jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gisela Cabré | Aida Garrido‐Charles | Miquel Moreno | Miquel Bosch | Montserrat Porta-de-la-Riva | Michael Krieg | Marta Gascón-Moya | Núria Camarero | Ricard Gelabert | José M. Lluch | Félix Busquè | Jordi Hernando | Pau Gorostiza | Ramón Alibés
Source: Nature Communications (article, 2019-02-22)
Field: Materials Science — Materials Chemistry

Rationally designed azobenzene photoswitches for efficient two-photon neuronal excitation Manipulation of neuronal activity using two-photon excitation of azobenzene photoswitches with near-infrared light has been recently demonstrated, but their practical use in neuronal tissue to photostimulate individual neurons with three-dimensional precision has been hampered by firstly, the low efficacy and reliability of NIR-induced azobenzene photoisomerization compared to one-photon excitation, and secondly, the short cis state lifetime of the two-photon responsive azo switches. Here we report the rational design based on theoretical calculations and the synthesis of azobenzene photoswitches endowed with both high two-photon absorption cross section and slow thermal back-isomerization. These compounds provide optimized and sustained two-photon neuronal stimulation both in light-scattering brain tissue and in Caenorhabditis elegans nematodes, displaying photoresponse intensities that are comparable to those achieved under one-photon excitation. This finding opens the way to use both genetically targeted and pharmacologically selective azobenzene photoswitches to dissect intact neuronal circuits in three dimensions. Azobenzene ; Photoisomerization ; Excitation ; Two-photon excitation microscopy ; Photoswitch ; Photon ; Isomerization ; Materials science ; Optoelectronics ; Biophysics ; Chemistry ; Photochemistry ; Physics ; Optics ; Molecule ; Biology Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Retinal Development and Disorders
Authors: Mojtaba Falahati | Farnoosh Attar | Majid Sharifi | Ali Akbar Saboury | Abbas Salihi | Falah Mohammad Aziz | Irena Kostova | Clemens Burda | Peter Priecel | José Antonio López-Sánchez | Sophie Laurent | Nasrin Hooshmand | Mostafa A. El‐Sayed
Source: Biochimica et Biophysica Acta (BBA) - General Subjects (review, 2019-09-14)
Field: Materials Science — Materials Chemistry

Gold nanomaterials as key suppliers in biological and chemical sensing, catalysis, and medicine Nanotechnology ; Nanomaterials ; Colloidal gold ; Photothermal therapy ; Cancer therapy ; Materials science ; Catalysis ; Biochemical engineering ; Computer science ; Nanoparticle ; Chemistry ; Cancer ; Medicine ; Engineering Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Sauceda, Huziel | Chmiela, Stefan | Poltavsky, Igor | Müller, Klaus-Robert | Tkatchenko, Alexandre
Source: Open Repository and Bibliography (University of Luxembourg) (article, 2019-03-18)
Field: Materials Science — Materials Chemistry

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al., Nat. Commun. 9, 3887 (2018) and Chmiela et al., Sci. Adv. 3, e1603015 (2017)]. This approach is able to accurately reconstruct complex high-dimensional potential-energy surfaces from just a few 100s of molecular conformations extracted from ab initio molecular dynamics trajectories. The data efficiency of the sGDML approach implies that atomic forces for these conformations can be computed with high-level wavefunction-based approaches, such as the “gold standard” coupled-cluster theory with single, double and perturbative triple excitations [CCSD(T)]. We demonstrate that the flexible nature of the sGDML model recovers local and non-local electronic interactions (e.g., H-bonding, proton transfer, lone pairs, changes in hybridization states, steric repulsion, and n → π* interactions) without imposing any restriction on the nature of interatomic potentials. The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy. Molecular dynamics ; Coupled cluster ; Cluster (spacecraft) ; Ab initio ; Wave function ; Lone pair ; Chemistry ; Molecule ; Physics ; Electronic structure ; Steric effects ; Force field (fiction) ; Domain (mathematical analysis) ; Chemical physics ; Molecular physics ; Computational chemistry ; Atomic physics ; Quantum mechanics ; Computer science ; Stereochemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Ding Zhang | Yuanhao Wang | Ya Yang
Source: Small (review, 2019-02-18)
Field: Materials Science — Materials Chemistry

Design, Performance, and Application of Thermoelectric Nanogenerators Thermal energy harvesting from the ambient environment through thermoelectric nanogenerators (TEGs) is an ideal way to realize self-powered operation of electronics, and even relieve the energy crisis and environmental degradation. As one of the most significant energy-related technologies, TEGs have exhibited excellent thermoelectric performance and played an increasingly important role in harvesting and converting heat into electric energy, gradually becoming one of the hot research fields. Here, the development of TEGs including materials optimization, structural designs, and potential applications, even the opportunities, challenges, and the future development direction, is analyzed and summarized. Materials optimization and structural designs of flexibility for potential applications in wearable electronics are systematically discussed. With the development of flexible and wearable electronic equipment, flexible TEGs show increasingly great application prospects in artificial intelligence, self-powered sensing systems, and other fields in the future. Electronics ; Energy harvesting ; Flexibility (engineering) ; Thermoelectric effect ; Wearable computer ; Materials science ; Wearable technology ; Thermoelectric materials ; Thermoelectric generator ; Nanotechnology ; Computer science ; Mechanical engineering ; Energy (signal processing) ; Electrical engineering ; Embedded system ; Engineering Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Thermal Radiation and Cooling Technologies
Authors: Gabriel R. Schleder | Carlos Mera Acosta | A. Fazzio
Source: ACS Applied Materials & Interfaces (article, 2019-11-06)
Field: Materials Science — Materials Chemistry

Exploring Two-Dimensional Materials Thermodynamic Stability via Machine Learning The increasing interest and research on two-dimensional (2D) materials has not yet translated into a reality of diverse materials applications. To go beyond graphene and transition metal dichalcogenides for several applications, suitable candidates with desirable properties must be proposed. Here we use machine learning techniques to identify thermodynamically stable 2D materials, which is the first essential requirement for any application. According to the formation energy and energy above the convex hull, we classify materials as having low, medium, or high stability. The proposed approach enables the stability evaluation of novel 2D compounds for further detailed investigation of promising candidates, using only composition properties and structural symmetry, without the need for information about atomic positions. We demonstrate the usefulness of the model generating more than a thousand novel compounds, corroborating with DFT calculations the classification for five of these materials. To illustrate the applicability of the stable materials, we then perform a screening of electronic materials suitable for photoelectrocatalytic water splitting, identifying the potential candidate Sn<sub>2</sub>SeTe generated by our model, and also PbTe, both not yet reported for this application. Materials science ; Stability (learning theory) ; Graphene ; Chemical stability ; Nanotechnology ; Convex hull ; Computer science ; Machine learning ; Regular polygon ; Thermodynamics ; Physics 2D Materials and Applications ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Jon Paul Janet | Fang Liu | Aditya Nandy | Chenru Duan | Tzuhsiung Yang | Sean Lin | Heather J. Kulik
Source: Inorganic Chemistry (article, 2019-03-05)
Field: Materials Science — Materials Chemistry

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition-metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy. We outline five key mandates for realizing computationally driven accelerated discovery in inorganic chemistry: (i) fully automated simulation of new compounds, (ii) knowledge of prediction sensitivity or accuracy, (iii) faster-than-fast property prediction methods, (iv) maps for rapid chemical space traversal, and (v) a means to reveal design rules on the kilocompound scale. Through case studies in open-shell transition-metal chemistry, we describe how advances in methodology and software in each of these areas bring about new chemical insights. We conclude with our outlook on the next steps in this process toward realizing fully autonomous discovery in inorganic chemistry using computational chemistry. Chemical space ; Chemistry ; Nanotechnology ; Computer science ; Drug discovery ; Biochemical engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Han Liu | Zipeng Fu | Kai Yang | Xinyi Xu | Mathieu Bauchy
Source: Journal of Non-Crystalline Solids (review, 2019-07-29)
Field: Materials Science — Ceramics and Composites

Machine learning for glass science and engineering: A review Science and engineering ; Mathematics education ; Computer science ; Engineering physics ; Engineering ethics ; Engineering ; Psychology Glass properties and applications ; Cultural Heritage Materials Analysis ; Phase-change materials and chalcogenides
Authors: Rafael Vargas-Bernal
Source: Sensors (review, 2019-03-14)
Field: Materials Science — Materials Chemistry

Electrical Properties of Two-Dimensional Materials Used in Gas Sensors In the search for gas sensing materials, two-dimensional materials offer the possibility of designing sensors capable of tuning the electronic band structure by controlling their thickness, quantity of dopants, alloying between different materials, vertical stacking, and the presence of gases. Through materials engineering it is feasible to study the electrical properties of two-dimensional materials which are directly related to their crystalline structure, first Brillouin zone, and dispersion energy, the latter estimated through the tight-binding model. A review of the electrical properties directly related to the crystalline structure of these materials is made in this article for the two-dimensional materials used in the design of gas sensors. It was found that most 2D sensing materials have a hexagonal crystalline structure, although some materials have monoclinic, orthorhombic and triclinic structures. Through the simulation of the mathematical models of the dispersion energy, two-dimensional and three-dimensional electronic band structures were predicted for graphene, hexagonal boron nitride (<i>h</i>-BN) and silicene, which must be known before designing a gas sensor. Materials science ; Orthorhombic crystal system ; Brillouin zone ; Monoclinic crystal system ; Graphene ; Electronic band structure ; Stacking ; Boron nitride ; Dopant ; Nanotechnology ; Crystal structure ; Optoelectronics ; Crystallography ; Condensed matter physics ; Doping ; Chemistry Graphene research and applications ; 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Cheera Prasad | Hua Tang | Qinqin Liu | Syed Zulfiqar | Sufaid Shah | Indra Bahadur
Source: Journal of Molecular Liquids (article, 2019-06-06)
Field: Materials Science — Materials Chemistry

