jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ömer H. Omar | Tahereh Nematiaram | Alessandro Troisi | Daniele Padula
Source: Scientific Data (article, 2022-02-14)
Field: Materials Science — Materials Chemistry

Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds We present a data set of 48182 organic semiconductors, constituted of molecules that were prepared with a documented synthetic pathway and are stable in solid state. We based our search on the Cambridge Structural Database, from which we selected semiconductors with a computational funnel procedure. For each entry we provide a set of electronic properties relevant for organic materials research, and the electronic wavefunction for further calculations and/or analyses. This data set has low bias because it was not built from a set of materials designed for organic electronics, and thus it provides an excellent starting point in the search of new applications for known materials, with a great potential for novel physical insight. The data set contains molecules used as benchmarks in many fields of organic materials research, allowing to test the reliability of computational screenings for the desired application, "rediscovering" well-known molecules. This is demonstrated by a series of different applications in the field of organic materials, confirming the potential for the repurposing of known organic molecules. Repurposing ; Organic molecules ; Organic semiconductor ; Set (abstract data type) ; Computer science ; Field (mathematics) ; Organic electronics ; Nanotechnology ; Materials science ; Molecule ; Chemistry ; Engineering ; Mathematics ; Transistor ; Organic chemistry ; Electrical engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Organic and Molecular Conductors Research
Authors: Mariagrazia Olivieri | Giansergio Menduni | Marilena Giglio | Angelo Sampaolo | Pietro Patimisco | Hongpeng Wu | Lei Dong | Vincenzo Spagnolo
Source: Photoacoustics (article, 2022-12-13)
Field: Chemistry — Spectroscopy

Characterization of H2S QEPAS detection in methane-based gas leaks dispersed into environment The increase in fatal accidents and chronic illnesses caused by hydrogen sulfide (H<sub>2</sub>S) exposure occurring in various workplaces is pushing the development of sensing systems for continuous and in-field monitoring of this hazardous gas. We report here on the design and realization of a Near-IR quartz-enhanced photoacoustic sensor (QEPAS) for H<sub>2</sub>S leaks detection. H<sub>2</sub>S QEPAS signal was measured in matrixes containing up to 1 % of methane (CH<sub>4</sub>) and nitrogen (N<sub>2</sub>) which were chosen as the laboratory model environment for leakages from oil and gas wells or various industrial processes where H<sub>2</sub>S and CH<sub>4</sub> can leak simultaneously. An investigation of the influence of CH<sub>4</sub> on H<sub>2</sub>S relaxation and photoacoustic generation was proposed in this work and the sensor performances were carefully assessed with respect to CH<sub>4</sub> content in the mixture. We demonstrated the high selectivity, with no cross talk between H<sub>2</sub>S, H<sub>2</sub>O and CH<sub>4</sub> absorption lines, high sensitivity, and fast response time of the developed sensor, achieving a minimum detection limit (MDL) of 2.5 ppm for H<sub>2</sub>S with 2 s lock-in integration time. The employed 2.6 µm laser allowed us to employ the sensor also for CH<sub>4</sub> detection, achieving an MDL of 85 ppm. The realized QEPAS sensor lends itself to the development of a portable and compact device for industrial monitoring. Methane ; Characterization (materials science) ; Gas analysis ; Environmental science ; Materials science ; Waste management ; Nuclear engineering ; Engineering ; Nanotechnology ; Chemistry Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: Andrea C. Kneuttinger
Source: Biological Chemistry (review, 2022-03-31)
Field: Materials Science — Materials Chemistry

A guide to designing photocontrol in proteins: methods, strategies and applications Light is essential for various biochemical processes in all domains of life. In its presence certain proteins inside a cell are excited, which either stimulates or inhibits subsequent cellular processes. The artificial photocontrol of specifically proteins is of growing interest for the investigation of scientific questions on the organismal, cellular and molecular level as well as for the development of medicinal drugs or biocatalytic tools. For the targeted design of photocontrol in proteins, three major methods have been developed over the last decades, which employ either chemical engineering of small-molecule photosensitive effectors (photopharmacology), incorporation of photoactive non-canonical amino acids by genetic code expansion (photoxenoprotein engineering), or fusion with photoreactive biological modules (hybrid protein optogenetics). This review compares the different methods as well as their strategies and current applications for the light-regulation of proteins and provides background information useful for the implementation of each technique. Genetic code ; Effector ; Computational biology ; Protein engineering ; Chemistry ; Fusion protein ; Amino acid ; Optogenetics ; Nanotechnology ; Biochemical engineering ; Computer science ; Biochemistry ; Biology ; Materials science ; Gene ; Enzyme ; Neuroscience Photochromic and Fluorescence Chemistry ; bioluminescence and chemiluminescence research ; Photoreceptor and optogenetics research
Authors: Runming Tao | Tao Wang | Juntian Fan | Harry M. Meyer | Albina Y. Borisevich | Chi‐Linh Do‐Thanh | Sheng Dai
Source: ChemNanoMat (article, 2022-02-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ionothermal Synthesis of Carbon/TiO<sub>2</sub> Nanocomposite for Supercapacitors Abstract A facile in‐situ ionothermal strategy is designed to synthesize carbon/oxide composites with nanoporous structure. Ionic liquids play key roles in this ionothermal method as the solvents, porous structure directing templates and carbon sources. The interaction between ionic liquids and oxides greatly improves the carbon yield of ionic liquids from nearly 0 to 18 wt.%. With the synergistic effects of the high electrical conductivity of the nitrogen‐rich carbon species and the Ti 3+ self‐doping enhanced electrical conductivity of TiO 2 , C/TiO 2 ‐4 composite exhibits better electrochemical performance than C/SiO 2 and C/Nb 2 O 5 do as a supercapacitor electrode material, delivering a high specific capacitance of 152.4 F g −1 at 5 mV s −1 with good capacitance retentions of 84.7%, 72.2% and 46.0% at 30, 100 and 500 mV s −1 , respectively. Ionic liquid ; Supercapacitor ; Materials science ; Nanocomposite ; Nanoporous ; Carbon fibers ; Electrochemistry ; Capacitance ; Chemical engineering ; Composite number ; Oxide ; Conductivity ; Electrode ; Inorganic chemistry ; Nanotechnology ; Composite material ; Chemistry ; Catalysis ; Organic chemistry ; Physical chemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced battery technologies research
Authors: Martina Crippa | Claudio Perego | Anna Lucia de Marco | Giovanni M. Pavan
Source: Nature Communications (article, 2022-04-20)
Field: Materials Science — Biomaterials

Molecular communications in complex systems of dynamic supramolecular polymers Supramolecular polymers are composed of monomers that self-assemble non-covalently, generating distributions of monodimensional fibres in continuous communication with each other and with the surrounding solution. Fibres, exchanging molecular species, and external environment constitute a sole complex system, which intrinsic dynamics is hard to elucidate. Here we report coarse-grained molecular simulations that allow studying supramolecular polymers at the thermodynamic equilibrium, explicitly showing the complex nature of these systems, which are composed of exquisitely dynamic molecular entities. Detailed studies of molecular exchange provide insights into key factors controlling how assemblies communicate with each other, defining the equilibrium dynamics of the system. Using minimalistic and finer chemically relevant molecular models, we observe that a rich concerted complexity is intrinsic in such self-assembling systems. This offers a new dynamic and probabilistic (rather than structural) picture of supramolecular polymer systems, where the travelling molecular species continuously shape the assemblies that statistically emerge at the equilibrium. Supramolecular chemistry ; Supramolecular polymers ; Polymer ; Molecular dynamics ; Monomer ; Probabilistic logic ; Nanotechnology ; Complex system ; Molecule ; Chemical physics ; Materials science ; Biological system ; Computer science ; Chemistry ; Computational chemistry ; Biology Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Lipid Membrane Structure and Behavior
Authors: Rui Shi | Peichen Pan | Rui Lv | Chongqing Ma | Enhui Wu | Ruochen Guo | Zhihao Zhao | Hexing Song | Joe Z. Zhou | Liu Yang | Guoqiang Xu | Tingjun Hou | Zhenhui Kang | Jian Liu
Source: Science Advances (article, 2022-02-16)
Field: Chemistry — Spectroscopy

High-throughput glycolytic inhibitor discovery targeting glioblastoma by graphite dots–assisted LDI mass spectrometry Malignant tumors will become vulnerable if their uncontrolled biosynthesis and energy consumption engaged in metabolic reprogramming can be cut off. Here, we report finding a glycolytic inhibitor targeting glioblastoma with graphite dots-assisted laser desorption/ionization mass spectrometry as an integrated drug screening and pharmacokinetic platform (GLMSD). We have performed high-throughput virtual screening to narrow an initial library of 240,000 compounds down to the docking of 40 compounds and identified five previously unknown chemical scaffolds as promising hexokinase-2 inhibitors. The best inhibitor (Compd 27) can regulate the reprogrammed metabolic pathway in U87 glioma cells (median inhibitory concentration ~ 11.3 μM) for tumor suppression. Highly effective therapy against glioblastoma has been demonstrated in both subcutaneous and orthotopic brain tumors by synergizing Compd 27 and temozolomide. Our glycolytic inhibitor discovery can inspire personalized medicine targeting reprogrammed metabolisms of malignant tumors. GLMSD enables large, high-quality data for next-generation artificial intelligence-aided drug development. Glioblastoma ; Throughput ; Mass spectrometry ; Glycolysis ; Graphite ; Computational biology ; Nanotechnology ; Chemistry ; Cancer research ; Materials science ; Computer science ; Biology ; Biochemistry ; Chromatography ; Metabolism Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Machine Learning in Materials Science
Authors: Ang Feng | Jonas Joos | Jiaren Du | Philippe F. Smet
Source: Physical review. B./Physical review. B (article, 2022-05-03)
Field: Materials Science — Materials Chemistry

Revealing trap depth distributions in persistent phosphors with a thermal barrier for charging The performance of persistent phosphors under given charging and working conditions is determined by the properties of the traps that are responsible for these unique properties. Traps are characterized by the height of their associated barrier for thermal detrapping, and a continuous distribution of trap depths is often found in real materials. Accurately determining trap depth distributions is hence of importance for the understanding and development of persistent phosphors. However, extracting the trap depth distribution is often hindered by the presence of a thermal barrier for charging as well, which causes a temperature-dependent filling of traps. For this case, we propose a method for extracting the trap depth distribution from a set of thermoluminescence glow curves obtained at different charging temperatures. The glow curves are first transformed into electron population functions via the Tikhonov regularization, assuming first-order kinetics. Subsequently, the occupation of the traps as a function of their depth, quantified by the so-called filling function, is obtained. Finally, the underlying trap depth distribution is reconstructed from the filling functions. The proposed method provides a substantial improvement in precision and resolution for the trap depth distribution compared with existing methods. This is hence a step forward in understanding the (de)trapping behavior of persistent and storage phosphors. Thermoluminescence ; Trap (plumbing) ; Trapping ; Phosphor ; Thermal ; Tikhonov regularization ; Population ; Distribution function ; Computational physics ; Materials science ; Atomic physics ; Optoelectronics ; Physics ; Luminescence ; Thermodynamics ; Mathematics Luminescence Properties of Advanced Materials ; Advanced Chemical Physics Studies ; Radiation Detection and Scintillator Technologies
Authors: Jingru Lu | Irina Paci | David C. Leitch
Source: Chemical Science (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (S <sub>N</sub> Ar) using simple descriptors We report a multivariate linear regression model able to make accurate predictions for the relative rate and regioselectivity of nucleophilic aromatic substitution (S<sub>N</sub>Ar) reactions based on the electrophile structure. This model uses a diverse training/test set from experimentally-determined relative S<sub>N</sub>Ar rates between benzyl alcohol and 74 unique electrophiles, including heterocycles with multiple substitution patterns. There is a robust linear relationship between the experimental S<sub>N</sub>Ar free energies of activation and three molecular descriptors that can be obtained computationally: the electron affinity (EA) of the electrophile; the average molecular electrostatic potential (ESP) at the carbon undergoing substitution; and the sum of average ESP values for the <i>ortho</i> and <i>para</i> atoms relative to the reactive center. Despite using only simple descriptors calculated from ground state wavefunctions, this model demonstrates excellent correlation with previously measured S<sub>N</sub>Ar reaction rates, and is able to accurately predict site selectivity for multihalogenated substrates: 91% prediction accuracy across 82 individual examples. The excellent agreement between predicted and experimental outcomes makes this easy-to-implement reactivity model a potentially powerful tool for synthetic planning. Reactivity (psychology) ; Chemistry ; Nucleophilic substitution ; Electrophile ; Nucleophile ; Selectivity ; Simple (philosophy) ; Computational chemistry ; Substitution (logic) ; Nucleophilic aromatic substitution ; Substitution reaction ; Organic chemistry ; Computer science Chemical Reaction Mechanisms ; Organic Chemistry Cycloaddition Reactions ; DNA and Nucleic Acid Chemistry
Authors: Anatoly P. Sobolev | Cinzia Ingallina | Mattia Spano | Giacomo Di Matteo | Luisa Mannina
Source: Molecules (review, 2022-11-15)
Field: Chemistry — Analytical Chemistry

NMR-Based Approaches in the Study of Foods In this review, the three different NMR-based approaches usually used to study foodstuffs are described, reporting specific examples. The first approach starts with the food of interest that can be investigated using different complementary NMR methodologies to obtain a comprehensive picture of food composition and structure; another approach starts with the specific problem related to a given food (frauds, safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc.) that can be addressed by choosing the most suitable NMR methodology; finally, it is possible to start from a single NMR methodology, developing a broad range of applications to tackle common food-related challenges and different aspects related to foods. Traceability ; Computer science ; Range (aeronautics) ; Biochemical engineering ; Data science ; Biotechnology ; Engineering ; Biology ; Software engineering Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Shobhit K. Patel | Juveriya Parmar | Vijay Katkar
Source: Scientific Reports (article, 2022-06-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultra-broadband, wide-angle plus-shape slotted metamaterial solar absorber design with absorption forecasting using machine learning Energy utilization is increasing day by day and there is a need for highly efficient renewable energy sources. Solar absorbers with high efficiency can be used to meet these growing energy demands by transforming solar energy into thermal energy. Solar absorber design with highly efficient and Ultra-broadband response covering visible, ultraviolet, and near-infrared spectrum is proposed in this paper. The absorption response is observed for three metamaterial designs (plus-shape slotted design, plus-shape design, and square-shape design) and one optimized design is used for solar absorber design based on its high efficiency. The design results are compared with AM 1.5 spectral irradiance response. The electric field response of the plus-shape slotted metamaterial design is also presented which matches well with the absorption results of different solar spectrum regions. The results proved that the attained absorption response showing wide angle of incidence. Machine learning is also used to examine the design data in order to forecast absorption for various substrate thickness, metasurface thickness, and incidence angles. Regression and forecasting simulations based on machine learning are used to try to anticipate absorber behaviour at forthcoming and intermediate wavelengths. Simulation results prove that Machine Learning based methods can lessen the obligatory simulation resources, time and can be used as an effective tool while designing the absorber. The proposed highly efficient, wide-angle, ultra-broadband solar absorber design with its behavior prediction capability using machine learning can be utilized for solar thermal energy harvesting applications. Absorption (acoustics) ; Metamaterial ; Optics ; Computer science ; Broadband ; Solar energy ; Materials science ; Renewable energy ; Optoelectronics ; Physics ; Engineering ; Electrical engineering Metamaterials and Metasurfaces Applications ; Energy Harvesting in Wireless Networks ; Thermal Radiation and Cooling Technologies
Authors: Hansu Zhang | Linsheng Huang | Wenjiang Huang | Yingying Dong | Shizhuang Weng | Jinling Zhao | Huiqin Ma | Linyi Liu
Source: Frontiers in Plant Science (article, 2022-09-21)
Field: Chemistry — Analytical Chemistry

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion Infection caused by <i>Fusarium</i> head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat. Artificial intelligence ; Hyperspectral imaging ; Feature (linguistics) ; Computer science ; Pattern recognition (psychology) ; Spectral signature ; Texture (cosmology) ; Spectral bands ; Random forest ; Support vector machine ; Computer vision ; Remote sensing ; Image (mathematics) ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Lei Xu | Daria A. Smirnova | Rocio Camacho‐Morales | Rifat Ahmmed Aoni | Khosro Zangeneh Kamali | Marcus Cai | Cuifeng Ying | Ze Zheng | Andrey E. Miroshnichenko | Dragomir N. Neshev | Mohsen Rahmani
Source: New Journal of Physics (article, 2022-02-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhanced four-wave mixing from multi-resonant silicon dimer-hole membrane metasurfaces Abstract Tailoring optically resonant features in dielectric metasurfaces unveils a robust scheme to control electromagnetic near fields of light and thus to boost the nanoscale nonlinear light–matter interactions. Membrane metasurfaces offer unique possibilities for supporting multipolar resonances and meanwhile maintaining high mode volume for enhancing nonlinear frequency conversion. Here we design a silicon membrane metasurface consisting of dimer airy holes, as a versatile platform for generating four-wave mixing (FWM). We show that such a metasurface exhibits a multi-resonant feature, including a quasi bound state in the continuum (BIC) generated by the collective toroidal dipole mode excited in the designed subdiffractive periodic system. We show that via employing the BIC mode in the short-wave infrared (SWIR), together with other resonant enhanced electric near fields in the near-infrared (NIR) region, simultaneously, one can convert invisible SWIR light to visible light radiation with high efficiency, via FWM. We experimentally demonstrated a significant FWM emission enhancement from our metasurface, which leads to a conversion efficiency of 0.76 × 10 −6 using pump and signal beam peak intensities as low as 0.33 GW cm −2 and 0.17 GW cm −2 , respectively. Our results open new routes for enhancing nonlinear efficiencies for up-conversion processes. Physics ; Excited state ; Energy conversion efficiency ; Four-wave mixing ; Optics ; Silicon ; Optoelectronics ; Dipole ; Mixing (physics) ; Infrared ; Nonlinear optics ; Atomic physics ; Laser Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics