Electrochemically active porous carbon nanospheres prepared by inhibition of pyrolytic condensation of polymers.

Porous carbon is a pivotal material for electrochemical applications. The manufacture of porous carbon has relied on chemical treatments (etching or template) that require processing in all areas of the carbon/carbon precursor. We present a unique approach to preparing porous carbon nanospheres by inhibiting the pyrolytic condensation of polymers. Specifically, the porous carbon nanospheres are obtained by coating a thin film of ZnO on polystyrene spheres. The porosity of the porous carbon nanospheres is controlled by the thickness of the ZnO shell, achieving a BET-specific area of 1,124 m2/g with a specific volume of 1.09 cm3/g. We confirm that under the support force by the ZnO shell, a hierarchical pore structure in which small mesopores are connected by large mesopores is formed and that the pore-associated sp3 defects are enriched. These features allow full utilization of the surface area of the carbon pores. The electrochemical capacitive performance of porous carbon nanospheres was evaluated, achieving a high capacitance of 389 F/g at 1 A/g, capacitance retention of 71% at a 20-fold increase in current density, and stability up to 30,000 cycles. In particular, we achieve a specific area-normalized capacitance of 34.6 µF/cm2, which overcomes the limitations of conventional carbon materials.

Application of Transcriptome-Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic Cancer.

Identifying the primary site of origin of metastatic cancer is vital for guiding treatment decisions, especially for patients with cancer of unknown primary (CUP). Despite advanced diagnostic techniques, CUP remains difficult to pinpoint and is responsible for a considerable number of cancer-related fatalities. Understanding its origin is crucial for effective management and potentially improving patient outcomes. This study introduces a machine learning framework, ONCOfind-AI, that leverages transcriptome-based gene set features to enhance the accuracy of predicting the origin of metastatic cancers. We demonstrate its potential to facilitate the integration of RNA sequencing and microarray data by using gene set scores for characterization of transcriptome profiles generated from different platforms. Integrating data from different platforms resulted in improved accuracy of machine learning models for predicting cancer origins. We validated our method using external data from clinical samples collected through the Kangbuk Samsung Medical Center and Gene Expression Omnibus. The external validation results demonstrate a top-1 accuracy ranging from 0.80 to 0.86, with a top-2 accuracy of 0.90. This study highlights that incorporating biological knowledge through curated gene sets can help to merge gene expression data from different platforms, thereby enhancing the compatibility needed to develop more effective machine learning prediction models.

Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome.

BACKGROUND: In case of focal neuropathy, the muscle fibers innervated by the corresponding nerves are replaced with fat or fibrous tissue due to denervation, which results in increased echo intensity (EI) on ultrasonography. EI analysis can be conducted quantitatively using gray scale analysis. Mean value of pixel brightness of muscle image defined as EI. However, the accuracy achieved by using this parameter alone to differentiate between normal and abnormal muscles is limited. Recently, attempts have been made to increase the accuracy using artificial intelligence (AI) in the analysis of muscle ultrasound images. CTS is the most common disease among focal neuropathy. In this study, we aimed to verify the utility of AI assisted quantitative analysis of muscle ultrasound in CTS. METHODS: This is retrospective study that used data from adult who underwent ultrasonographic examination of hand muscles. The patient with CTS confirmed by electromyography and subjects without CTS were included. Ultrasound images of the unaffected hands of patients or subjects without CTS were used as controls. Ultrasonography was performed by one physician in same sonographic settings. Both conventional quantitative grayscale analysis and machine learning (ML) analysis were performed for comparison. RESULTS: A total of 47 hands with CTS and 27 control hands were analyzed. On conventional quantitative analysis, mean EI ratio (i.e. mean thenar EI/mean hypothenar EI ratio) were significantly higher in the patient group than in the control group, and the AUC was 0.76 in ROC analysis. In the analysis using machine learning, the AUC was the highest for the linear support vector classifier (AUC = 0.86). When recursive feature elimination was applied to the classifier, the AUC value improved to 0.89. CONCLUSION: This study showed a significant increase in diagnostic accuracy when AI was used for quantitative analysis of muscle ultrasonography. If an analysis protocol using machine learning can be established and mounted on an ultrasound machine, a noninvasive and non-time-consuming muscle ultrasound examination can be conducted as an ancillary tool for diagnosis.

Temperature- and Pressure-induced Structural Transition of Binary Clathrate Hydrates.

We discover new structure II (sII) hydrate forming agents of two C4 H8 O molecules (2-methyl-2-propen-1-ol and 2-butanone) and report the abnormal structural transition of binary C4 H8 O+CH4 hydrates between structure I (sI) and sII with varying temperature and pressure conditions. In both (2-methyl-2-propen-1-ol+CH4 ) and (2-butanone+CH4 ) systems, the phase boundary of the two different hydrate phases (sI and sII) exists at the slope change of the phase-equilibrium curve in the semi-logarithmic plots. We confirm the crystal structures of two hydrates synthesized at low (278 K and 6 MPa) and high (286 K and 15 MPa) temperature and pressure conditions by using high-resolution powder diffraction and Raman spectroscopy. 2-Methyl-2-propen-1-ol and 2-butanone can occupy the large cages of sII hydrate at low temperature and pressure conditions; however, they are excluded from the hydrate phase at high temperature and pressure conditions, resulting in the formation of pure sI CH4 hydrate.

Anti-Adhesive Behaviors between Solid Hydrate and Liquid Aqueous Phase Induced by Hydrophobic Silica Nanoparticles.

This study introduces an "anti-adhesive force" at the interface of solid hydrate and liquid solution phases. The force was induced by the presence of hydrophobic silica nanoparticles or one of the common anti-agglomerants (AAs), sorbitan monolaurate (Span 20), at the interface. The anti-adhesive force, which is defined as the maximum pushing force that does not induce the formation of a capillary bridge between the cyclopentane (CP) hydrate particle and the aqueous solution, was measured using a microbalance. Both hydrophobic silica nanoparticles and Span 20 can inhibit adhesion between the CP hydrate probe and the aqueous phase because silica nanoparticles have an aggregative property at the interface, and Span 20 enables the hydrate surface to be wetted with oil. Adding water-soluble sodium dodecyl sulfate (SDS) to the nanoparticle system cannot affect the aggregative property or the distribution of silica nanoparticles at the interface and, thus, cannot change the anti-adhesive effect. However, the combined system of Span 20 and SDS dramatically reduces the interfacial tension: emulsion drops were formed at the interface without any energy input and were adsorbed on the CP hydrate surface, which can cause the growth of hydrate particles. Silica nanoparticles have a good anti-adhesive performance with a relatively smaller dosage and are less influenced by the presence of molecular surfactants; consequently, these nanoparticles may have a good potential for hydrate inhibition as AAs.

How effective are universal payments for raising consumption? Evidence from a natural experiment.

We investigate the impact of the universal stimulus payments (100-350 thousand KRW per person) distributed by the largest Korean province of Gyeonggi during the COVID-19 pandemic on household consumption using large-scale credit and debit card data from Korea Credit Bureau. As the neighboring Incheon metropolitan city did not distribute stimulus payments, we employ a difference-in-difference approach and find that the stimulus payments increased monthly consumption per person by approximately 30 thousand KRW within the first 20 days. The overall marginal propensity to consume (MPC) of the payments was approximately 0.40 for single families. The MPC decreased from 0.58 to 0.36 as the transfer size increased from 100-150 to 300-350 thousand KRW. We also found that the effects of universal payments were very heterogeneous across different groups of people. The MPC for liquidity-constrained households, which account for 8% of all households, was close to one, but the MPCs of the other household groups were not significantly different from zero. The unconditional quantile treatment effect estimates reveal that there was a positive and significant increase in monthly consumption only in the lower part of the distribution below the median. Our results show that a more targeted approach may more efficiently achieve the policy goal of boosting aggregate demand.

Properties of flexible phosphorescence polymer light emitting diodes coated on polyethylenenaphthalate plastic substrates.

Flexible phosphorescence polymer light emitting diodes (PhPLEDs) with PEN/ITO/PEDOT:PSS/ PVK:Ir(ppy)3/TPBI/LiF/Al structure were fabricated to investigate the effects of Ir(ppy)3 doping concentrations on the optical and electrical properties of the devices. PVK and Ir(ppy)3 conjugated polymers as host and guest materials in the emission layer were spun coated at various concentrations of Ir(ppy)3 ranging from 2.0 to 8.0 vol%. As the concentration of Ir(ppy)3 increased from 2.0 to 6.0 vol%, the electrical and optical properties of the flexible PhPLEDs were improved clearly. Maximum luminance and current density were obtained for a PhPLED with an Ir(ppy)3 concentration of 6.0 vol%, with 6815 cd/m2 and 393 mA/cm2 at 9 V. The current efficiency tends to increase with the Ir(ppy)3 concentration, because of the formation of the excitons required to emit light.

Scale-attentional U-Net for the segmentation of the median nerve in ultrasound images.

PURPOSE: The aim of this study was to develop a neural network that accurately and effectively segments the median nerve in ultrasound (US) images. METHODS: In total, 1,305 images of the median nerve of 123 normal subjects were used to train and evaluate the model. Four datasets from two measurement regions (wrist and forearm) of the nerve and two US machines were used. The neural network was designed for high accuracy by combining information at multiple scales, as well as for high efficiency to prevent overfitting. The model was designed in two parts (cascaded and factorized convolutions), followed by selfattention over scale and channel features. The precision, recall, dice similarity coefficient (DSC), and Hausdorff distance (HD) were used as performance metrics. The area under the receiver operating characteristic curve (AUC) was also assessed. RESULTS: In the wrist datasets, the proposed network achieved 92.7% and 90.3% precision, 92.4% and 89.8% recall, DSCs of 92.3% and 89.7%, HDs of 5.158 and 4.966, and AUCs of 0.9755 and 0.9399 on two machines. In the forearm datasets, 79.3% and 87.8% precision, 76.0% and 85.0% recall, DSCs of 76.1% and 85.8%, HDs of 5.206 and 4.527, and AUCs of 0.8846 and 0.9150 were achieved. In all datasets, the model developed herein achieved better performance in terms of DSC than previous U-Net-based systems. CONCLUSION: The proposed neural network yields accurate segmentation results to assist clinicians in identifying the median nerve.

Hydrophobic particle effects on hydrate crystal growth at the water-oil interface.

This study introduced hydrophobic silica nanoparticles (SiNPs) into an interface of aqueous and hydrate-forming oil phases and analyzed the inhibition of hydrate crystal growth after seeding the hydrate slurry. The hydrate inhibition performance was quantitatively identified by micro-differential scanning calorimetry (micro-DSC) experiments. Through the addition of 1.0 wt% of SiNPs into the water-oil interface, the hydrate crystal growth only occurred around the seeding position of cyclopentane (CP) hydrate slurry, and the growth of hydrate crystals was retarded. Upon a further increase in the SiNP concentration up to 2.0 wt%, the SiNP-laden interface completely prevented hydrate growth. We observed a hollow conical shape of hydrate crystals with 0.0 and 1.0 wt% of SiNPs, respectively, but the size and shape of the conical crystals was shrunken at 1.0 wt% of silica nanoparticles. However, the conical shape did not appear with an increased nanoparticle concentration of 2 wt%. These findings can provide insight into hydrate inhibition in oil and gas delivery lines, possibly with nanoparticles.