An overview of semiconductors/layered double hydroxides composites: Properties, synthesis, photocatalytic and photoelectrochemical applications Layered double hydroxides ; Photocatalysis ; Semiconductor ; Materials science ; Composite material ; Photoelectrochemistry ; Catalysis ; Chemistry ; Optoelectronics ; Electrochemistry ; Electrode ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Advanced Photocatalysis Techniques ; Polyoxometalates: Synthesis and Applications
Authors: Liming Bai | Cristina García Elósegui | Weiqi Li | Ping Yu | Junjie Fei | Lanqun Mao
Source: Frontiers in Chemistry (review, 2019-05-07)
Field: Materials Science — Polymers and Plastics

Biological Applications of Organic Electrochemical Transistors: Electrochemical Biosensors and Electrophysiology Recording Organic electrochemical transistors (OECTs) are recently developed high-efficient transducers not only for electrochemical biosensor but also for cell electrophysiological recording due to the separation of gate electrode from the transistor device. The efficient integration of OECTs with electrochemical gate electrode makes the as-prepared sensors with improved performance, such as sensitivity, limit of detection, and selectivity. We herein reviewed the recent progress of OECTs-based biosensors and cell electrophysiology recording, mainly focusing on the principle and chemical design of gate electrode and the channel. First, the configuration, work principle, semiconductor of OECT are briefly introduced. Then different kinds of sensing modes are reviewed, especially for the biosensing and electrophysiological recording. Finally, the challenges and opportunities of this research field are discussed. Biosensor ; Nanotechnology ; Transistor ; Electrophysiology ; Electrode ; Materials science ; Electrochemistry ; Field-effect transistor ; Transducer ; Optoelectronics ; Voltage ; Chemistry ; Electrical engineering ; Neuroscience ; Engineering ; Biology Conducting polymers and applications ; Advanced Memory and Neural Computing ; Electrochemical Analysis and Applications
Authors: Alessandro Parodi | Jiaxing Miao | Surinder M. Soond | Magdalena Rudzińska | Andrey A. Zamyatnin
Source: Biomolecules (review, 2019-06-05)
Field: Materials Science — Biomaterials

Albumin Nanovectors in Cancer Therapy and Imaging Albumin nanovectors represent one of the most promising carriers recently generated because of the cost-effectiveness of their fabrication, biocompatibility, safety, and versatility in delivering hydrophilic and hydrophobic therapeutics and diagnostic agents. In this review, we describe and discuss the recent advances in how this technology has been harnessed for drug delivery in cancer, evaluating the commonly used synthesis protocols and considering the key factors that determine the biological transport and the effectiveness of such technology. With this in mind, we highlight how clinical and experimental albumin-based delivery nanoplatforms may be designed for tackling tumor progression or improving the currently established diagnostic procedures. Drug delivery ; Biocompatibility ; Cancer therapy ; Nanotechnology ; Cancer ; Medical physics ; Medicine ; Computer science ; Materials science Nanoparticle-Based Drug Delivery ; Protein Interaction Studies and Fluorescence Analysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Hai Anh Le Phuong | Nor Amira Izzati Ayob | Christopher F. Blanford | Nurul Fazita Mohammad Rawi | György Székely
Source: ACS Sustainable Chemistry & Engineering (article, 2019-06-11)
Field: Materials Science — Biomaterials

Nonwoven Membrane Supports from Renewable Resources: Bamboo Fiber Reinforced Poly(Lactic Acid) Composites Membrane separation has been widely recognized as an energy-efficient technology with a rapidly growing market. However, such growth raises concerns about sustainability due to current fabrication methods that employ toxic solvents and nondegradable petroleum-based polymers. The focus of tackling these challenges has been on the active layer of membranes via renewable materials, while the equally important membrane supports are yet to turn green. Herein we report the fabrication of sustainable, biodegradable, nonwoven composite membrane supports made from three renewable materials: bamboo fiber, poly(lactic acid) (PLA), and dimethyl carbonate. The biobased membrane supports exhibited a porous structure (porosity of 0.719 ± 0.132) with tensile strengths (32.7-73.3 MPa) comparable to conventional materials, such as polypropylene. The microstructure and porosity of the supports were revealed by laser scanning confocal microscopy. The increase in bamboo content resulted in increased mechanical stability, decreased swelling, and enhanced permeance, up to 1068 ± 32 L m-2 h-1 bar-1 in water. The long-term chemical stability of membrane supports was verified in 19 of the 25 organic solvents screened. In particular, they were found to be stable in some conventional and emerging green polar aprotic solvents including Cyrene, 2-methyltetrahydrofuran, ?-valerolactone, and propylene carbonate. Stable cross-flow filtration performance over 2 weeks was successfully demonstrated. The results demonstrated that the bamboo/PLA membrane supports could provide a sustainable alternative for conventional membrane backing materials by eliminating the need for petroleum-based nondegradable polymers and toxic solvents. Membrane ; Materials science ; Chemical engineering ; Ultimate tensile strength ; Polymer ; Fiber ; Porosity ; Composite material ; Chemistry biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Polymer crystallization and properties
Authors: Musen Zhou | Alejandro Gallegos | Kun Liu | Sheng Dai | Jianzhong Wu
Source: Carbon (article, 2019-10-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Insights from machine learning of carbon electrodes for electric double layer capacitors Capacitor ; Limiting ; Materials science ; Electrode ; Supercapacitor ; Carbon fibers ; Porosity ; Energy storage ; Power density ; Electrochemistry ; Nanotechnology ; Layer (electronics) ; Computer science ; Power (physics) ; Engineering physics ; Electrical engineering ; Mechanical engineering ; Composite material ; Engineering ; Chemistry ; Voltage Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advancements in Battery Materials
Authors: Oleksandr I. Malyi | Kostiantyn V. Sopiha | Clas Persson
Source: ACS Applied Materials & Interfaces (article, 2019-04-15)
Field: Materials Science — Materials Chemistry

Energy, Phonon, and Dynamic Stability Criteria of Two-Dimensional Materials First-principles calculations have become a powerful tool to exclude the Edisonian approach in search of novel two-dimensional (2D) materials. However, no universal first-principles criteria to examine the realizability of hypothetical 2D materials have been established in the literature yet. Because of this, and as the calculations are always performed in an artificial simulation environment, one can unintentionally study compounds that do not exist in experiments. Although investigations of physics and chemistry of unrealizable materials can provide some fundamental knowledge, the discussion of their applications can mislead experimentalists for years and increase the gap between experimental and theoretical research. By analyzing energy convex hull, phonon spectra, and structure evolution during ab initio molecular dynamics simulations for a range of synthesized and recently proposed 2D materials, we construct energy, phonon, and dynamic stability filters that need to be satisfied before proposing novel 2D compounds. We demonstrate the power of the suggested filters for several selected 2D systems, revealing that some of them cannot be ever realized experimentally. Realizability ; Phonon ; Stability (learning theory) ; Statistical physics ; Molecular dynamics ; Range (aeronautics) ; Energy (signal processing) ; Computer science ; Ab initio ; Materials science ; Theoretical physics ; Physics ; Quantum mechanics ; Algorithm 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; MXene and MAX Phase Materials
Authors: Milica Todorović | Michael U. Gutmann | Jukka Corander | Patrick Rinke
Source: npj Computational Materials (article, 2019-03-18)
Field: Materials Science — Materials Chemistry

Bayesian inference of atomistic structure in functional materials Abstract Tailoring the functional properties of advanced organic/inorganic heterogeneous devices to their intended technological applications requires knowledge and control of the microscopic structure inside the device. Atomistic quantum mechanical simulation methods deliver accurate energies and properties for individual configurations, however, finding the most favourable configurations remains computationally prohibitive. We propose a ‘building block’-based Bayesian Optimisation Structure Search (BOSS) approach for addressing extended organic/inorganic interface problems and demonstrate its feasibility in a molecular surface adsorption study. In BOSS, a Bayesian model identifies material energy landscapes in an accelerated fashion from atomistic configurations sampled during active learning. This allowed us to identify several most favourable molecular adsorption configurations for C 60 on the (101) surface of TiO 2 anatase and clarify the key molecule-surface interactions governing structural assembly. Inferred structures were in good agreement with detailed experimental images of this surface adsorbate, demonstrating good predictive power of BOSS and opening the route towards large-scale surface adsorption studies of molecular aggregates and films. Bayesian optimization ; Molecular dynamics ; Inference ; Bayesian inference ; Surface (topology) ; Bayesian probability ; Interface (matter) Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Advanced Chemical Physics Studies