jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anquan Jiang | Yan Zhang
Source: NPG Asia Materials (article, 2019-01-23)
Field: Materials Science — Materials Chemistry

Next-generation ferroelectric domain-wall memories: principle and architecture Abstract The downscaling of commercial one-transistor–one capacitor ferroelectric memory cells is limited by the available signal window for the use of a charge integration readout technique. However, the erasable conducting charged walls that occur in insulating ferroelectrics can be used to read the bipolar domain states. Both out-of-plane and in-plane cell configurations are compared for the next sub-10-nm integration of ferroelectric domain wall memories with high reliability. It is highlighted that a nonvolatile read strategy of domain information within mesa-like cells under the application of a strong in-plane read field can enable a massive crossbar connection to reduce mobile charge accumulation at the walls and crosstalk currents from neighboring cells. The memory has extended application in analog data processing and neural networks. Ferroelectricity ; Materials science ; Crossbar switch ; Non-volatile memory ; Capacitor ; Optoelectronics ; Domain wall (magnetism) ; Ferroelectric capacitor ; Domain (mathematical analysis) ; Transistor ; Memory cell ; Computer science ; Electrical engineering ; Voltage ; Dielectric ; Physics ; Magnetic field ; Telecommunications ; Engineering Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies
Authors: Seyed Mohammad Motevalli | Ahmed Shaker Eltahan | Lu Liu | Andrea Magrini | Nicola Rosato | Weisheng Guo | Massimo Bottini | Xing‐Jie Liang
Source: Biophysics Reports (article, 2019-01-11)
Field: Materials Science — Biomaterials

Co-encapsulation of curcumin and doxorubicin in albumin nanoparticles blocks the adaptive treatment tolerance of cancer cells The adaptive treatment tolerance (ATT) of cancer cells is the main encumbrance to cancer chemotherapy. A potential solution to this problem is to treat cancer cells with multiple drugs using nanoparticles (NPs). In this study, we tested the co-administration of curcumin (Cur) and doxorubicin (Dox) to MCF-7 resistant breast cancer cells to block the ATT and elicit efficient cell killing. Drugs were co-administered to cells both sequentially and simultaneously. Sequential drug co-administration was carried out by pre-treating the cells with albumin nanoparticles (ANPs) loaded with Cur (Cur@ANPs) followed by treatment with Dox-loaded ANPs (Dox@ANPs). Simultaneous drug co-administration was carried out by treating the cells with ANPs loaded with both the drugs (Cur/Dox@ANPs). We found that the simultaneous drug co-administration led to a greater intra-cellular accumulation of Dox and cell killing with respect to the sequential drug co-administration. However, the simultaneous drug co-administration led to a lower intra-cellular accumulation of Cur with respect to the sequential drug co-administration. We showed that this result was due to the aggregation and entrapment of Cur in the lysosomes as soon as it was released from Cur@ANPs, a phenomenon called lysosomotropism. In contrast, the simultaneous release of Dox and Cur from Cur/Dox@ANPs into the lysosomes led to lysosomal pH elevation, which, in turn, avoided Cur aggregation, led to lysosome swelling and drug release in the cytosol, and finally provoked efficient cell killing. Our study shed the light on the molecular processes driving the therapeutic effects of anti-cancer drugs co-administered to cancer cells in different manners. Curcumin ; Doxorubicin ; Chemistry ; Pharmacology ; Cancer cell ; Drug ; Albumin ; Chemotherapy ; Cancer ; Medicine ; Biochemistry ; Internal medicine Nanoparticle-Based Drug Delivery ; Curcumin's Biomedical Applications ; Cancer Treatment and Pharmacology
Authors: K. P. Ananthapadmanabhan
Source: Tenside Surfactants Detergents (article, 2019-09-10)
Field: Chemistry — Organic Chemistry

Amino-Acid Surfactants in Personal Cleansing (Review) Abstract The consumer demand for greener, sustainable and skin friendly chemicals is driving the use of amino-acid based surfactants in the personal care area. Acyl glutamates and glycinates are already being used in commercial products. Available literature clearly shows that the amino acid based surfactants are generally milder than their corresponding alkyl sulfates and carboxylates. However, they do offer some interesting challenges in the area of structuring and consumer desired lather properties. Furthermore, the amino acid surfactants, unlike alkyl sulfates, have pH as an interesting variable for fine-tuning their functional properties. In this paper, the solution, interfacial and skin mildness properties of commercially relevant amino acid surfactants are reviewed and the opportunities and challenges for their wider application are outlined. Chemistry ; Alkyl ; Amino acid ; Structuring ; Organic chemistry ; Environmentally friendly ; Biochemistry ; Business ; Ecology Surfactants and Colloidal Systems ; Environmental Chemistry and Analysis ; Advanced Polymer Synthesis and Characterization
Authors: Martin Koller
Source: Chemical and Biochemical Engineering Quarterly (article, 2019-01-16)
Field: Materials Science — Biomaterials

Chemical and Biochemical Engineering Approaches in Manufacturing Polyhydroxyalkanoate (PHA) Biopolyesters of Tailored Structure with Focus on the Diversity of Building Blocks Polyhydroxyalkanoates (PHA) constitute prokaryotic storage materials not only harnessing microbial cells with benefits for survival under challenging environmental conditions, but also attracting attention as biological materials with properties resembling those of currently used thermoplasts and elastomers of petrochemical origin. Strongly dependent on their monomeric composition and microstructure, PHAs exact material properties are predestined in statu nascendi, hence, during their biosynthesis. The present review sheds light on established and emerging strategies to produce differently composed PHA homo-, co-, ter-, and quarterpolyesters from the groups of short-, medium-, and long-chain PHA. It is shown how microbial strain selection, sophisticated genetic strain engineering based on synthetic biology approaches, advanced feeding strategies, and smart process engineering can be implemented to generate PHA of tailored monomeric composition, microstructure, molar mass, and molar mass distribution. Tailoring these parameters offers the possibility to produce customer-oriented PHA for various purposes, such as packaging materials, carriers of pharmaceutically active compounds, implants, or other emerging fields of use. Polyhydroxyalkanoates ; Diversity (politics) ; Focus (optics) ; Biochemical engineering ; Engineering ; Chemistry ; Manufacturing engineering ; Biology ; Bacteria ; Sociology ; Physics biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Additive Manufacturing and 3D Printing Technologies
Authors: Angela Capocefalo | Daisy Mammucari | Francesco Brasili | Claudia Fasolato | F. Bordi | P. Postorino | Fabio Domenici
Source: Frontiers in Chemistry (article, 2019-06-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring the Potentiality of a SERS-Active pH Nano-Biosensor The merging of the molecular specificity of Raman spectroscopy with the extraordinary optical properties of metallic nanoarchitectures is at the heart of Surface Enhanced Raman Spectroscopy (SERS), which in the last few decades proved its worth as powerful analytical tool with detection limits pushed to the single molecule recognition. Within this frame, SERS-based nanosensors for localized pH measurements have been developed and employed for a wide range of applications. Nevertheless, to improve the performances of such nanosensors, many key issues concerning their assembling, calibration and stability, that could significantly impact on the outcome of the pH measurements, need to be clarified. Here, we report on the detailed characterization of a case study SERS-active pH nanosensor, based on the conjugation of gold nanoparticles with the pH-sensitive molecular probe 4-mercaptobenzoic acid (4MBA). We analyzed and optimized all the aspects of the synthesis procedure and of the operating conditions to preserve the sensor stability and provide the highest responsiveness to pH. Exploiting the dependence of the SERS spectrum on the protonation degree of the carboxylic group at the edge of the 4MBA molecules, we derived a calibration curve for the nanosensor. The extrapolated working point, i.e., the pH value corresponding to the highest sensitivity, falls at pH 5.6, which corresponds to the pKa value of the molecule confined at the nanoparticle surface. A shift of the pKa of 4MBA, observed on the molecules confined at the nanostructured interface respect to the bulk counterpart, unveils the opportunity to assembly a SERS-based pH nanosensor with the ability to select its working point in the sensitivity region of interest, by acting on the nanostructured surface on which the molecular probe is confined. As a proof-of-concept, the nanosensor was successfully employed to measure the extracellular pH of normal and cancer cells, demonstrating the capability to discriminate between them. Nanosensor ; Raman spectroscopy ; Protonation ; Surface-enhanced Raman spectroscopy ; Molecule ; Nanotechnology ; Nanoparticle ; Biosensor ; Chemistry ; Calibration curve ; Colloidal gold ; Spectroscopy ; Calibration ; Materials science ; Analytical Chemistry (journal) ; Combinatorial chemistry ; Detection limit ; Raman scattering ; Organic chemistry ; Chromatography ; Physics ; Ion ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical Analysis and Applications
Authors: Thomas Unden | Daniel Louzon | Michael Zwolak | W. H. Zurek | Fedor Jelezko
Source: Physical Review Letters (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

Revealing the Emergence of Classicality Using Nitrogen-Vacancy Centers The origin of classical reality in our quantum world is a long-standing mystery. Here, we examine a nitrogen-vacancy center in diamond evolving in the presence of its magnetic nuclear spin environment which is formed by the natural appearance of carbon ^{13}C atoms in the diamond lattice, to study quantum Darwinism-the proliferation of information about preferred quantum states throughout the world via the environment. This redundantly imprinted information accounts for the perception of objective reality, as it is independently accessible by many without perturbing the system of interest. To observe this process, we implement a novel dynamical decoupling scheme that enables the measurement and control of several nuclear spins (the environment E) interacting with a nitrogen vacancy (the system S). Our experiment demonstrates that, in the course of the decoherence of S, redundant information is indeed imprinted onto E, giving rise to incipient classical objectivity-a consensus recorded in redundant copies, and available from the fragments of the nuclear spin environment E, about the state of S. This provides the first laboratory verification of the process responsible for the emergence of the objective classical world from the underlying quantum substrate. Quantum decoherence ; Spins ; Diamond ; Vacancy defect ; Dynamical decoupling ; Qubit ; Quantum information ; Quantum ; Physics ; Quantum metrology ; Quantum mechanics ; Condensed matter physics ; Chemistry ; Quantum network Diamond and Carbon-based Materials Research ; Advanced Memory and Neural Computing ; High-pressure geophysics and materials
Authors: Olsthoorn, Bart | Geilhufe, R. Matthias | Borysov, Stanislav S. | Balatsky, Alexander V.
Source: Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Band gap prediction for large organic crystal structures with machine learning Machine-learning models are capable of capturing the structure-property relationship from a dataset of computationally demanding ab initio calculations. Over the past two years, the Organic Materials Database (OMDB) has hosted a growing number of calculated electronic properties of previously synthesized organic crystal structures. The complexity of the organic crystals contained within the OMDB, which have on average 82 atoms per unit cell, makes this database a challenging platform for machine learning applications. In this paper, the focus is on predicting the band gap which represents one of the basic properties of a crystalline materials. With this aim, a consistent dataset of 12 500 crystal structures and their corresponding DFT band gap are released, freely available for download at https://omdb.mathub.io/dataset. An ensemble of two state-of-the-art models reach a mean absolute error (MAE) of 0.388 eV, which corresponds to a percentage error of 13% for an average band gap of 3.05 eV. Finally, the trained models are employed to predict the band gap for 260 092 materials contained within the Crystallography Open Database (COD) and made available online so that the predictions can be obtained for any arbitrary crystal structure uploaded by a user. Band gap ; Artificial intelligence ; Crystal (programming language) ; Computer science ; Machine learning ; Crystal structure ; Ab initio ; Upload ; Property (philosophy) ; Algorithm ; Materials science ; Crystallography ; Chemistry ; Optoelectronics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Adam S. Abbott | Justin M. Turney | Boyi Zhang | Daniel G. A. Smith | Doaa Altarawy | Henry F. Schaefer
Source: Journal of Chemical Theory and Computation (article, 2019-06-17)
Field: Materials Science — Materials Chemistry

PES-Learn: An Open-Source Software Package for the Automated Generation of Machine Learning Models of Molecular Potential Energy Surfaces We introduce a free and open-source software package (PES-Learn) which largely automates the process of producing high-quality machine learning models of molecular potential energy surfaces (PESs). PES-Learn incorporates a generalized framework for producing grid points across a PES that is compatible with most electronic structure theory software. The newly generated or externally supplied PES data can then be used to train and optimize neural network or Gaussian process models in a completely automated fashion. Robust hyperparameter optimization schemes designed specifically for molecular PES applications are implemented to ensure that the best possible model for the data set is fit with high quality. The performance of PES-Learn toward fitting a few semiglobal PESs from the literature is evaluated. We also demonstrate the use of PES-Learn machine learning models in carrying out high-level vibrational configuration interaction computations on water and formaldehyde. Computer science ; Hyperparameter ; Hyperparameter optimization ; Software ; Process (computing) ; Machine learning ; Artificial intelligence ; Set (abstract data type) ; Artificial neural network ; Gaussian process ; Energy (signal processing) ; Computation ; Computational science ; Gaussian ; Algorithm ; Support vector machine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Dicho Stratiev | Ivelina Shishkova | Ivaylo Tankov | A. Pavlova
Source: Journal of Petroleum Science and Engineering (review, 2019-03-19)
Field: Chemistry — Analytical Chemistry

Challenges in characterization of residual oils. A review Asphaltene ; Residual ; Characterization (materials science) ; Fraction (chemistry) ; Residual oil ; Chemistry ; Osmometer ; Mole fraction ; Vacuum distillation ; Chemical composition ; Analytical Chemistry (journal) ; Mass fraction ; Organic chemistry ; Materials science ; Physical chemistry ; Nanotechnology ; Distillation Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Balaji Sesha Sarath Pokuri | Sambuddha Ghosal | Apurva Kokate | Soumik Sarkar | Baskar Ganapathysubramanian
Source: npj Computational Materials (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

Interpretable deep learning for guided microstructure-property explorations in photovoltaics Abstract The microstructure determines the photovoltaic performance of a thin film organic semiconductor film. The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate, thus making microstructure optimization challenging. Here, we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural networks. We characterize this approach in terms of two critical metrics, its generalizability (has it learnt a reasonable map?), and its intepretability (can it produce meaningful microstructure characteristics that influence its prediction?). A surrogate model that exhibits these two features of generalizability and intepretability is particularly useful for subsequent design exploration. We illustrate this by using the surrogate model for both manual exploration (that verifies known domain insight) as well as automated microstructure optimization. We envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems. Computer science ; Convolutional neural network ; Property (philosophy) ; Deep learning ; Photovoltaic system ; Artificial intelligence ; Layer (electronics) ; Architecture ; Active layer ; Organic solar cell ; Connection (principal bundle) ; Computer architecture ; Computer engineering ; Materials science ; Nanotechnology ; Engineering ; Mechanical engineering ; Electrical engineering Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Advanced Neural Network Applications
Authors: Francesco Naccarato | Francesco Ricci | Jin Suntivich | Geoffroy Hautier | Ludger Wirtz | Gian-Marco Rignanese
Source: Physical Review Materials (article, 2019-04-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Searching for materials with high refractive index and wide band gap: A first-principles high-throughput study Materials combining both a high refractive index and a wide band gap are of great interest for optoelectronic and sensor applications. However, these two properties are typically described by an inverse correlation with high refractive index appearing in small gap materials and vice versa. Here, we conduct a first-principles high-throughput study on more than 4000 semiconductors (with a special focus on oxides). Our data confirm the general inverse trend between refractive index and band gap but interesting outliers are also identified. The data are then analyzed through a simple model involving two main descriptors: the average optical gap and the effective frequency. The former can be determined directly from the electronic structure of the compounds, but the latter cannot. This calls for further analysis in order to obtain a predictive model. Nonetheless, it turns out that the negative effect of a large band gap on the refractive index can be counterbalanced in two ways: (i) by limiting the difference between the direct band gap and the average optical gap which can be realized by a narrow distribution in energy of the optical transitions and (ii) by increasing the effective frequency which can be achieved through either a high number of transitions from the top of the valence band to the bottom of the conduction band or a high average probability for these transitions. Focusing on oxides, we use our data to investigate how the chemistry influences this inverse relationship and rationalize why certain classes of materials would perform better. Our findings can be used to search for new compounds in many optical applications both in the linear and nonlinear regime (waveguides, optical modulators, laser, frequency converter, etc.). Refractive index ; Band gap ; Inverse ; Semiconductor ; Direct and indirect band gaps ; Refractive index profile ; Semimetal ; Limiting Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; Crystal Structures and Properties
Authors: Yanxin Wang | Jing Yan | Qifeng Sun | Junyi Li | Zhou Yang
Source: IEEE Access (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

A MobileNets Convolutional Neural Network for GIS Partial Discharge Pattern Recognition in the Ubiquitous Power Internet of Things Context: Optimization, Comparison, and Application With the construction and promotion of the Ubiquitous Power Internet of Things (UPIoT), it is an increasingly urgent challenge to comprehensively improve the recognition accuracy of the gas-insulated switchgear (GIS) partial discharge (PD), and to incorporate the model into UPIoT intelligent terminals supported by edge computing in embedded systems. Therefore, this paper proposes a novel MobileNets convolutional neural network (MCNN) model to identify the GIS PD patterns. We first construct the PD pattern recognition classification datasets by means of experiments and FDTD simulation, and also preprocess images via binarization processing. After constructing the MCNN model, depthwise separable convolutions and an inverse residual structure are adopted to deal with the vanishing gradient of the deep convolutional neural network (DCNN) in the GIS PD pattern recognition process. Then, through the graphics standardization process, the MCNN model is trained and tested. The whole training process is visualized by Tensorboard. Compared with other deep learning models and traditional machine learning methods, MCNN particularly stands out in recognition accuracy and time consumption with a 96.5% overall recognition rate and merely 7.3 seconds in training time. This research explores how to optimize the model by improving the recognition accuracy, and by reducing its computing load, storage space and energy consumption for better incorporation into intelligent terminals in the UPIoT context. Computer science ; Convolutional neural network ; Artificial intelligence ; Context (archaeology) ; Deep learning ; Machine learning ; Artificial neural network ; Pattern recognition (psychology) ; Process (computing) ; Data pre-processing ; Data mining High voltage insulation and dielectric phenomena ; Vehicle License Plate Recognition ; Infrastructure Maintenance and Monitoring
Authors: Bincy Rose Vergis | Nagaraju Kottam | R. Hari Krishna | B.M. Nagabhushana
Source: Nano-Structures & Nano-Objects (article, 2019-03-21)
Field: Chemistry — Organic Chemistry

Removal of Evans Blue dye from aqueous solution using magnetic spinel ZnFe2O4 nanomaterial: Adsorption isotherms and kinetics Adsorption ; Freundlich equation ; Superparamagnetism ; Spinel ; Materials science ; Langmuir adsorption model ; Chemical engineering ; Langmuir ; Diffusion ; Monolayer ; Nuclear chemistry ; Analytical Chemistry (journal) ; Aqueous solution ; Nanoparticle ; Nanomaterials ; Chemistry ; Magnetization ; Chromatography ; Nanotechnology ; Physical chemistry ; Thermodynamics ; Metallurgy Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Magnetic Properties and Synthesis of Ferrites
Authors: Chen Chen | Aydın Özcan | A. Özgür Yazaydın | Bradley P. Ladewig
Source: Journal of Membrane Science (article, 2019-01-16)
Field: Chemistry — Inorganic Chemistry

Gas permeation through single-crystal ZIF-8 membranes Membrane ; Permeation ; Crystallite ; Materials science ; Selectivity ; Single crystal ; Adsorption ; Chemical engineering ; Gas separation ; Crystal (programming language) ; Metal-organic framework ; Permeability (electromagnetism) ; Porosity ; Crystallography ; Chemistry ; Physical chemistry ; Composite material ; Organic chemistry ; Catalysis ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Covalent Organic Framework Applications