jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Johannes Gedeon | Emadeldeen Hassan | Antonio Calà Lesina
Source: ACS Photonics (article, 2023-10-26)
Field: Materials Science — Surfaces, Coatings and Films

Time-Domain Topology Optimization of Arbitrary Dispersive Materials for Broadband 3D Nanophotonics Inverse Design In the last decades, nanostructures have unlocked myriads of functionalities in nanophotonics by engineering light-matter interaction beyond what is possible with conventional bulk optics. The space of parameters available for design is practically unlimited due to the large variety of optical materials and nanofabrication techniques. Thus, computational approaches are necessary to efficiently search for the optimal solutions. In this paper, we enable the free-form inverse design in 3D of linear optical materials with arbitrary dispersion and anisotropy. This is achieved by (1) deriving an analytical adjoint scheme based on the complex-conjugate pole-residue pair model in the time domain and (2) its implementation in a parallel finite-difference time-domain framework with a topology optimization routine, efficiently running on high-performance computing systems. Our method is tested on the design problem of field confinement using dispersive nanostructures. The obtained designs satisfy the fundamental curiosity of how free-form metallic and dielectric nanostructures perform when optimized in 3D, also in comparison to fabrication-constrained designs. Unconventional free-form designs revealed by computational methods, although may be challenging or unfeasible to realize with current technology, bring new insights into how light can more efficiently interact with nanostructures and provide new ideas for forward design. Nanophotonics ; Nanolithography ; Computer science ; Inverse ; Inverse problem ; Broadband ; Topology (electrical circuits) ; Materials science ; Nanotechnology ; Telecommunications ; Mathematics ; Geometry Optical Coatings and Gratings ; Photonic and Optical Devices ; Photonic Crystals and Applications
Authors: Yongli Duan | Miaomiao Song | Fanxi Sun | Yi Xu | Fanfan Shi | Hong Wang | Yonghao Zheng | Chao He | Xilin Liu | Wei Chen | Xu Deng | Longquan Chen | Fucai Liu | Dongsheng Wang
Source: Advanced Science (article, 2023-03-11)
Field: Materials Science — Materials Chemistry

Controlling Isomerization of Photoswitches to Modulate 2D Logic‐in‐Memory Devices by Organic–Inorganic Interfacial Strategy Logic-in-memory devices are a promising and powerful approach to realize data processing and storage driven by electrical bias. Here, an innovative strategy is reported to achieve the multistage photomodulation of 2D logic-in-memory devices, which is realized by controlling the photoisomerization of donor-acceptor Stenhouse adducts (DASAs) on the surface of graphene. Alkyl chains with various carbon spacer lengths (n = 1, 5, 11, and 17) are introduced onto DASAs to optimize the organic-inorganic interfaces: 1) Prolonging the carbon spacers weakens the intermolecular aggregation and promotes isomerization in the solid state. 2) Too long alkyl chains induce crystallization on the surface and hinder the photoisomerization. Density functional theory calculation indicates that the photoisomerization of DASAs on the graphene surface is thermodynamically promoted by increasing the carbon spacer lengths. The 2D logic-in-memory devices are fabricated by assembling DASAs onto the surface. Green light irradiation increases the drain-source current (I<sub>ds</sub> ) of the devices, while heat triggers a reversed transfer. The multistage photomodulation is achieved by well-controlling the irradiation time and intensity. The strategy based on the dynamic control of 2D electronics by light integrates molecular programmability into the next generation of nanoelectronics. Photoisomerization ; Materials science ; Isomerization ; Graphene ; Nanotechnology ; Alkyl ; Optoelectronics ; Chemistry ; Catalysis ; Organic chemistry Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Advanced Memory and Neural Computing
Authors: Amjed Abdullah | Akshay Kumar Reddy Koppula | Omar Alkorjia | Mahmoud Almasri
Source: Scientific Reports (article, 2023-03-01)
Field: Materials Science — Polymers and Plastics

Uncooled two-microbolometer stack for long wavelength infrared detection We have investigated an uncooled infrared (IR) detector utilizing a dual level architecture. This was achieved by combining two-microbolometer stack in the vertical direction to achieve high IR absorption over two distinct spectral windows across the long wavelength infrared region (LWIR). In addition, we have studied amorphous silicon germanium oxide (Si<sub>x</sub>Ge<sub>y</sub>O<sub>1-x-y</sub>) as an IR sensitive material, and metasurface to control IR absorption/reflection in interaction with standard Fabry-Perot cavity. The bottom microbolometer uses a metasurface to selectively absorbs a portion of the spectrum and reflects radiation outside this window range. At the same time, the top microbolometer uses a conventional Fabry-Perot resonant cavity to absorb a different portion of the spectrum and transmit any unabsorbed radiation outside this window. This device can be used to measure the absolute temperature of an object by comparing the relative signals in the two spectral bands. The spectral responsivity and detectivity, and thermal response time were > 10<sup>5</sup> V/W, > 10<sup>8</sup> cm Hz<sup>1/2</sup>/W, and 1.13 ms to filtered blackbody infrared radiation between (2-16) µm. The microbolometer voltage noise power spectral density was reduced by annealing the microbolometers in vacuum at 300 °C. Microbolometer ; Infrared ; Bolometer ; Materials science ; Optoelectronics ; Optics ; Responsivity ; Thermal infrared spectroscopy ; Radiation ; Detector ; Photodetector ; Physics Transition Metal Oxide Nanomaterials ; Polydiacetylene-based materials and applications ; Gas Sensing Nanomaterials and Sensors
Authors: Gerard Baquer | Lluc Sementé | Pere Ràfols | Lucía Martín‐Saíz | Christoph Bookmeyer | José A. Fernández | Xavier Correig | María García‐Altares
Source: Journal of Cheminformatics (article, 2023-09-15)
Field: Chemistry — Spectroscopy

rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) spatially resolves the chemical composition of tissues. Lipids are of particular interest, as they influence important biological processes in health and disease. However, the identification of lipids in MALDI-MSI remains a challenge due to the lack of chromatographic separation or untargeted tandem mass spectrometry. Recent studies have proposed the use of MALDI in-source fragmentation to infer structural information and aid identification. Here we present rMSIfragment, an open-source R package that exploits known adducts and fragmentation pathways to confidently annotate lipids in MALDI-MSI. The annotations are ranked using a novel score that demonstrates an area under the curve of 0.7 in ROC analyses using HPLC-MS and Target-Decoy validations. rMSIfragment applies to multiple MALDI-MSI sample types and experimental setups. Finally, we demonstrate that overlooking in-source fragments increases the number of incorrect annotations. Annotation workflows should consider in-source fragmentation tools such as rMSIfragment to increase annotation confidence and reduce the number of false positives. Fragmentation (computing) ; Annotation ; Mass spectrometry ; Computer science ; Lipidomics ; False positive paradox ; Mass spectrometry imaging ; Tandem mass spectrometry ; Computational biology ; Matrix-assisted laser desorption/ionization ; Identification (biology) ; Chromatography ; Workflow ; Chemistry ; Desorption ; Artificial intelligence ; Database ; Biology ; Biochemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Jonas Pangerl | Elisabeth Moser | Max Müller | Stefan Weigl | Simon Jobst | Thomas Rück | Rudolf Bierl | Frank‐Michael Matysik
Source: Photoacoustics (article, 2023-03-15)
Field: Chemistry — Spectroscopy

A sub-ppbv-level Acetone and Ethanol Quantum Cascade Laser Based Photoacoustic Sensor – Characterization and Multi-Component Spectra Recording in Synthetic Breath Trace gas analysis in breath is challenging due to the vast number of different components. We present a highly sensitive quantum cascade laser based photoacoustic setup for breath analysis. Scanning the range between 8263 and 8270 nm with a spectral resolution of 48 pm, we are able to quantify acetone and ethanol within a typical breath matrix containing water and CO<sub>2</sub>. We photoacoustically acquired spectra within this region of mid-infra-red light and prove that those spectra do not suffer from non-spectral interferences. The purely additive behavior of a breath sample spectrum was verified by comparing it with the independently acquired single component spectra using Pearson and Spearman correlation coefficients. A previously presented simulation approach is improved and an error attribution study is presented. With a 3σ detection limit of 6.5 ppbv in terms of ethanol and 250 pptv regarding acetone, our system is among the best performing presented so far. Quantum cascade laser ; Spectral line ; Photoacoustic imaging in biomedicine ; Laser ; Detection limit ; Analytical Chemistry (journal) ; Cascade ; Materials science ; Breath gas analysis ; Acetone ; Spectral resolution ; Trace gas ; Optics ; Chemistry ; Physics ; Chromatography ; Meteorology Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Sensors
Authors: Jingjing Si | Gengchen Fu | Xin Liu | Yinbo Cheng | Rui Zhang | Jiangnan Xia | Yalei Fu | Godwin Enemali | Chang Liu
Source: IEEE Transactions on Industrial Informatics (article, 2023-01-31)
Field: Chemistry — Spectroscopy

A Spatially Progressive Neural Network for Locally/Globally Prioritized TDLAS Tomography Tunable diode laser absorption spectroscopy tomography (TDLAST) has been widely applied for imaging two-dimensional distributions of industrial flow-field parameters, e.g., temperature and species concentration. Two main interested imaging objectives in TDLAST are the local combustion and its radiation in the entire sensing region. State-of-the-art algorithms were developed to retrieve either of the two objectives. In this article, we address both by developing a novel multioutput imaging neural network, named as spatially progressive neural network (SpaProNet). This network consists of locally and globally prioritized reconstruction stages. The former enables hierarchical imaging of the finely resolved and highly accurate local combustion, but coarsely resolved background. The latter retrieves a fine-resolved image for the entire sensing region, at the cost of slightly trading off the reconstruction accuracy in the combustion zone. Furthermore, the proposed network is driven by the hydrodynamics of the real reactive flows, in which the training dataset is obtained from large eddy simulation. The proposed SpaProNet is validated by both simulation and lab-scale experiment. In all test cases, the visual and quantitative metric comparisons show that the proposed SpaProNet outperforms the existing methods from the following two perspectives: 1) the locally prioritized stage provides ever-better accuracy in the combustion zone; and 2) the globally prioritized stage shows turbulence-indicative accuracy in the entire sensing region for diagnosis of heat radiation from the flame and flame-air interactions. Combustion ; Metric (unit) ; Artificial neural network ; Computer science ; Tunable diode laser absorption spectroscopy ; Stage (stratigraphy) ; Artificial intelligence ; Remote sensing ; Laser ; Optics ; Tunable laser ; Physics ; Engineering ; Geology Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Air Quality Monitoring and Forecasting
Authors: Kittiya Plaeyao | Ratchaneekorn Kampangta | Yuparat Korkokklang | Chanon Talodthaisong | Apichart Saenchoopa | Saengrawee Thammawithan | Krailikhit Latpala | Rina Patramanon | Navaphun Kayunkid | Sirinan Kulchat
Source: RSC Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Gingerol extract-stabilized silver nanoparticles and their applications: colorimetric and machine learning-based sensing of Hg(<scp>ii</scp>) and antibacterial properties This study focused on synthesizing ginger-stabilized silver nanoparticles (Gin-AgNPs) using a more eco-friendly method that utilized AgNO<sub>3</sub> and natural ginger solution. These nanoparticles underwent a color change from yellow to colorless when exposed to Hg<sup>2+</sup>, enabling the detection of Hg<sup>2+</sup> in tap water. The colorimetric sensor had good sensitivity, with a limit of detection (LOD) of 1.46 μM and a limit of quantitation (LOQ) of 3.04 μM. Importantly, the sensor operated accurately without being affected by various other metal ions. To enhance its performance, a machine learning approach was employed and achieved accuracy ranging from 0% to 14.66% when trained with images of Gin-AgNP solutions containing different Hg<sup>2+</sup> concentrations. Furthermore, the Gin-AgNPs and Gin-AgNPs hydrogels exhibited antibacterial effects against both Gram-negative and Gram-positive bacteria, indicating potential future applications in the detection of Hg<sup>2+</sup> and in wound healing. Silver nanoparticle ; Detection limit ; Chemistry ; Antibacterial activity ; Tap water ; Metal ions in aqueous solution ; Nanoparticle ; Nuclear chemistry ; Nanotechnology ; Metal ; Chromatography ; Materials science ; Bacteria ; Organic chemistry Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis
Authors: S. Quondam Antonio | Francesco Riganti Fulginei | Antonino Laudani | Gabriele Maria Lozito | Riccardo Scorretti
Source: Engineering Applications of Artificial Intelligence (article, 2023-02-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep neural networks for the efficient simulation of macro-scale hysteresis processes with generic excitation waveforms An effective and performing hysteresis model, based on a deep neural network, with the capability to reproduce the evolution of magnetization processes under arbitrary waveforms of excitation is here presented. The proposed model consists of a standalone multi-layer feed-forward neural network, with reserved input neurons for the past values of both the input (H) and output (M), aiming at the reproduction of the storage mechanism typical of hysteretic systems. The training set has been opportunely prepared starting from a set of simulations, performed by the Preisach hysteresis model. The optimized training procedure, based on multi-stage check of the model performance, will be comprehensively discussed. The comparative analysis between the neural network-based model, implemented at low level of abstraction, and the Preisach model covers additional hysteresis processes, different from those involved in the training. The mild/moderate memory requirement and the significant computational speed make the proposed approach suitable for a future coupling with finite-element analysis. Computer science ; Artificial neural network ; Hysteresis ; Waveform ; Abstraction ; Set (abstract data type) ; Coupling (piping) ; Excitation ; Algorithm ; Control theory (sociology) ; Artificial intelligence ; Materials science ; Physics Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Microstructure and Mechanical Properties of Steels
Authors: Elizaveta N. Mochalova | E. A. Egorova | Kristina S. Komarova | Victoria O. Shipunova | Nelli F. Khabibullina | Petr I. Nikitin | Maxim P. Nikitin
Source: International Journal of Molecular Sciences (article, 2023-06-25)
Field: Materials Science — Biomaterials

Comparative Study of Nanoparticle Blood Circulation after Forced Clearance of Own Erythrocytes (Mononuclear Phagocyte System-Cytoblockade) or Administration of Cytotoxic Doxorubicin- or Clodronate-Loaded Liposomes Recent developments in the field of nanomedicine have introduced a wide variety of nanomaterials that are capable of recognizing and killing tumor cells with increased specificity. A major limitation preventing the widespread introduction of nanomaterials into the clinical setting is their fast clearance from the bloodstream via the mononuclear phagocyte system (MPS). One of the most promising methods used to overcome this limitation is the MPS-cytoblockade, which forces the MPS to intensify the clearance of erythrocytes by injecting allogeneic anti-erythrocyte antibodies and, thus, significantly prolongs the circulation of nanoagents in the blood. However, on the way to the clinical application of this approach, the question arises whether the induced suppression of macrophage phagocytosis via the MPS-cytoblockade could pose health risks. Here, we show that highly cytotoxic doxorubicin- or clodronate-loaded liposomes, which are widely used for cancer therapy and biomedical research, induce a similar increase in the nanoparticle blood circulation half-life in mice as the MPS-cytoblockade, which only gently and temporarily saturates the macrophages with the organism's own erythrocytes. This result suggests that from the point of view of in vivo macrophage suppression, the MPS-cytoblockade should be less detrimental than the liposomal anti-cancer drugs that are already approved for clinical application while allowing for the substantial improvement in the nanoagent effectiveness. Mononuclear phagocyte system ; Phagocytosis ; Doxorubicin ; Liposome ; Phagocyte ; Macrophage ; Pharmacology ; Peripheral blood mononuclear cell ; Cytotoxic T cell ; In vivo ; Medicine ; Cancer cell ; Nanomedicine ; Chemistry ; Microvesicles ; Nanotoxicology ; Cancer ; Immune system ; Immunology ; Toxicity ; Biology ; In vitro ; Chemotherapy ; Nanotechnology ; Nanoparticle ; Biochemistry ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Erythrocyte Function and Pathophysiology ; Phagocytosis and Immune Regulation
Authors: Lia Vasconcelos | Luís G. Dias | Ana Leite | Iasmin Ferreira | Etelvina Pereira | Severiano Silva | Sandra Rodrigues | A. Teixeira
Source: Foods (article, 2023-01-19)
Field: Chemistry — Analytical Chemistry

SVM Regression to Assess Meat Characteristics of Bísaro Pig Loins Using NIRS Methodology This study evaluates the ability of the near infrared reflectance spectroscopy (NIRS) to estimate the aW, protein, moisture, ash, fat, collagen, texture, pigments, and WHC in the <i>Longissimus thoracis et lumborum</i> (LTL) of Bísaro pig. Samples (<i>n</i> = 40) of the LTL muscle were minced and scanned in an FT-NIR MasterTM N500 (BÜCHI) over a NIR spectral range of 4000-10,000 cm<sup>-1</sup> with a resolution of 4 cm<sup>-1</sup>. The PLS and SVM regression models were developed using the spectra's math treatment, DV1, DV2, MSC, SNV, and SMT (<i>n</i> = 40). PLS models showed acceptable fits (estimation models with RMSE ≤ 0.5% and R<sup>2</sup> ≥ 0.95) except for the RT variable (RMSE of 0.891% and R<sup>2</sup> of 0.748). The SVM models presented better overall prediction results than those obtained by PLS, where only the variables pigments and WHC presented estimation models (respectively: RMSE of 0.069 and 0.472%; R<sup>2</sup> of 0.993 and 0.996; slope of 0.985 ± 0.006 and 0.925 ± 0.006). The results showed NIRs capacity to predict the meat quality traits of Bísaro pig breed in order to guarantee its characterization. Longissimus Thoracis ; Mathematics ; Linear regression ; Food science ; Animal science ; Chemistry ; Statistics ; Biology ; Tenderness Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Animal Nutrition and Physiology
Authors: Ruben De Blaere | Kévin Lievens | Dieter Hassel | Victor Deklerck | Tom De Mil | Wannes Hubau | Joris Van Acker | Nils Bourland | Jan Verwaeren | Jan Van den Bulcke | Hans Beeckman
Source: Database (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

SmartWoodID—an image collection of large end-grain surfaces to support wood identification systems Wood identification is a key step in the enforcement of laws and regulations aimed at combatting illegal timber trade. Robust wood identification tools, capable of distinguishing a large number of timbers, depend on a solid database of reference material. Reference material for wood identification is typically curated in botanical collections dedicated to wood consisting of samples of secondary xylem of lignified plants. Specimens from the Tervuren Wood Collection, one of the large institutional wood collections around the world, are used as a source of tree species data with potential application as timber. Here, we present SmartWoodID, a database of high-resolution optical scans of the end-grain surfaces enriched with expert wood anatomical descriptions of macroscopic features. These can serve as annotated training data to develop interactive identification keys and artificial intelligence for computer vision-based wood identification. The first edition of the database consists of images of 1190 taxa, with a focus on potential timber species from the Democratic Republic of the Congo with at least four different specimens per species included. Database URL https://hdl.handle.net/20.500.12624/SmartWoodID_first_edition. Identification (biology) ; Database ; Computer science ; Biology ; Botany Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Plant and Fungal Species Descriptions
Authors: Zhujun Wang | Xuyuan Tao | Xianyi Zeng | Yingmei Xing | Zhenzhen Xu | Pascal Bruniaux
Source: Sustainability (article, 2023-04-04)
Field: Materials Science — Polymers and Plastics

A Machine Learning-Enhanced 3D Reverse Design Approach to Personalized Garments in Pursuit of Sustainability The fashion industry is facing increasing pressure to move toward sustainable development, especially with concern to cost and environmental sustainability. Innovative digital technologies are regarded as a promising solution for fashion companies to resolve this issue. In this context, this paper put forth a new 3D reverse garment design approach embedded with a garment fit prediction and structure self-adaptive adjustment mechanism, using machine learning (ML) techniques. Initially, the 3D basic garment was drawn directly on the scanned mannequin of a specific consumer. Next, a probabilistic neural network (PNN) was employed to predict the garment’s fit. Afterwards, genetic algorithms (GA) and support vector regression (SVR) were utilized to estimate and control the garment structural parameters following the feedback of fit evaluation and the consumer’s personalized needs. Meanwhile, a comprehensive evaluation was constructed to characterize the quantitative relationships between the consumer profile and the designed garment profile (garment fit and styles). Ultimately, the desired garment which met the consumer’s needs was obtained by performing the routine of “design–fit evaluation–pattern adjustment–comprehensive evaluation”, iteratively. The experimental results show that the proposed approach provides a new solution to develop quality personalized fashion products (garments) more accurately, economically, and in an environmentally friendly way. It is feasible to facilitate the sustainable development of fashion companies by simultaneously reducing costs and negative impacts on the environment. Sustainability ; Clothing ; Context (archaeology) ; Genetic algorithm ; Computer science ; Probabilistic logic ; Quality (philosophy) ; Engineering ; Industrial engineering ; Manufacturing engineering ; Artificial intelligence ; Machine learning Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Color perception and design