jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mingkun Chen | Jiaqi Jiang | Jonathan A. Fan
Source: arXiv (Cornell University) (preprint, 2020-07-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design space reparameterization enforces hard geometric constraints in inverse-designed nanophotonic devices Inverse design algorithms are the basis for realizing high-performance, freeform nanophotonic devices. Current methods to enforce geometric constraints, such as practical fabrication constraints, are heuristic and not robust. In this work, we show that hard geometric constraints can be imposed on inverse-designed devices by reparameterizing the design space itself. Instead of evaluating and modifying devices in the physical device space, candidate device layouts are defined in a constraint-free latent space and mathematically transformed to the physical device space, which robustly imposes geometric constraints. Modifications to the physical devices, specified by inverse design algorithms, are made to their latent space representations using backpropagation. As a proof-of-concept demonstration, we apply reparameterization to enforce strict minimum feature size constraints in local and global topology optimizers for metagratings. We anticipate that concepts in reparameterization will provide a general and meaningful platform to incorporate physics and physical constraints in any gradient-based optimizer, including machine learning-enabled global optimizers. Computer science ; Heuristic ; Inverse ; Constraint (computer-aided design) ; Space (punctuation) ; Topology (electrical circuits) ; Inverse problem ; Theoretical computer science ; Mathematical optimization ; Algorithm ; Artificial intelligence ; Mathematics ; Geometry Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Photonic and Optical Devices
Authors: Mai H. Philipsen | Nhu T. N. Phan | John S. Fletcher | Andrew G. Ewing
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-01-09)
Field: Chemistry — Spectroscopy

Mass Spectrometry Imaging of Cocaine, Drug Removal and Methylphenidate Alter Phospholipids in Drosophila Brain Abstract Cocaine dependence displays a broad impairment in cognitive performance including attention, learning and memory. To obtain a better understanding of the action of cocaine in the nervous system, and the relation between phospholipids and memory, we have investigated whether phospholipids recover in the brain following cocaine removal using the fly model, Drosophila . In addition, the effects of methylphenidate, a substitute medication for cocaine dependence, on fly brain lipids after cocaine abuse are also determined to see if it can rescue the lipid changes caused by cocaine. Time of flight secondary ion mass spectrometry with a (CO 2 ) 6000+ gas cluster ion beam was used to detect intact phospholipids. We show that cocaine has persistent effects, both increasing and decreasing the levels of specific phosphatidylethanolamines and phosphatidylinositols. These changes remain after cocaine withdrawal and are not rescued by methylphenidate. Cocaine is again shown to generally increase the levels of phosphatidylcholines in the fly brain; however, after drug withdrawal the abundance of these lipids returns to the original level and methylphenidate treatment of the flies following cocaine exposure enhances the reversal of the lipid level reducing them below the original control. The study provides insight into the molecular effects of cocaine and methylphenidate on brain lipids. We suggest that phosphatidylcholine could be a potential target for the treatment of cocaine abuse, as well as be a significant hallmark of cognition and memory loss with cocaine. Methylphenidate ; Drugs of abuse ; Drug ; Pharmacology ; Cocaine dependence ; Chemistry ; Phosphatidylcholine ; Psychology ; Neuroscience ; Addiction ; Biochemistry ; Medicine ; Attention deficit hyperactivity disorder ; Psychiatry ; Phospholipid Mass Spectrometry Techniques and Applications ; Forensic Toxicology and Drug Analysis ; Metabolomics and Mass Spectrometry Studies
Authors: Dhanabalan Murali | K. Narayanamoorthy | Aagashram Neelakandan | Rehan Biag | K. Gnanasekaran | Ramesh Krishnan
Source: International Journal of Engineering and Advanced Technology (article, 2020-12-30)
Field: Materials Science — Materials Chemistry

Performance Comparison of Cryogenic Treated Tool Vs Untreated Tool using an Accelerometer Sensor In this paper, performance of the Cryogenic treated tool along with the untreated tool is investigated in turning of En8 Medium carbon steel. Results are got by comparing parameters of vibration in tool. For vibration Accelerometer sensor, it is used to find the Acceleration of tool in X, Y and Z axis for incremental depth of cut. The cryogenic tool has lesser machining time, increased tool life and surface finish due to the reduced vibration than the untreated tool. Accelerometer ; Machining ; Vibration ; Machine tool ; Acceleration ; Materials science ; Cryogenic treatment ; Mechanical engineering ; Acoustics ; Computer science ; Engineering ; Composite material ; Physics Metal Alloys Wear and Properties ; High Entropy Alloys Studies ; Microstructure and Mechanical Properties of Steels
Authors: Elnaz Khodapanah
Source: Petroleum & Petrochemical Engineering Journal (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Parametric Investigation of Wettability Alteration of Reservoir Rocks by Asphaltene Deposition: Experimental and Modeling Approaches Rock wettability plays an important role in water flooding process as it controls fluid flow, oil recovery and distribution of residual oil in any oil reservoir. In this context, polar oil components such as asphaltene contents may adsorb onto the pore mineral surfaces and alter wettability of the reservoir rock. Due to this importance, this study aims to investigate the effects of different parameters such as concentration of asphaltene, salinity, temperature and time on the rock wettability alteration process. For this purpose, dynamic contact angle measurement was performed. The results showed that the increment of asphaltene concentration in the oleic phase changes the wettability of water-wet sandstone rock to oil-wet condition; the increase in the concentration of asphaltene fraction from 0.1 to 5.0 g/lit increased the contact angle from 0 to 97 degrees. In addition, the increase in the brine salinity from 500 to 8000 ppm increased the ability of asphaltene to adsorb on the rock surface and consequently, increased oil wetness; the equivalent contact angle changed from 0 to 113 degree for 5 g/lit asphaltene content after 192 hours. Moreover, the results illustrated that a rise in temperature from 40 to 70 o C accelerates adsorption of asphaltene, but it has not significant effect on the final contact angle. Furthermore, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is incorporated into the Particle Swarm Optimization (PSO) algorithm to correlate contact angle with the aforementioned parameters. To this end, the obtained experimental data are used to train and test the algorithm. The outputs of ANFIS-PSO algorithm are compared with the measured contact angles in both graphical and statistical manners. The training and testing determination coefficients (R 2 ) have been obtained as 0.99091 and 0.98761, respectively. The analysis indicates that the predictive model can be used with a high degree of confidence to investigate the effect of different parameters on wettability alteration Asphaltene ; Contact angle ; Wetting ; Imbibition ; Adsorption ; Petroleum engineering ; Chemical engineering ; Materials science ; Context (archaeology) ; Mineralogy ; Chemistry ; Geology ; Composite material ; Organic chemistry Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Pranay Paul | A. Bhuimali | P. S. Aithal
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-11-04)
Field: Chemistry — Physical and Theoretical Chemistry

Environmental Informatics: The Foundation, Allied &amp; Related Branches—Analytical Study Environmental Informatics is the emerging interdisciplinary practicing facet responsible for the applications and utilization of Information Technology in Environment, Nature, and Ecology. Different other areas viz. Geology, Agriculture, Forestry, Geography, Climatology, Oceanography, etc are also important stakeholders of Environmental Informatics. The branch Environmental Informatics also called as Eco Informatics, Ecology Informatics in some contexts. Environmental Informatics is quite different from Environmental Information Technology. Environmental Informatics interconnects both environmental as well as information sciences for the complete natural processes with language common to both humans and computers. This is still a practicing phenomenon; however, in few countries, this can be seen as a branch of study. There are different potentialities to offer Environmental Informatics as an academic branch due to its wider scope and utilizations. The field is practiced by professionals in forestry, agriculture, ecology and environment, corps and horticulture, geography and geology, etc. In this paper, various internal and related aspects of Environmental Informatics in different areas and sectors are analyzed. Foundation (evidence) ; Informatics ; Data science ; Engineering ; Computer science ; Geography ; Archaeology History and advancements in chemistry ; Data Quality and Management ; Big Data and Business Intelligence
Authors: Sreenija Pudi | G.D.V.S.S. Chandramouli | G. Avinash | K. Ashok | A. Chandra Kiran Durga Prasad
Source: International Journal of Engineering Applied Sciences and Technology (article, 2020-07-31)
Field: Materials Science — General Materials Science

OPTIMIZATION OF PROCESS PARAMETERS OF AWJM ON AA6061-7.5% SiC MMC FOR LOWER SURFACE ROUGHNESS AND HIGHER MRR Metal matrix composites (MMCs) have evoked a keen interest in recent times for potential applications in aerospace and automotive industries. Hardness and strength are the prime requirements of MMCs used in structural applications. In general, these properties are exhibited by Al-SiC MMCs. But major restraints are material cost, heterogeneous distribution of reinforcement in matrix during manufacturing and inaccurate dimensions of final product after machining. Subduing the limitations, an attempt has been made to fabricate AA6061-7.5%SiC using two step stir casting method and machine it using Abrasive Water Jet Machining (AWJM) technique. The present investigation targets to optimize the AWJM process parameters while machining AA6061-7.5%SiC MMC. The variable process parameters of AWJM are considered as abrasive feed, stand-off distance and traverse speed. Using the L9 orthogonal array by Taguchi method for design of experiments and analysis, the liaison between these parameters and their responses is explored by ANOVA and Response Surface Methodology (RSM). The results are developed in accordance to a quality control factor and a machinability factor which are surface roughness (SR) and material removal rate (MRR) respectively. Optimal parameters are obtained with respect to lower SR and higher MRR. Surface roughness ; Materials science ; Surface finish ; Process (computing) ; Composite material ; Computer science Material Properties and Applications ; Aluminum Alloys Composites Properties
Authors: W.G. Davey | M.R. Bache | H. Davies | Matthew Thomas | I. Bermant-Parr
Source: MATEC Web of Conferences (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Fatigue Performance of the Novel Titanium Alloy TIMETAL<sup>®</sup>407 TIMETAL ® 407 (Ti-407) is a novel titanium alloy formulated as a medium strength, highly ductile alloy offering a range of manufacturing cost reduction opportunities. It can be used as a direct replacement for Ti-6Al-4V or Ti-3Al-2.5V alloys, particularly in applications where energy absorption during fracture or HCF endurance are the key design criteria. The effect of thermo-mechanical processing on microstructure has been characterised and the room temperature high cycle, low cycle and dwell cycle fatigue properties of Ti-407 containing 30-40% primary alpha volume fraction are presented and discussed. These results are compared with data generated from Ti-6Al-4V processed to provide a similar but non-standard microstructure and demonstrate that Ti-407 shows superior HCF endurance strength, despite having a significantly lower tensile and yield strength. Materials science ; Alloy ; Microstructure ; Titanium alloy ; Ultimate tensile strength ; Metallurgy ; Titanium ; Volume fraction ; Dwell time ; Fatigue limit ; Composite material Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Additive Manufacturing Materials and Processes
Authors: Chih‐Hsiang Chang | Darien Yeung | Victor Spicer | Oleg V. Krokhin | Yasushi Ishihama
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-09-15)
Field: Chemistry — Spectroscopy

Sequence-specific model for predicting peptide collision cross-section values in proteomic ion mobility spectrometry ABSTRACT The contribution of peptide amino-acid sequence to collision cross-section values (CCS) has been investigated using a dataset of ∼134,000 peptides of four different charge states (1+ to 4+). The migration data was acquired using a two-dimensional LC/trapped ion mobility spectrometry/quadrupole/time-of-flight MS analysis of HeLa cell digests created using 7 different proteases and was converted to CCS values. Following the previously reported modeling approaches using intrinsic size parameters (ISP), we extended this methodology to encode the position of individual residues within a peptide sequence. A generalized prediction model was built by dividing the dataset into 8 groups (four charges for both tryptic/non-tryptic peptides). Position dependent ISPs were independently optimized for the eight subsets of peptides, resulting in prediction accuracy of ∼0.981 for the entire population of peptides. We find that ion mobility is strongly affected by the peptide’s ability to solvate the positively charged sites. Internal positioning of polar residues and proline leads to decreased CCS values as they improve charge solvation; conversely, this ability decreases with increasing peptide charge due to electrostatic repulsion. Furthermore, higher helical propensity and peptide hydrophobicity result in preferential formation of extended structures with higher than predicted CCS values. Finally, acidic/basic residues exhibit position dependent ISP behaviour consistent with electrostatic interaction with the peptide macro-dipole, which affects the peptide helicity. Peptide ; Ion-mobility spectrometry ; Chemistry ; Mass spectrometry ; Peptide sequence ; Ion ; Solvation ; Electrospray ionization ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Hui Shao | Wenmin Huang | Luisana Avilán | Véronique Receveur‐Brechot | Carine Puppo | Rémy Puppo | Régine Lebrun | Brigitte Gontero | Hélène Launay
Source: Research Square (preprint, 2020-12-29)
Field: Chemistry — Spectroscopy

A new type of disordered CP12 protein in the marine diatom Thalassiosira pseudonana Abstract Background CP12 is a small chloroplast protein that is widespread in various photosynthetic organisms and is often involved in the redox metabolic on/off switch of the Calvin Benson Bassham (CBB) cycle. The gene encoding this protein is conserved in many diatoms, but the protein has been overlooked in these organisms, despite their ecological predominance and their complex and still enigmatic evolutionary background. Methods A combination of biochemical, bioinformatics and biophysical methods including electrospray ionization-mass spectrometry, circular dichroism, nuclear magnetic resonance and small X ray scattering spectroscopy, was used to characterize a diatom CP12. Results Here, we demonstrate that CP12 is expressed in the marine diatom Thalassiosira pseudonana constitutively in dark-treated and in continuous light-treated cells as well as in all growth phases. This CP12 behaves abnormally under gel electrophoresis, is heat resistant and lacks a structural core, all features of intrinsically disorder family similarly to its homologues in other species. By contrast, unlike other known CP12 proteins that are monomers, this protein is a dimer as shown by native electrospray ionization-mass spectrometry and small angle X-ray scattering. In addition, small angle X-ray scattering showed that this CP12 is an elongated cylinder with kinks. Circular dichroism spectra indicated that CP12, though it has features of disordered proteins, has a high content of α-helices. Nuclear magnetic resonance spectroscopy showed that these helices are unstable and dynamic within a millisecond timescale. Together with in silico predictions, these results suggest that T. pseudonana CP12 has both coiled-coil and disordered regions. Conclusions These findings bring new insights into the large family of intrinsically disordered proteins increasing the diversity of known CP12 proteins. This raises questions about the role of this protein in addition to the well-established regulation of the CBB cycle. Thalassiosira pseudonana ; Diatom ; Type (biology) ; Chemistry ; Botany ; Biology ; Ecology ; Nutrient ; Phytoplankton Advanced Proteomics Techniques and Applications ; Diatoms and Algae Research ; Mass Spectrometry Techniques and Applications
Authors: Hiroyuki Hori | Shota Takemura | Hayato Obata | Kazuyoshi Ogasawara
Source: Advance Sustainable Science Engineering and Technology (article, 2020-04-09)
Field: Materials Science — Materials Chemistry

Prediction of 4f7-4f65d1 transition energy of Eu2+ in oxides based on first-principles calculations and machine learning In order to establish a method to predict the 4f7-4f65d1 transition energy of Eu2+ in oxides, linear regression models were created based on first-principles calculations and machine learning. The model clusters consisting of the central Eu2+ and O2- ions closer than the nearest cation were constructed and the 4f7-4f65d1 absorption energy of Eu2+ in these clusters were calculated by first-principles many-electron calculation using the relativistic discrete variational multi-electron (DVME) method. However, the 4f7-4f65d1 absorption energies of Eu2+ in oxides calculated by relatively simple first-principles calculations tend to be overestimated by ca. 1.6 eV. In order to improve the accuracy of the prediction, we performed machine learning considering the calculated absorption energy as well as the other electronic and structural parameters as the attributes. As a result, the regression formula to predict the 4f7-4f65d1 absorption energy of Eu2+ in oxides has been created by machine learning. The 4f7-4f65d1 absorption energy predicted by this model are in good agreement with the experimental ones. Therefore, accuracy of the prediction was significantly improved compared to the simple first-principles calculations. In a similar way, a predictive model of the 4f65d1-4f7 emission energy of Eu2+ in oxides has been also created Computer science ; Energy (signal processing) ; Absorption (acoustics) ; Physics ; Optics ; Mathematics ; Statistics Luminescence Properties of Advanced Materials ; Chemical Synthesis and Characterization ; Catalytic Processes in Materials Science
Authors: Zhongmin Yang | Guowu Tang | Zhonghong Jiang | Guoquan Qian | Qi Qian
Source: Scientia Sinica Technologica (article, 2020-03-23)
Field: Materials Science — Ceramics and Composites

Glass genetic engineering <p indent=0mm>On its 125th anniversary edition, the journal, <italic>Science</italic>, listed “what is the nature of the glassy state?” as one of the 125 most challenging scientific issues facing modern science. Predicting glass properties is challenging because of the complexity of the glassy materials, which significantly imped the development of functional glasses. At present, the development of high-performance functional glasses relies heavily on prior experience and trial-and-error, resulting in long experimental periods, high costs, and low efficiency. Materials genome engineering is expected to accelerate glass research. Based on the metastable composition diagram, this paper proposes that the neighboring congruent glassy compounds in the glass composition diagram can be regarded as glass genes. The glass-forming region of a BaO-Ga<sub>2</sub>O<sub>3</sub>-GeO<sub>2</sub> glass system was calculated by thermodynamic method using the compound’s thermodynamic data. The properties of different glass systems were calculated using the glass gene approach, giving theoretical calculation results consistent with experimental data in the literature. Furthermore, the germanate anomaly can be predicted by the glass gene approach. This work may provide an effective way to decode the glass genome and develop functional glasses rapidly and at low cost. Metastability ; Materials science ; Glass transition ; Chemistry ; Polymer ; Composite material Glass properties and applications
Authors: Philipp O. Tsvetkov | Rémi Eyraud | Stéphane Ayache | Anton Bougaev | Soazig Malesinski | Hamed Benazha | Svetlana Gorokhova | Christophe Buffat | Caroline Dehais | Marc Sanson | Franck Bielle | Dominique Figarella‐Branger | Olivier Chinot | Émeline Tabouret | François Devred
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-08-10)
Field: Chemistry — Physical and Theoretical Chemistry

An AI-Powered Blood Test to Detect Cancer Using NanoDSF Glioblastoma is the most frequent and aggressive primary brain tumor. Its diagnosis is based on resection or biopsy that could be especially difficult and dangerous in the case of deep location or patient comorbidities. Monitoring disease evolution and progression also requires repeated biopsies that are often not feasible. Therefore, there is an urgent need to develop biomarkers to diagnose and follow glioblastoma evolution in a minimally invasive way. In the present study, we described a novel cancer detection method based on plasma denaturation profiles obtained by a non-conventional use of differential scanning fluorimetry. Using blood samples from 84 glioma patients and 63 healthy controls, we showed that their denaturation profiles can be automatically distinguished with the help of machine learning algorithms with 92% accuracy. Proposed high throughput workflow can be applied to any type of cancer and could become a powerful pan-cancer diagnostic and monitoring tool requiring only a simple blood test. Workflow ; Computer science ; Cancer ; Cancer detection ; Blood test ; Artificial intelligence ; Diagnostic test ; Pattern recognition (psychology) ; Medicine ; Internal medicine thermodynamics and calorimetric analyses ; Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies