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Development and characterization of carbon-based conductive pastes with high mechanical integrity under bending stress for room-temperature printable electronics
Abstract Room temperature processing of flexible electronics has become of great interest, as it allows for simpler and cheaper methodologies for high throughput manufacturing of printed electronics. This study focuses on the development and characterization of carbon-based conductive pastes made from a combination of graphite (G) and carbon black (CB), in a polymethyl methacrylate (PMMA) polymer matrix. Raw materials were characterized by Raman Spectroscopy, FTIR, SEM and TEM, showing the structural properties, morphologies and particles size which influenced the characteristics of the pastes. By varying the ratios of G/CB (1 to 4), carbon filler content (11.6–20%), and polymer content (1.5–7%), 48 different formulations were fabricated and further analyzed to determine their electrical conductivity as films. This process identified the optimal formulation for each G/CB ratio. Pastes with higher relative graphite content (G/CB ratios of 3 and 4) yielded the lowest resistivities (as low as 0.078 Ω cm) attributed to the effective formation of conductive networks between G and CB. Best-performing pastes were further characterized by sheet resistance, viscosity, adhesion, and scanning electron microscopy (SEM) analysis to understand the microstructure of the films. Flexible electrodes fabricated on PET substrates withstood 6000 bending cycles, thermal stress at 70 °C, and immersion in water, maintaining electrical conductivity. These results have significant implications for the future development of carbon-based conductive materials for room-temperature applications in flexible and printed electronics.
Extraction of mango seed kernels via super fluid extraction and their anti-H. pylori, anti-ovarian and anti prostate cancer properties
Machine learning based seizure classification and digital biosignal analysis of ECT seizures
Abstract While artificial intelligence has received considerable attention in various medical fields, its application in the field of electroconvulsive therapy (ECT) remains rather limited. With the advent of digital seizure collection systems, the development of novel ECT seizure quality metrics and treatment guidance systems in particular will require cutting-edge digital seizure analysis. Using artificial intelligence will offer more analytical degrees of freedom and could play a key role in enhancing the precision of currently available procedures. To this end, we developed the first machine learning (ML) framework that can classify ictal and non-ictal EEG segments, accurately identifying seizure endpoints—a critical step in deriving seizure quality parameters—and computing these metrics at least as reliable as existing precomputed scores. The ML model retained in this study effectively discriminated ictal from non-ictal EEG segments with 89% accuracy, precision, and sensitivity. The reproduced ECT quality parameters showed correlations up to ϱ = 0.99 (p < 0.01) with the pre-calculated values from the stimulation device and did not significantly differ from the reference values. Mean seizure duration differences were 0.23 ± 15.59 s compared to the expert rater and 0.28 ± 16.19 s compared to the stimulation device. The study highlights the potential of integrating ML into the field of ECT and emphasizes the critical role of a highly sensitive seizure detection method in reliably determining seizure duration and deriving subsequent quality indices, paving the way for more individualized treatment strategies and novel approaches to determine seizure quality.
Comparative study of third-generation sequencing-based CASMA-trio and STR linkage analysis for identifying SMN1 2 + 0 carriers
An efficient multi-objective framework for wireless sensor network using machine learning
Optimizing wind turbine blade pitch control via input output differential model free adaptive control
Prevalence of erectile dysfunction as long-COVID symptom in hospitalized Japanese patients
Selection and validation of reference genes in alfalfa based on transcriptome sequence data
A multiple correspondence analysis of the fear of falling, sociodemographic, physical and mental health factors in older adults
Low lying excited states quantum entanglement and continuous quantum phase transitions
Detecting the physiological and molecular mechanisms by which abscisic acid (ABA) regulates the consistency of sweet cherry fruit maturity
Aflatoxin B1 (AFB1) biodegradation by a lignolytic phenoloxidase of Trametes hirsuta
Abstract Aflatoxin B1 (AFB1) is a highly potent mycotoxin that poses a serious threat to human and animal health. This study investigated the biodegradation of AFB1 by the supernatant of submerged cultured Trametes hirsuta, with a focus on identifying and characterizing the responsible enzyme(s). The extracellular enzymes of the white-rot mushroom were extracted from the supernatant and pre-separated using anion exchange fast protein liquid chromatography (FPLC). To pinpoint the specific enzyme, the eluted protein fractions exhibiting the highest degradation activity were subjected to detailed biochemical and proteomic analyses. A second purification step, ultrafiltration, yielded an electrophoretically pure enzyme. Sequencing of tryptic peptides using a nano-LC system coupled to a qQTOF mass spectrometer identified the enzyme as a lignolytic phenoloxidase. The enzyme exhibited a molecular mass of 55.6 kDa and achieved an impressive AFB1 degradation rate of 77.9% under optimized experimental conditions. This is the first fungal lignolytic phenoloxidase capable of aflatoxin degradation without requiring hydrogen peroxide as a cofactor, highlighting its unique catalytic mechanism. It may be used in mycotoxin remediation strategies, such as treating the surfaces of contaminated fruits, vegetables, and nuts.
Phosphorus doped few layer WS2 flakes grown by chemical vapor deposition for hydrogen evolution reactions
Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders
Abstract Biomedical discovery is fraught with challenges stemming from diverse data types and siloed analysis. In this study, we explored common biomedical data tasks and pain points that could be addressed to elevate data quality, enhance sharing, streamline analysis, and foster collaboration across stakeholders. We recruited fifteen professionals from various biomedical roles and industries to participate in sixty-minute semi-structured interviews, which involved an assessment of their challenges, needs, and tasks as well as a brainstorm exercise to validate each professional’s research process. We applied a qualitative analysis of individual interviews using an inductive-deductive thematic coding approach for emerging themes. We identified a common set of challenges related to procuring and validating data, applying new analysis techniques and navigating varied computational environments, distributing results effectively and reproducibly, and managing the flow of data across phases of the data lifecycle. Our findings emphasize the importance of secure data sharing and facilities for collaboration throughout the discovery process. Our identified pain points provide researchers with an opportunity to align workstreams and enhance research data lifecycles to conduct biomedical discovery. We conclude our study with a summary of key actionable recommendations to tackle multiomic data challenges across the stages and phases of biomedical discovery.
On the build-up of effective hyperuniformity from large globular colloidal aggregates
A simple three-dimensional model of a fluid whose constituent particles interact via a short range attractive and long range repulsive potential is used to model the aggregation into large spherical-like clusters made up of hundreds of particles. The model can be thought of as a straightforward rendition of colloid flocculation into large spherical aggregates. We illustrate how temperature and particle density influence the cluster size distribution and affect inter- and intra-cluster dynamics. The system is shown to exhibit two well separated length and time scales, which can be tuned by the balance between repulsive and attractive forces. Interestingly, cluster aggregates at moderate/low temperatures approach a cluster glassy phase, whereas cluster particles retain a local liquid-like structure. These states present a strong suppression of density fluctuations for a significant range of relatively large wavelengths, meeting the criterion of effective disordered hyperuniform materials as far as the intercluster structure is concerned.
Effects of organic salts of virucidal and antiviral compounds from Nelumbo nucifera and Kaempferia parviflora against SARS-CoV-2
Large-scale sparse wave function circuit simulator for applications with the variational quantum eigensolver
The standard paradigm for state preparation on quantum computers for the simulation of physical systems in the near term has been widely explored with different algorithmic methods. One such approach is the optimization of parameterized circuits, but this becomes increasingly challenging with circuit size. As a consequence, the utility of large-scale circuit optimization is relatively unknown. In this work we demonstrate that purely classical resources can be used to optimize quantum circuits in an approximate but robust manner such that we can bridge the resources that we have from high performance computing and see a direct transition to quantum advantage. We show this through the sparse wave function circuit solvers, which we detail here, and demonstrate a region of efficient classic simulation. With such tools, we can avoid the many problems that plague circuit optimization for circuits with hundreds of qubits using only practical and reasonable classical computing resources. These tools allow us to probe the true benefit of variational optimization approaches on quantum computers, thus opening the window to what can be expected with near term hardware for physical systems. We demonstrate this with a unitary coupled cluster ansatz on various molecules up to 64 qubits with tens of thousands of variational parameters.
Early warning study of field station process safety based on VMD-CNN-LSTM-self-attention for natural gas load prediction
Temperature and pressure effects on the surface structure of liquid gallium
Liquid gallium exhibits a unique metallic-covalent coexistence. Leveraging the volume constant pressure molecular dynamics method and a well-trained neural network potential, we study the evolution of liquid Ga surface structures under varying temperatures and pressures. Our study presents a schematic P–T phase diagram of the liquid surface. We observe symmetric static structure factor main peaks in the outermost layers of the liquid Ga surface compared with asymmetric ones for inner layers, indicating a simple liquid behavior and a lack of Ga2 dimers at the surface. We calculate the surface energy and the surface tension, which reveal non-monotonic changes. All these results provide a further insight into understanding the physics of the strange metal gallium.