value of ε <sub>PVA/5GA</sub> = 280% and ε <sub>PVA/5QC</sub> = 255%, with 190% for neat PVA), comparable values for antioxidant activity at the high contents (Radical Scavenging Activity, RSA(%) <sub>PVA/</sub><sub>10</sub><sub>GA</sub> = 95 and RSA(%) <sub>PVA/10QC</sub> = 91) and different coloring attitude of the polymeric films. It was proved that GA, even if it represents the best antioxidant ingredient to be used with PVA and can be easily dispersed in water, it gives more rigid films in comparison to QC, that indeed was more efficient in tuning the deformability of the PVA films, due the presence of sole hydroxyl groups carrying agent. The deviation of the film coloring towards greenish tones for GA films and redness for QC films after 7 and within 21 days in the simulated conditions confirmed the possibility of using easy processable PVA films as active and intelligent films in food packaging. Vinyl alcohol ; Gallic acid ; Food packaging ; Active packaging ; Materials science ; Solvent ; Quercetin ; Alcohol ; Thermal stability ; Casting ; Antioxidant ; Chemical engineering ; Organic chemistry ; Chemistry ; Food science ; Composite material ; Polymer Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Phytochemicals and Antioxidant Activities
Authors: Ming‐Hsien Chan | Ru‐Shi Liu | Michael Hsiao
Source: Nanoscale (review, 2019-01-01)
Field: Materials Science — Materials Chemistry

Graphitic carbon nitride-based nanocomposites and their biological applications: a review Quantum dots (QDs) have extensive application prospects in the fields of optics, magnetism, catalysis, and biomedicine. New carbon-doped QDs are currently being used in these research fields. Graphitic carbon nitride QDs (g-CNs) composed of only carbon and nitrogen have attracted attention because of their unique optical and catalytic properties. g-CNs have numerous electronic properties and can be used as photocatalytic modifiers in a wide range of applications in electrochemistry. Additionally, g-CNs also have biological potential and due to their chemical composition have extremely low toxicity; their blue light emission can be applied to biological imaging, and their appropriate energy level (2.7 eV) allows electrons to be deposited on their surface, which allows g-CNs to be used as photosensitizers in optical therapy. Finally, g-CNs can be combined with other nanomaterials to form composite materials, which can result in new advantages not seen in either of the materials alone. In this manuscript, we thoroughly report the most recent findings regarding the synthesis of g-CNs and their respective properties. We report the advantages of g-CNs conferred by their unique properties and their advantages for application in current biology and medicines. Graphitic carbon nitride ; Nanotechnology ; Materials science ; Nanomaterials ; Biological imaging ; Carbon fibers ; Nanocomposite ; Photocatalysis ; Catalysis ; Fluorescence ; Composite number ; Chemistry ; Physics Carbon and Quantum Dots Applications ; Advanced Photocatalysis Techniques ; Advanced Nanomaterials in Catalysis
Authors: Pascal M. Vecsei | Kenny Choo | J. Chang | Titus Neupert
Source: Physical review. B./Physical review. B (article, 2019-06-11)
Field: Materials Science — Materials Chemistry

Neural network based classification of crystal symmetries from x-ray diffraction patterns Machine learning algorithms based on artificial neural networks have proven very useful for a variety of classification problems. Here we apply them to a well-known problem in crystallography, namely the classification of x-ray diffraction (XRD) patterns of inorganic powder specimens by the respective crystal system and space group. Over ${10}^{5}$ theoretically computed powder XRD patterns were obtained from inorganic crystal structure databases and used to train a deep dense neural network. For space group classification, we obtain an accuracy of around 54% on experimental data. Finally, we introduce a scheme where the network has the option to refuse the classification of XRD patterns that would be classified with a large uncertainty. This enhances the accuracy on experimental data to 82% at the expense of having half of the experimental data unclassified. With further improvements of neural network architecture and experimental data availability, machine learning constitutes a promising complement to classical structure determination methodology. Diffraction ; Crystal (programming language) ; Artificial neural network ; X-ray crystallography ; Crystallography ; Materials science ; Physics ; Computer science ; Artificial intelligence ; Optics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Matthew R. Carbone | Shinjae Yoo | Mehmet Topsakal | Deyu Lu
Source: Physical Review Materials (article, 2019-03-13)
Field: Materials Science — Materials Chemistry

Classification of local chemical environments from x-ray absorption spectra using supervised machine learning X-ray absorption near-edge structure (XANES) spectroscopy is a robust and element-specific tool for probing the atomic structure of materials. Traditional spectroscopy methods work in the forward direction by simulating XANES spectra from atomic models. Here, the authors present the opposite: a computational method predicting local structural geometry from XANES spectra in which the so-called inverse problem is solved using supervised machine learning. The robustness and fidelity of the method are demonstrated by an average of 86% classification accuracy in the K-edge XANES spectra of hundreds of materials across eight $3d$ transition metal families. XANES ; Robustness (evolution) ; Materials science ; Spectral line ; Spectroscopy ; X-ray absorption spectroscopy ; Absorption spectroscopy ; Artificial intelligence ; Computer science ; Optics ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Anja Aarva | Volker L. Deringer | Sami Sainio | Tomi Laurila | A. Miguel
Source: Chemistry of Materials (article, 2019-10-28)
Field: Materials Science — Materials Chemistry

Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part I: Fingerprint Spectra Carbonaceous materials, especially tetrahedral amorphous carbon (ta-C), can form complex functionalized surface structures and are thus promising candidates for applications in biomedical devices and electrochemistry. Functional groups at ta-C surfaces have been widely studied by spectroscopic techniques; however, interpretation of the experimental data is extremely difficult, especially in the case of X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS). The assignments of experimental XPS and XAS signals are normally based on references obtained from molecular or crystalline samples, which are simplified approximations for the far more complex amorphous structures. Here, we use extensive density functional theory (DFT) simulations to predict XAS and XPS signatures for carbon-based materials in more realistic environments, building on large data sets of structural models generated by a machine-learning (ML) interatomic potential. The results indicate clear signatures: individual fingerprint XAS spectra and distinctive XPS binding energy distributions, both in terms of center and broadness of the signal, for chemically different groups. The results point out what kind of structural information can and cannot be extracted with X-ray spectroscopy. This study will enable a deeper physicochemical understanding of experimental data and ultimately theory-based identification and quantification of functional groups in carbonaceous materials. X-ray photoelectron spectroscopy ; X-ray absorption spectroscopy ; Density functional theory ; Amorphous solid ; Binding energy ; Absorption spectroscopy ; Spectroscopy ; Materials science ; Chemistry ; Chemical physics ; Computational chemistry ; Crystallography ; Nuclear magnetic resonance ; Atomic physics ; Physics ; Optics Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; Electron and X-Ray Spectroscopy Techniques
Authors: Devin A. Gredell | Amelia Schroeder | K. E. Belk | Corey D. Broeckling | Adam L. Heuberger | Soo Young Kim | D. A. King | Todd K. Shackelford | Julia L. Sharp | T. L. Wheeler | Dale R. Woerner | Jessica E. Prenni
Source: Scientific Reports (article, 2019-04-05)
Field: Chemistry — Spectroscopy

Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Here, we present an evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared to generate predictive models using REIMS data to classify beef quality attributes based on the United States Department of Agriculture (USDA) quality grade, production background, breed type and muscle tenderness. The results revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a "one size fits all" approach to developing predictive models from REIMS data is not appropriate. The highest performing models for each classification achieved prediction accuracies between 81.5-99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef. Machine learning ; Computer science ; Mass spectrometry ; Algorithm ; Artificial intelligence ; Data mining ; Chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography
Authors: Rinkle Juneja | George Yumnam | Swanti Satsangi | Abhishek K. Singh
Source: Chemistry of Materials (article, 2019-06-27)
Field: Materials Science — Materials Chemistry

Coupling the High-Throughput Property Map to Machine Learning for Predicting Lattice Thermal Conductivity Low thermal conductivity materials are crucial for applications such as thermoelectric conversion of waste heat to useful energy and thermal barrier coatings. On the other hand, high thermal conductivity materials are necessary for cooling electronic devices. However, search for such materials via explicit evaluation of thermal conductivity either experimentally or computationally is very challenging. Here, we carried out high-throughput ab initio calculations, on a dataset containing 195 binary, ternary, and quaternary compounds. The lattice thermal conductivity κl values of 120 dynamically stable and nonmetallic compounds are calculated, which span over 3 orders of magnitude. Among these, 11 ultrahigh and 15 ultralow κl materials are identified. An analysis of generated property map of this dataset reveals a strong dependence of κl on simple descriptors, namely, maximum phonon frequency, integrated Grüneisen parameter up to 3 THz, average atomic mass, and volume of the unit cell. Using these descriptors, a Gaussian process regression-based machine learning (ML) model is developed. The model predicts log-scaled κl with a very small root mean square error of ∼0.21. Comparatively, the Slack model, which uses more involved parameters, severely overestimates κl. The superior performance of our ML model can ensure a reliable and accelerated search for multitude of low and high thermal conductivity materials. Thermal conductivity ; Ternary operation ; Materials science ; Phonon ; Lattice (music) ; Binary number ; Thermoelectric materials ; Thermodynamics ; Ab initio ; Computer science ; Condensed matter physics ; Composite material ; Chemistry ; Mathematics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Hang Zhang | Hao Zeng | Arri Priimägi | Olli Ikkala
Source: Nature Communications (article, 2019-07-22)
Field: Materials Science — Materials Chemistry

Programmable responsive hydrogels inspired by classical conditioning algorithm Living systems have inspired research on non-biological dynamic materials and systems chemistry to mimic specific complex biological functions. Upon pursuing ever more complex life-inspired non-biological systems, mimicking even the most elementary aspects of learning is a grand challenge. We demonstrate a programmable hydrogel-based model system, whose behaviour is inspired by associative learning, i.e., conditioning, which is among the simplest forms of learning. Algorithmically, associative learning minimally requires responsivity to two different stimuli and a memory element. Herein, nanoparticles form the memory element, where a photoacid-driven pH-change leads to their chain-like assembly with a modified spectral behaviour. On associating selected light irradiation with heating, the gel starts to melt upon the irradiation, originally a neutral stimulus. A logic diagram describes such an evolution of the material response. Coupled chemical reactions drive the system out-of-equilibrium, allowing forgetting and memory recovery. The findings encourage to search non-biological materials towards associative and dynamic properties. Forgetting ; Computer science ; Associative property ; Conditioning ; Self-healing hydrogels ; Content-addressable memory ; Classical conditioning ; Living systems ; Biological system ; Algorithm ; Artificial intelligence ; Materials science ; Artificial neural network ; Mathematics ; Biology Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Supramolecular Self-Assembly in Materials
Authors: Vo Van Giau | Seong Soo A. An | John Hulme
Source: Drug Design Development and Therapy (review, 2019-01-01)
Field: Materials Science — Materials Chemistry

&lt;p&gt;Recent advances in the treatment of pathogenic infections using antibiotics and nano-drug delivery vehicles&lt;/p&gt; The worldwide misuse of antibiotics and the subsequent rise of multidrug-resistant pathogenic bacteria have prompted a paradigm shift in the established view of antibiotic and bacterial-human relations. The clinical failures of conventional antibiotic therapies are associated with lengthy detection methods, poor penetration at infection sites, disruption of indigenous microflora and high potential for mutational resistance. One of the most promising strategies to improve the efficacy of antibiotics is to complex them with micro or nano delivery materials. Such materials/vehicles can shield antibiotics from enzyme deactivation, increasing the therapeutic effectiveness of the drug. Alternatively, drug-free nanomaterials that do not kill the pathogen but target virulent factors such as adhesins, toxins, or secretory systems can be used to minimize resistance and infection severity. The main objective of this review is to examine the potential of the aforementioned materials in the detection and treatment of antibiotic-resistant pathogenic organisms. Antibiotics ; Drug ; Drug delivery ; Medicine ; Pharmacology ; Microbiology ; Nanotechnology ; Biology ; Materials science Nanoparticles: synthesis and applications ; Biosensors and Analytical Detection ; Bacterial biofilms and quorum sensing
Authors: Liang Ge | Dingxing Wang | Renyi Xing | Di Ma | Patrick J. Walsh | Chao Feng
Source: Nature Communications (article, 2019-09-25)
Field: Chemistry — Organic Chemistry

Photoredox-catalyzed oxo-amination of aryl cyclopropanes Cyclopropanes represent a class of versatile building blocks in modern organic synthesis. While the release of ring strain offers a thermodynamic driving force, the control of selectivity for C-C bond cleavage and the subsequent regiochemistry of the functionalization remains difficult, especially for unactivated cyclopropanes. Here we report a photoredox-coupled ring-opening oxo-amination of electronically unbiased cyclopropanes, which enables the expedient construction of a host of structurally diverse β-amino ketone derivatives. Through one electron oxidation, the relatively inert aryl cyclopropanes are readily converted into reactive radical cation intermediates, which in turn participate in the ensuing ring-opening functionalizations. Based on mechanistic studies, the present oxo-amination is proposed to proceed through an S<sub>N</sub>2-like nucleophilic attack/ring-opening manifold. This protocol features wide substrate scope, mild reaction conditions, and use of dioxygen as an oxidant both for catalyst regeneration and oxygen-incorporation. Moreover, a one-pot formal aminoacylation of olefins is described through a sequential cyclopropanation/oxo-amination. Amination ; Regioselectivity ; Nucleophile ; Chemistry ; Combinatorial chemistry ; Aryl ; Ketone ; Bond cleavage ; Cyclopropane ; Cyclopropanation ; Ring (chemistry) ; Photoredox catalysis ; Organic synthesis ; Catalysis ; Organic chemistry Cyclopropane Reaction Mechanisms ; Catalytic C–H Functionalization Methods ; Sulfur-Based Synthesis Techniques
Authors: Qun‐Li Lei | Ran Ni
Source: Proceedings of the National Academy of Sciences (article, 2019-10-30)
Field: Materials Science — Materials Chemistry

Hydrodynamics of random-organizing hyperuniform fluids Disordered hyperuniform structures are locally random while uniform like crystals at large length scales. Recently, an exotic hyperuniform fluid state was found in several nonequilibrium systems, while the underlying physics remains unknown. In this work, we propose a nonequilibrium (driven-dissipative) hard-sphere model and formulate a hydrodynamic theory based on Navier-Stokes equations to uncover the general mechanism of the fluidic hyperuniformity (HU). At a fixed density, this model system undergoes a smooth transition from an absorbing state to an active hyperuniform fluid and then, to the equilibrium fluid by changing the dissipation strength. We study the criticality of the absorbing-phase transition. We find that the origin of fluidic HU can be understood as the damping of a stochastic harmonic oscillator in q space, which indicates that the suppressed long-wavelength density fluctuation in the hyperuniform fluid can exhibit as either acoustic (resonance) mode or diffusive (overdamped) mode. Importantly, our theory reveals that the damping dissipation and active reciprocal interaction (driving) are the two ingredients for fluidic HU. Based on this principle, we further demonstrate how to realize the fluidic HU in an experimentally accessible active spinner system and discuss the possible realization in other systems. Dissipative system ; Fluidics ; Non-equilibrium thermodynamics ; Dissipation ; Physics ; Realization (probability) ; Harmonic oscillator ; Statistical physics ; Phase transition ; Work (physics) ; Classical mechanics ; Quantum mechanics ; Mathematics ; Engineering Material Dynamics and Properties ; Micro and Nano Robotics ; Nonlinear Dynamics and Pattern Formation