jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Meerambika Behera | Nitika Tiwari | Shirsendu Banerjee | Abdul Rauf Sheik | Manish Kumar | Madhubanti Pal | Parimal Pal | Rishya Prava Chatterjee | Sankha Chakrabortty | Suraj K. Tripathy
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2022-02-22)
Field: Chemistry — Organic Chemistry

Ag/biochar nanocomposites demonstrate remarkable catalytic activity towards reduction of p-nitrophenol via restricted agglomeration and leaching characteristics Biochar ; Sodium borohydride ; 4-Nitrophenol ; Leaching (pedology) ; Catalysis ; Nanocomposite ; Chemical engineering ; Nitrophenol ; Materials science ; Nuclear chemistry ; Nanoparticle ; Economies of agglomeration ; Chemistry ; Nanotechnology ; Organic chemistry ; Pyrolysis ; Soil science ; Environmental science Nanomaterials for catalytic reactions ; Copper-based nanomaterials and applications ; Graphene research and applications
Authors: Junyi Gong | Peifa Wei | Junkai Liu | Yuncong Chen | Zheng Zhao | Weijun Zhao | Huilin Xie | Chao Ma | Jacky W. Y. Lam | Kam Sing Wong | Ying Li | Ben Zhong Tang
Source: Aggregate (article, 2022-09-08)
Field: Materials Science — Materials Chemistry

Polarity‐triggered anti‐Kasha system for high‐contrast cell imaging and classification Abstract Kasha's rule, which states that all exciton emissions occur from the lowest excited state and are independent of excitation energy, makes high‐energy excitons difficult to use and severely hinders the widespread applications of organic photoluminescent materials in the real world. For decades, scientists have tried to break this rule to unleash the power of high‐energy excitons, but only minimal progress has been achieved, with no rational guiding principles provided, and few applications developed. So far, breaking Kasha's rule has remained a purely academic concept. In this paper, we introduce a design principle for a purely organic anti‐Kasha system and synthesise a series of compounds based on the design rule. As predicted, these compounds all display evident S 2 emissions in dilute solutions. In addition, we introduce a highly accurate (over 90%) convolutional neural network as an assistant for the classification of cells using anti‐Kasha luminogens, thereby providing a new application direction for anti‐Kasha systems. Exciton ; Excited state ; Excitation ; Nanotechnology ; Physics ; Computer science ; Chemistry ; Theoretical physics ; Materials science ; Quantum mechanics Luminescence and Fluorescent Materials ; Photochromic and Fluorescence Chemistry ; Photochemistry and Electron Transfer Studies
Authors: Cunxing Lu | Xili Wan | Xuhao Ma | Xinjie Guan | Aichun Zhu
Source: Journal of Chemical Information and Modeling (review, 2022-05-16)
Field: Chemistry — Inorganic Chemistry

Deep-Learning-Based End-to-End Predictions of CO<sub>2</sub> Capture in Metal–Organic Frameworks Metal-organic frameworks (MOFs) have become an active topic because of their excellent carbon capture and storage (CCS) properties. However, it is quite challenging to identify MOFs with superior performance within a massive combinatorial search space. To this end, we propose a deep-learning-based end-to-end prediction model to rapidly and accurately predict the CO<sub>2</sub> working capacity and CO<sub>2</sub>/N<sub>2</sub> selectivity of a given MOF under low-pressure conditions. Different from previous methods, our prediction model relies only on the data from the Crystallographic Information File (CIF) rather than handcrafted geometric descriptors and chemical descriptors. The model was developed, trained, and tested on a dataset of 342489 topologically diverse MOFs. Experimental results on the dataset show that the proposed model achieves high prediction performance, i.e., <i>R</i><sup>2</sup> = 0.916 for predicting the CO<sub>2</sub> working capacity and <i>R</i><sup>2</sup> = 0.911 for predicting the CO<sub>2</sub>/N<sub>2</sub> selectivity. With regard to the identification of potential high-performing MOFs, 1020 of 1027 (top 3%) high-performance MOFs were recovered while screening only 12% of the entire dataset using our provided pretrained model, reducing the computation time by nearly an order of magnitude when the model was used to prescreen material prior to computationally intensive grand canonical Monte Carlo (GCMC) simulations while still capturing 99% of the high-performance MOFs. In the ab initio training task, the method can achieve <i>R</i><sup>2</sup> = 0.85 with only 20% of the labeled data used for training and recover 995 of 1027 (top 3%) high-performance MOFs with only 12% of the entire dataset screened. Computer science ; Deep learning ; Chemical space ; Computation ; Artificial intelligence ; Selectivity ; Metal-organic framework ; Task (project management) ; Machine learning ; Data mining ; Algorithm ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Covalent Organic Framework Applications
Authors: K. Balasubramanian
Source: Journal of Physical Organic Chemistry (article, 2022-09-15)
Field: Chemistry — Organic Chemistry

Density functional and graph theory computations of vibrational, electronic, and topological properties of porous nanographenes Abstract We have utilized the density functional theory (DFT) in conjunction with graph‐theoretical techniques to compute the vibrational, electronic and topological properties of porous nanographenes starting with the building blocks of kekulene, septulene, extended kekulenes, and circumkekulene. Furthermore, graph theoretically based spectral polynomials and other topological properties including Kekulé counts, delocalization energies, and resonance energies are computed for such structures and larger tessellations of kekulenes which are precursors to nanographene belts with multiple pores. The success of the DFT methods is demonstrated with the computed vibrational modes and infrared and Raman spectra of several of these structures. The computed spectral polynomials and the spectra reveal the underlying patterns of the energy levels and structural features and hence suggest the possibility of integration of graph theory with quantum chemical techniques for the computations of properties of large porous graphenes including the possibility of the Pariser–Parr–Pople (PPP) method with parameters extracted from machine learning of the DFT computations on a combinatorial library of precursors. Finally, the computations reveal that the porous structures can be tailored for sequestration of various ions including heavy metal ions for environmental remediation. Density functional theory ; Delocalized electron ; Computation ; Graph theory ; Raman spectroscopy ; Chemistry ; Computational chemistry ; Graph ; Topology (electrical circuits) ; Chemical physics ; Physics ; Quantum mechanics ; Computer science ; Mathematics ; Algorithm ; Theoretical computer science ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Graph theory and applications ; Graphene research and applications
Authors: Hamza Ahmad Isiyaka | Khairulazhar Jumbri | Nonni Soraya Sambudi | Zakariyya Uba Zango | Nor Ain Fathihah Abdullah | Bahruddin Saad
Source: Environmental Nanotechnology Monitoring & Management (article, 2022-02-14)
Field: Chemistry — Inorganic Chemistry

Effective adsorption of metolachlor herbicide by MIL-53(Al) metal-organic framework: Optimization, validation and molecular docking simulation studies Adsorption ; Response surface methodology ; Endothermic process ; Freundlich equation ; Central composite design ; Metal-organic framework ; Fourier transform infrared spectroscopy ; Chemistry ; Materials science ; Chemical engineering ; Physical chemistry ; Chromatography Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Arsenic contamination and mitigation
Authors: Limei Wang | Yi Liu | Yuchao Lin | Haoran Liu | Shuiwang Ji
Source: arXiv (Cornell University) (preprint, 2022-06-17)
Field: Materials Science — Materials Chemistry

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging. Existing methods either use partial 3D information, or suffer from excessive computational cost. To incorporate 3D information completely and efficiently, we propose a novel message passing scheme that operates within 1-hop neighborhood. Our method guarantees full completeness of 3D information on 3D graphs by achieving global and local completeness. Notably, we propose the important rotation angles to fulfill global completeness. Additionally, we show that our method is orders of magnitude faster than prior methods. We provide rigorous proof of completeness and analysis of time complexity for our methods. As molecules are in essence quantum systems, we build the \underline{com}plete and \underline{e}fficient graph neural network (ComENet) by combing quantum inspired basis functions and the proposed message passing scheme. Experimental results demonstrate the capability and efficiency of ComENet, especially on real-world datasets that are large in both numbers and sizes of graphs. Our code is publicly available as part of the DIG library (\url{https://github.com/divelab/DIG}). Computer science ; Message passing ; Theoretical computer science ; Completeness (order theory) ; Graph ; Scheme (mathematics) ; Code (set theory) ; Algorithm ; Distributed computing ; Mathematics ; Programming language Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Hydrocarbon exploration and reservoir analysis
Authors: Reza Maleki | Seyed Mohammadreza Shams | Yasin Mehdizadeh Chellehbari | Sima Rezvantalab | Ahmad Miri Jahromi | Mohsen Asadnia | Rouzbeh Abbassi | Tejraj M. Aminabhavi | Amir Razmjou
Source: Communications Chemistry (review, 2022-10-20)
Field: Materials Science — Materials Chemistry

Materials discovery of ion-selective membranes using artificial intelligence Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering. Membrane ; Computer science ; Artificial intelligence ; Nanotechnology ; Materials science ; Chemistry ; Biochemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Maryam Masoori | Mehdi Nekoomanesh | Sergio Posada‐Pérez | Reza Rashedi | Naeimeh Bahri‐Laleh
Source: Journal of Polymer Research (article, 2022-05-01)
Field: Chemistry — Organic Chemistry

Exploring cocatalyst type effect on the Ziegler–Natta catalyzed ethylene polymerizations: experimental and DFT studies Abstract Due to the important role of cocatalyst in the polymerization process employing industrially favored Ziegler–Natta catalysts, its effect on kinetic behavior, catalyst activity, and polymer properties is discussed. In this paper, triethyl aluminum (TEA) and triisobutyl aluminum (TIBA) have been used as the main cocatalyst ingredient with 10–20 mol percent of diethyl aluminum chloride (DEAC) and ethyl aluminum dichloride (EADC) cocatalysts, being neat TEA the cocatalysts with the highest activity. Moreover, TEA-DEAC and TEA-EADC cocatalysts revealed a built-up kinetic profile, while TIBA-DEAC and TIBA-EADC show a decay-type kinetic curve. According to melt flow index results, no considerable change in flowability was detected in the synthesized polyethylenes (PE). On the other hand, the ethylene insertion and chain termination mechanisms were investigated by means of density functional calculations using Ti active center located in (110) and (104) facets of the MgCl 2 surface. To shed light on the bulkiness level of employed cocatalysts, buried volume (V Bur ) together with the two-dimensional map of cocatalyst systems were considered. Higher V Bur of TIBA complex can explain its lower activity and decay type kinetic profile obtained by experimental studies. Catalysis ; Ethylene ; Materials science ; Aluminium ; Polymerization ; Polymer ; Polymer chemistry ; Kinetic energy ; Chemical engineering ; Organic chemistry ; Chemistry ; Composite material Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis ; Advanced Polymer Synthesis and Characterization
Authors: Chenru Duan | Daniel B. K. Chu | Aditya Nandy | Heather J. Kulik
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high-throughput screening (VHTS). Despite development of numerous MR diagnostics, the extent to which a single value of such a diagnostic indicates the MR effect on a chemical property prediction is not well established. We evaluate MR diagnostics for over 10 000 transition-metal complexes (TMCs) and compare to those for organic molecules. We observe that only some MR diagnostics are transferable from one chemical space to another. By studying the influence of MR character on chemical properties (<i>i.e.</i>, MR effect) that involve multiple potential energy surfaces (<i>i.e.</i>, adiabatic spin splitting, Δ<i>E</i> <sub>H-L</sub>, and ionization potential, IP), we show that differences in MR character are more important than the cumulative degree of MR character in predicting the magnitude of an MR effect. Motivated by this observation, we build transfer learning models to predict CCSD(T)-level adiabatic Δ<i>E</i> <sub>H-L</sub> and IP from lower levels of theory. By combining these models with uncertainty quantification and multi-level modeling, we introduce a multi-pronged strategy that accelerates data acquisition by at least a factor of three while achieving coupled cluster accuracy (<i>i.e.</i>, to within 1 kcal mol<sup>-1</sup> MAE) for robust VHTS. Throughput ; Cluster (spacecraft) ; Character (mathematics) ; Computer science ; Virtual screening ; Transfer of learning ; Transfer (computing) ; High-throughput screening ; Artificial intelligence ; Chemistry ; Computer network ; Computational chemistry ; Telecommunications ; Parallel computing ; Mathematics Machine Learning in Materials Science ; Machine Learning in Bioinformatics
Authors: Carmen Moya-López | Joaquín González-Fuentes | Iván Bravo | D. Chapron | P. Bourson | Carlos Alonso‐Moreno | Daniel Hermida‐Merino
Source: Pharmaceutics (review, 2022-08-11)
Field: Materials Science — Biomaterials

Polylactide Perspectives in Biomedicine: From Novel Synthesis to the Application Performance The incessant developments in the pharmaceutical and biomedical fields, particularly, customised solutions for specific diseases with targeted therapeutic treatments, require the design of multicomponent materials with multifunctional capabilities. Biodegradable polymers offer a variety of tailored physicochemical properties minimising health adverse side effects at a low price and weight, which are ideal to design matrices for hybrid materials. PLAs emerge as an ideal candidate to develop novel materials as are endowed withcombined ambivalent performance parameters. The state-of-the-art of use of PLA-based materials aimed at pharmaceutical and biomedical applications is reviewed, with an emphasis on the correlation between the synthesis and the processing conditions that define the nanostructure generated, with the final performance studies typically conducted with either therapeutic agents by in vitro and/or in vivo experiments or biomedical devices. Biomedicine ; Nanotechnology ; Biochemical engineering ; Computer science ; Materials science ; Engineering ; Bioinformatics biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Graphene and Nanomaterials Applications
Authors: Ebrahim Sharifpour | Payam Arabkhani | Fatemeh Sadegh | Ali Mousavizadeh | Arash Asfaram
Source: Scientific Reports (article, 2022-07-20)
Field: Chemistry — Analytical Chemistry

In-situ hydrothermal synthesis of CNT decorated by nano ZnS/CuO for simultaneous removal of acid food dyes from binary water samples The zinc sulfide/copper oxide-carbon nanotube nanocomposite (ZnS/CuO-CNT) was fabricated by using an in-situ hydrothermal synthesis method and was used for simultaneous ultrasound-assisted adsorptive removal of a binary mixture of ponceau 4R (P4R) and tartrazine (TA) acid food dyes from contaminated water. The as-synthesized ZnS/CuO-CNT was described by FESEM, XRD, FTIR, BET, and zeta potential analysis. The results included nested network morphology, high purity with the crystalline structure, oxygen-containing functional groups, mesoporous/micropores texture with cumulate interspace, specific surface area of 106.54 m<sup>2</sup> g<sup>-1</sup>, and zero-point charge (pH<sub>zpc</sub>) of 5.3. In adsorption experiments, the simultaneous effect of main independent variables, including solution pH, adsorbent dosage, concentration of each dye, temperature, and sonication time on the removal efficiency of dyes was studied systematically using the central composite design (CCD) method based on response surface methodology (RSM). Also, the second-order multivariate equation was presented to determine the relationship between the removal efficiencies of P4R and AT dyes and six independent effective variables. The high correlation coefficient (R<sup>2</sup> ≥ 0.99), significant p-value (P < 0.0001), and non-significant lack-of-fit (P > 0.05) showed the high accuracy, and validity of the proposed model to predict the removal efficiency of P4R and TA acid food dyes. The experimental removal efficiency for P4R and TA dyes was found to be 98.45 ± 2.54, and 99.21 ± 2.23, respectively. Also, the Langmuir maximum adsorption capacity for P4R and TA dyes was determined to be 190.1 mg g<sup>-1</sup> and 183.5 mg g<sup>-1</sup>, respectively. Finally, the adsorbent's reusability was tested for six periods and could be reused repeatedly without significant reduction in adsorption performance. Adsorption ; Point of zero charge ; Materials science ; Chemical engineering ; Response surface methodology ; Nuclear chemistry ; Specific surface area ; Malachite green ; Langmuir adsorption model ; Mesoporous material ; Central composite design ; Chemistry ; Chromatography ; Organic chemistry Dye analysis and toxicity ; Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors
Authors: Shizhao Lu | Brian Montz | Todd Emrick | Arthi Jayaraman
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images Semi-supervised transfer learning workflow facilitates rapid, automated nanomaterial morphology classification for small image datasets. Self-supervised training enables label-free pretraining that minimizes drawbacks of manual labeling. Transmission electron microscopy ; Workflow ; Nanowire ; Electron microscope ; Computer science ; Materials science ; Nanotechnology ; Transmission (telecommunications) ; Artificial intelligence ; Physics ; Optics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Fariba Alimohammadi | Mansour Rasekh | Amir Hosein Afkari Sayyah | Yousef Abbaspour‐Gilandeh | Hamed Karami | Vali Rasooli Sharabiani | Ambra Fioravanti | Marek Gancarz | Pavol Findura | D. Kwaśniewski
Source: International Agrophysics (article, 2022-04-07)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging coupled with multivariate analysis and artificial intelligenceto the classification of maize kernels A b s t r a c t. Maize (Zea mays) is one of the key crops in the world, taking third place after wheat and rice in terms of cultivated area. This study aimed to demonstrate the potential of non-destructive hyperspectral imaging in the wavelength range of 400-1000 nm to discriminate between and classify maize kernels in three cultivars by using non-destructive hyperspectral imaging in the wavelength range of 400-1000 nm. Three cultivars of maize kernels were exposed to hyperspectral imaging with 20 replications. Predictor variables included 28 intensities of reflection wave for spectral imaging and 4 variables in terms of the weight, length, width, and thickness of a single kernel. The classification was successfully performed through Linear Discriminant Analysis and Artificial Neural Network methods, taking into account 32, 15, and 5 predictor variables. According to the results, Linear Discriminant Analysis with 32 predictor variables is characterized by a high degree of accuracy (95%). The most important predictor variables included the reflection wave intensity of the third peak, the wavelength intensity of 490 nm, the wavelength intensity of 580 nm, and the weight and thickness of a single kernel. Hyperspectral imaging ; Characterization (materials science) ; Plant science ; Multivariate statistics ; Remote sensing ; Artificial intelligence ; Computer science ; Biology ; Botany ; Physics ; Geology ; Machine learning ; Optics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sameh H. Negm | Mahmoud O. Abd El‐Magied | Wael M. El Maadawy | M. M. Abdel Aal | Samia M. Abd El Dayem | Mahmoud A. Taher | Khaled A. Abd El-Rahem | Mohamed Nageeb Rashed | Mohamed F. Cheira
Source: Separations (article, 2022-10-14)
Field: Chemistry — Inorganic Chemistry

Appreciatively Efficient Sorption Achievement to U(VI) from the El Sela Area by ZrO2/Chitosan The need to get uranium out of leaching liquid is pushing scientists to come up with new sorbents. This study uses the wet technique to improve the U(VI) sorption properties of ZrO2/chitosan composite sorbent. To validate the synthesis of ZrO2/CS composite with Zirconyl-OH, -NH, and -NH2 for U(VI) binding, XRD, FTIR, SEM, EDX, and BET are used to describe the ZrO2/chitosan wholly formed. To get El Sela leaching liquid, it used 150 g/L H2SO4, 1:4 S:L ratio, 200 rpm agitation speed, four hours of leaching period, and particle size 149–100 µm. In a batch study, the sorption parameters are evaluated at pH 3.5, 50 min of sorbing time, 50 mL of leaching liquid (200 mg/L U(VI)), and 25 °C. The sorption capability is 175 mg/g. Reusing ZrO2/CS for seven cycles with a slight drop in performance is highly efficient, with U(VI) desorption using 0.8 M acid and 75 min of desorption time. The selective U(VI) recovery from El Sela leachate was made possible using ZrO2/CS. Sodium diuranate was precipitated and yielded a yellow cake with a purity level of 94.88%. Sorption ; Sorbent ; Leaching (pedology) ; Leachate ; Nuclear chemistry ; Desorption ; Chitosan ; Chemistry ; Particle size ; Uranium ; Adsorption ; Materials science ; Environmental chemistry ; Organic chemistry ; Geology ; Metallurgy ; Soil water Radioactive element chemistry and processing ; Extraction and Separation Processes ; Geochemistry and Geologic Mapping