jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohana Krishnudu Doni | Reddy Pathapalli | V. Saikumar | Reddy Ravipati | Mohana Doni | P Reddy | D Krishnudu | P Prasad | B Reddy | R Reddy | Bcm Reddy | H Rao | J Prakash | L Manjunath | J Rao | A Boukhoulda | F Boukhoulda | H Makich | M Nouari | B Haddag | S Mouhoubi | Meh Bourahli | H Osmani | S Abdeslam | A Bessadok | S Marais | F Gouanv | L Colasse | I Zimmerlin | A Mohanty | M Khan | G Hinrichsen | S Sumi | N Unnikrishnan | L Mathew | J Jayaramudu | A Maity | E Sadiku | B Guduri | A Rajulu | Gsm Reddy | K Varaprasad | S Ray | M Amin | A Maadhah | A Usmani | G Prasad | B Gowda | R Velmurugan | S Sathiyamurthy | A Thaheer | S Jayabal | C Nwobi-Okoye | B Ochieze | N Saba | M Paridah | K Abdan | N Ibrahim | P Tahir | M Jawaid | Bhowmik Jagadish | A Ray | M Nikzad | K Movagharnejad | F Talebnia | Z Aghaiy | M Mighani | H Parikh | P Gohil | K Sen | S Reddy | E Betiku | Odude Vo | N Ishola | A Bamimore | A Osunleke | Y Wang | J Feng | Q Zhang | X Zhang | J Reddy | Bjs Varaprasad
Source: NanoWorld Journal (article, 2023-11-27)
Field: Materials Science — Biomaterials

Modeling for Predicting the Mechanical Properties of Calcium Carbonate Filled Coir-silk Squash Hybrid Composites Using Response Surface Methodology and Artificial Neural Network Mohana Krishnudu Doni, Venkateshwar Reddy Pathapalli and Venkata Saikumar Reddy Ravipati Presently, there is a need to switch for more sustainable and renewable materials because of its low weight, economic, eco-friendly, biodegradable and easily recyclable. The samples of coir, silk squash hybrid composites filled with calcium carbonate (CaCO3) powder have been prepared using hand lay-up technique and the mechanical properties of the prepared hybrid composite material are determined. Composite material ; Squash ; Artificial neural network ; Calcium carbonate ; Materials science ; SILK ; Coir ; Response surface methodology ; Computer science ; Artificial intelligence ; Machine learning ; Botany ; Biology Silk-based biomaterials and applications ; Textile materials and evaluations
Authors: Sirous Yourdkhani | Jiří Klimeš
Source: arXiv (Cornell University) (preprint, 2023-08-23)
Field: Materials Science — Surfaces, Coatings and Films

Using Non-covalent Interactions to Test Precision of Projector-Augmented Wave Data Sets The projector-augmented wave (PAW) method is one of the approaches that are widely used to approximately treat core electrons and thus to speed-up plane-wave basis set electronic structure calculations. However, PAW involves approximations and it is thus important to understand how they affect the results. Tests of precision of PAW data sets often use properties of isolated atoms or of atomic solids. While this is sufficient to identify problematic PAW data sets, little information has been gained to understand the origins of the errors and suggest ways to correct them. Here we show that interaction energies of molecular dimers are very useful not only to identify problematic PAW data sets but also to uncover the origin of the errors. Using dimers from the S22 and S66 test sets and other dimers we find that the error in the interaction energy is composed of a short range component with an exponential decay and a long range electrostatic part caused by error in the total charge density. We propose and evaluate a simple improvable scheme to correct the long range error and find that even in its simple and readily usable form, it is able to reduce the interaction energy errors to less than one half on average for hydrogen bonded dimers. Projector ; USable ; Range (aeronautics) ; Computer science ; Set (abstract data type) ; Energy (signal processing) ; Simple (philosophy) ; Algorithm ; Interaction energy ; Physics ; Molecule ; Artificial intelligence ; Materials science ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices ; Advanced Chemical Physics Studies
Authors: Giacomo Lancia | Claudio Durastanti | Cristian Spitoni | Ilaria De Benedictis | Antonio Sciortino | Emilio N. M. Cirillo | Mario Ledda | Antonella Lisi | Annalisa Convertino | Valentina Mussi
Source: arXiv (Cornell University) (preprint, 2023-02-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Learning models for classifying Raman spectra of genomic DNA from tumor subtypes An early detection of different tumor subtypes is crucial for an effective guidance to personalized therapy. While much efforts focus on decoding the sequence of DNA basis to detect the genetic mutations related to cancer, it is becoming clear that physical properties, including structural conformation, stiffness, and shape, as well as biological processes, such as methylation, can be pivotal to recognize DNA modifications. Here we exploit the Surface Enhanced Raman Scattering (SERS) platform, based on disordered silver coated--silicon nanowires, to investigate genomic DNA from subtypes of melanoma and colon cancers and to efficiently discriminate tumor and healthy cells, as well as the different tumor subtypes. The diagnostic information is obtained by performing label--free Raman maps of the dried drops of DNA solutions onto the Ag/NWs mat, and leveraging the classification ability of learning models to reveal the specific and distinct interaction of healthy and tumor DNA molecules with nanowires. DNA ; genomic DNA ; Raman scattering ; Raman spectroscopy ; Computational biology ; Personalized medicine ; Materials science ; Nanotechnology ; Biology ; Genetics ; Physics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Atashgar, K. | Masoudi, R.
Source: Mavādd-i pīshraftah dar muhandisī (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Radar Evasion Control of Military Structures Using Graphene Oxide Coating RGO/NiFe2O4 and Polynomial Profile Monitoring Tracking military structures and equipment is one of the parameters to create superiority in military battles. Camouflage has long been used to reduce the possibility of detection of military structures and equipment. Development of knowledge in the field of tracking and discovering military structures and equipment followed by the necessity of using the concealment in order to reduce vulnerability in war, has enhanced the importance of using new camouflage and radar evasion methods. The use of nanotechnology in the field of radar evasion of military facilities was developed by introducing graphene as a very strong absorber of electromagnetic waves. Graphene coating on the military installations causes the absorption of electromagnetic waves and as a result, these installations are not detected by the enemy's radar. Referring to the fact that there is a functional relationship between the diameter of the graphene oxide used and the radar evasion of the equipment, an attempt has been made in this article for the first time to find a solution to control and monitor the radar evasion quality using graphene oxide (with the scientific name of RGO/NiFe2O4) in the microwave spectrum of 7GHZ by the profile approach and presentation of a regression relationship. This model can be used to monitor the quality of radar evasion products (cheaper and faster than existing methods). Finally, sensitivity analysis of the model showed that the ability to detect non-conformity in the manufactured products can be detected quickly (between 1 and 20 samples) with the change in the parameters of the regression model. Radar ; Camouflage ; Graphene ; Evasion (ethics) ; Computer science ; Microwave ; Remote sensing ; Materials science ; Aerospace engineering ; Nanotechnology ; Engineering ; Artificial intelligence ; Geology ; Telecommunications Graphene research and applications ; Optical Systems and Laser Technology ; Military Technology and Strategies
Authors: Xin Li | Qi Wang | Wei Gao | Ziyang Shi | Hao Guo | Zhengjie Luo | Zhonghao Li | Huanfei Wen | Zongmin Ma | Jun Tang | Jun Liu
Source: Japanese Journal of Applied Physics (article, 2023-10-13)
Field: Materials Science — Materials Chemistry

Extremely low-frequency magnetic spectrum measurement method based on the NV center in diamond Abstract In this paper, we establish a set of schemes to generate, detect, and identify the multi-frequency magnetic field in the extremely low-frequency range. Based on the magnetic sensitivity of nitrogen-vacancy centers in diamond, the schemes adopt frequency closed-loop proportion-integration-differentiation locking and microwave modulation and demodulation to obtain magnetic field information. A set of multi-coil mutual inductance devices is used to generate a multi-frequency AC magnetic field. In the schemes, the DenseNet network structure is used to train and identify the magnetic field information, with a recognition rate of 99.16%. When the Net is used to identify noisy signals, it still maintains an average recognition rate of 95.18% for random frequency noisy signals. This generating, detecting, and identifying schemes of the multi-frequency magnetic field in the extremely low-frequency range based on quantum sensors in this paper provides a novel idea for the future application of quantum sensors in biomedicine. Demodulation ; Magnetic field ; Diamond ; Computer science ; Quantum sensor ; Frequency modulation ; Center frequency ; Sensitivity (control systems) ; Quantum ; Physics ; Electronic engineering ; Radio frequency ; Quantum information ; Materials science ; Telecommunications ; Quantum network ; Engineering ; Optics ; Band-pass filter ; Quantum mechanics Diamond and Carbon-based Materials Research ; Atomic and Subatomic Physics Research ; Magnetic Field Sensors Techniques
Authors: Matthew Helmi Leth Larsen | William Bang Lomholdt | Cuauhtémoc Núñez Valencia | Thomas W. Hansen | Jakob Schiøtz
Source: arXiv (Cornell University) (preprint, 2023-02-24)
Field: Materials Science — Surfaces, Coatings and Films

Quantifying Noise Limitations of Neural Network Segmentations in High-Resolution Transmission Electron Microscopy Motivated by the need for low electron dose transmission electron microscopy imaging, we report the optimal frame dose (i.e. $e^-/A^{2}$) range for object detection and segmentation tasks with neural networks. The MSD-net architecture shows promising abilities over the industry standard U-net architecture in generalising to frame doses below the range included in the training set, for both simulated and experimental images. It also presents a heightened ability to learn from lower dose images. The MSD-net displays mild visibility of a Au nanoparticle at 20-30 $e^-/A^{2}$, and converges at 200 $e^-/A^{2}$ where a full segmentation of the nanoparticle is achieved. Between 30 and 200 $e^-/A^{2}$ object detection applications are still possible. This work also highlights the importance of modelling the modulation transfer function when training with simulated images for applications on images acquired with scintillator based detectors such as the Gatan Oneview camera. A parametric form of the modulation transfer function is applied with varying ranges of parameters, and the effects on low electron dose segmentation is presented. Optical transfer function ; Computer science ; Artificial intelligence ; Segmentation ; Visibility ; Noise (video) ; Artificial neural network ; Detector ; Transmission (telecommunications) ; Electron tomography ; Frame (networking) ; Computer vision ; Image segmentation ; Modulation (music) ; Optics ; Transmission electron microscopy ; Physics ; Scanning transmission electron microscopy ; Image (mathematics) ; Acoustics ; Telecommunications Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Onurcan Kaya | Luigi Colombo | Aleandro Antidormi | Marco A. Villena | Mario Lanza | Ivan Cole | Stephan Roche
Source: arXiv (Cornell University) (preprint, 2023-10-27)
Field: Materials Science — Materials Chemistry

Impact of Hydrogenation on the Stability and Mechanical Properties of Amorphous Boron Nitride Interconnect materials with ultralow dielectric constant, and good thermal and mechanical properties are crucial for the further miniaturization of electronic devices. Recently, it has been demonstrated that ultrathin amorphous boron nitride (aBN) films have a very low dielectric constant, high density (above 2.1 g/cm3), high thermal stability, and mechanical properties. The excellent properties of aBN derive from the nature and degree of disorder, which can be controlled at fabrication, allowing tuning of the physical properties for desired applications. Here, we report an improvement in the stability and mechanical properties of amorphous boron nitride upon hydrogen doping. With the introduction of a Gaussian approximation potential (GAP) for atomistic simulations, we investigate the changing morphology of amorphous boron nitride with varying H doping concentrations. We found that for 8 at% of H doping, the concentration of $sp^3$-hybridized atoms reaches a maximum which leads to an improvement of thermal stability and mechanical properties by 20%. These results will be a guideline for experimentalists and process engineers to tune the growth conditions of amorphous boron nitride films for numerous applications. Boron nitride ; Materials science ; Amorphous solid ; Thermal stability ; Dielectric ; Nitride ; Fabrication ; Doping ; Nanotechnology ; Boron ; Optoelectronics ; Chemical engineering ; Chemistry ; Crystallography Diamond and Carbon-based Materials Research ; Graphene research and applications ; Semiconductor materials and devices
Authors: Rashid Khan | Kavita Srivastava
Source: Journal for Research in Applied Sciences and Biotechnology (article, 2023-10-31)
Field: Materials Science — Polymers and Plastics

Advanced Polymer Blends for Enhanced Mechanical Performance Highly engineered polymer combinations have become an important avenue towards high performance in demanding engineered industries. Nevertheless, their reliability is still hampered by immiscibility, poor interfacial adhesion and morphology instability. This study is critical of the latest advances in compatibilizer techniques, nanofiller reinforcement and process optimisation so as to evaluate their overall effect on tensile strength, toughness, fatigue behaviour, and thermomechanical stability. The comparative analysis was conducted with the data published within 20212025 with the emphasis on the polymer-pair selection, interfacial design, dispersed-phase control, and hybrid reinforcement systems. The results indicate that compatibilizers are able to steadily enhance morphology, as well as, interfacial adhesion, producing 2050 enhancements in various systems. Graphene, nano-clay, silica and CNTs are other types of nanofillers that increase the stiffness and impact strength when dispersed effectively to a range of 17-35%. Compatibilizers and nanofillers when used in combination provide synergistic effects with hybrid improvements that are better than the contributions of each and synergy factors which varies between 1.1 to 1.5. High-shear mixing, twin-screw reactive extrusion, and controlled cooling were also demonstrated to be equally decisive, to the extent of domain size of less than 0.8 2 m and improvement of tensile performance by as much as 17 percent. The association of a negative correlation between the domain size and the mechanical performance is made certain through structure-property relations. Although the results are promising, there are still gaps in fatigue, durability and validation at industry scale. In general, this paper draws attention to the fact that the development of high-performance polymer blends is impossible without interrelating material chemistry, interfacial engineering and processing design into one optimisation concept. Ultimate tensile strength ; Durability ; Stiffness ; Polymer ; Polymer blend ; Reinforcement Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Tribology and Wear Analysis
Authors: Yizhou Xu | Jianxun Liu | Rui Zhou
Source: Highlights in Science Engineering and Technology (article, 2023-03-29)
Field: Chemistry — Inorganic Chemistry

Study on the preparation of C4 olefins by ethanol coupling based on multiple regression analysis With the continuous development of the current chemical industry, the demand for olefins as raw materials for the production of chemical products and pharmaceutical intermediates is increasing. Compared with traditional fossil energy, ethanol is an excellent green raw material for the production of C4 olefins. In the preparation process, the selectivity of C4 olefins and the conversion rate of ethanol depend to a certain extent on the reaction temperature and the catalyst of the reaction, so the reasonable selection of catalyst and temperature becomes very important. In order to study the effects of different catalyst combinations and temperatures on ethanol conversion and C4 olefin selectivity, a multiple regression model was established and its endogeneity was tested by Monte Carlo simulation. The stepwise regression method was used to select the appropriate independent variables, and then the model was optimized to reduce the dimension, and the first optimized linear regression equation was obtained. Finally, in order to test and eliminate the heteroscedasticity of the model, this paper uses the combination of White test and Wls least squares estimation and robust variance error to obtain the second optimized linear regression equation. Linear regression ; Regression analysis ; Partial least squares regression ; Regression ; Catalysis ; Heteroscedasticity ; Raw material ; Chemistry ; Mathematics ; Econometrics ; Applied mathematics ; Statistics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Mariko Kojima | Satoshi Abe | Tadaomi Furuta | Kunio Hirata | Xinchen Yao | Ayako Kobayashi | Ririko Kobayashi | Takafumi Ueno
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-12-19)
Field: Materials Science — Materials Chemistry

High-throughput structure determination of an intrinsically disordered protein using cell-free protein crystallization Abstract Intrinsically disordered proteins (IDPs) play a crucial role in various biological phenomena, dynamically changing their conformations in response to external environmental cues. To gain a deeper understanding of these proteins, it is essential to identify the determinants that fix their structures at the atomic level. Here, we developed a pipeline for rapid crystal structure analysis of IDP using a cell-free protein crystallization (CFPC) method. Through this approach, we successfully demonstrated the determination of the structure of an IDP to uncover the key determinants that stabilize its conformation. Specifically, we focused on the 11-residue fragment of c-Myc, which forms an α-helix through dimerization with a binding partner protein. This fragment was strategically fused with an in-cell crystallizing protein and was expressed in a cell-free system. The resulting crystal structures of the c-Myc fragment were successfully determined at a resolution of 1.92 Å and we confirmed that they are identical to the structures of the complex with the native binding partner protein. This indicates that the environment of the scaffold crystal can fix the structure of c-Myc. Significantly, these crystals were obtained directly from a small reaction mixture (30 μL) incubated for only 72 hours. Analysis of 8 crystal structures derived from 22 mutants revealed two hydrophobic residues as the key determinants responsible for stabilizing the α-helical structure. These findings underscore the power of our CFPC screening method as a valuable tool for determining the structures of challenging target proteins and elucidating the essential molecular interactions that govern their stability. Crystallization ; Protein crystallization ; Crystallography ; Protein structure ; Chemistry ; Residue (chemistry) ; Biophysics ; Crystal structure ; Mutant ; Biochemistry ; Biology ; Gene Enzyme Structure and Function ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Kayla Chun | William E. Bentley
Source: Biophysical Journal (article, 2023-02-01)
Field: Chemistry — Electrochemistry

Electrochemical classification and quantification of biologics using cyclic voltammetry and machine learning Cyclic voltammetry ; Electrochemistry ; Voltammetry ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Chemistry ; Materials science ; Electrode ; Physical chemistry Electrochemical Analysis and Applications ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Zhiyuan Du | Madhav Gupta | Feng Xu | Kai Zhang | Jiahua Zhang | Yan Zhou | Yiyao Liu | Zhenyu Wang | Jörg Wrachtrup | Ngai Wong | Can Li | Zhiqin Chu
Source: arXiv (Cornell University) (preprint, 2023-06-25)
Field: Materials Science — Materials Chemistry

High-precision and low-latency widefield diamond quantum sensing with neuromorphic vision sensors During the past decade, interest has grown significantly in developing ultrasensitive widefield diamond magnetometry for various applications. Despite attempts to improve the adoption of conventional frame-based sensors, achieving high temporal resolution and sensitivity simultaneously remains a key challenge. This is largely due to the transfer and processing of massive amounts of sensor data to capture the widefield fluorescence intensity changes of spin defects in diamonds. In this study, we adopt a neuromorphic vision sensor to address this issue. This sensor pre-processes the detected signals in optically detected magnetic resonance (ODMR) measurements for quantum sensing, employing a working principle that closely resembles the operation of the human vision system. By encoding the changes of light intensity into spikes, this approach results in a vast dynamic range, high temporal resolution, and exceptional signal-to-background ratio. After a thorough evaluation of theoretical feasibility, our experiment with an off-the-shelf event camera demonstrated a 13x improvement in temporal resolution with comparable precision of detecting ODMR resonance frequencies compared with the state-of-the-art highly specialized frame-based approach. A specialized camera system with the same mechanism has the potential to enhance these benefits further. This performance improvement is primarily attributable to orders of magnitude smaller data volumes and, thus, reduced latency. We further showcase the deployment of this technology in monitoring dynamically modulated laser heating of gold nanoparticles coated on a diamond surface, a recognizably difficult task using existing approaches. The current development provides new insights for high-precision and low-latency widefield quantum sensing, with possibilities for integration with emerging memory devices for more efficient event-based data processing. Neuromorphic engineering ; Computer science ; Temporal resolution ; Quantum sensor ; Frame rate ; Latency (audio) ; Low latency (capital markets) ; Biophotonics ; Optoelectronics ; Materials science ; Optics ; Physics ; Quantum ; Laser ; Artificial intelligence ; Quantum computer ; Telecommunications ; Artificial neural network Diamond and Carbon-based Materials Research ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing