Improved Photocatalytic Performance via Air-Plasma Modification of Titanium Dioxide: Insights from Experimental and Simulation Investigation

Commercial titanium dioxide is successfully plasma-treated under ambient conditions for different time periods, leading to reduced crystallite size and the creation of oxygen vacancies. Density functional theory-based calculations reveal the emergence of additional localized states close to the conduction band, primarily associated with under-coordinated titanium atoms in non-stoichiometric titanium-oxide systems. The plasma-treated samples exhibit improved photocatalytic performance in the degradation of methylene blue compared to untreated samples. Moreover, the 4-hour plasma-treated photocatalyst demonstrates commendable stability and reusability. This work highlights the potential of cost-effective plasma treatment as a simple modification technique to significantly enhance the photocatalytic capabilities of titanium dioxide.Comment: Manuscript and Supplementary material include

Formation of amorphous carbon multi-walled nanotubes from random initial configurations

Amorphous carbon nanotubes (a-CNT) with up to four walls and sizes ranging from 200 to 3200 atoms have been simulated, starting from initial random configurations and using the Gaussian Approximation Potential [Phys. Rev. B 95, 094203 (2017)]. The important variables (like density, height, and diameter) required to successfully simulate a-CNTs, were predicted with a machine learning random forest technique. The models were validated $\textit{ex post facto}$ using density functional codes. The a-CNT models ranged from 0.55 nm - 2 nm wide with an average inter-wall spacing of 0.31 nm. The topological defects in a-CNTs were discussed and new defect configurations were observed. The electronic density of states and localization in these phases were discussed and delocalized electrons in the $\pi$ subspace were identified as an important factor for inter-layer cohesion. Spatial projection of the electronic conductivity favors axial transport along connecting hexagons, while non-hexagonal parts of the network either hinder or bifurcate the electronic transport. A vibrational density of states was calculated and is potentially an experimentally testable fingerprint of the material and the appearance of a low-frequency radial breathing mode was discussed. The thermal conductivity at 300 K was calculated using the Green-Kubo formula

Agreement and accuracy using the FIGO, ACOG and NICE cardiotocography interpretation guidelines.

INTRODUCTION: One of the limitations reported with cardiotocography (CTG) is the modest interobserver agreement observed in tracing interpretation. This study compared agreement, reliability and accuracy of CTG interpretation using the FIGO, ACOG and NICE guidelines. MATERIAL AND METHODS: A total of 151 tracings was evaluated by 27 clinicians from three centers where FIGO, ACOG and NICE guidelines were routinely used. Interobserver agreement was evaluated using the proportions of agreement (PA) and reliability with the kappa (k) statistic. The accuracy of tracings classified as "pathological/category III" was assessed for prediction of newborn acidemia. For all measures, 95% confidence intervals (95%CI) were calculated RESULTS: CTG classifications were more distributed with FIGO (9%, 52%, 39%) and NICE (30%, 33%, 37%) than with ACOG (13%, 81%, 6%). The category with the highest agreement was ACOG category II (PA=0.73 95%CI 0.70-76), and the ones with the lowest agreement were ACOG categories I and III. Reliability was significantly higher with FIGO (k=0.37, 95%CI 0.31-0.43), and NICE (k=0.33, 95%CI 0.28-0.39) than with ACOG (k= 0.15, 95%CI 0.10-0.21), however all represent only slight/fair reliability. FIGO and NICE showed a trend towards higher sensitivities in prediction of newborn acidemia (89% and 97% respectively) than ACOG (32%,), but the latter achieved a significantly higher specificity (95%) CONCLUSIONS: With ACOG guidelines there is high agreement in category II, low reliability, low sensitivity and high specificity in prediction of acidemia. With FIGO and NICE guidelines there is higher reliability, a trend towards higher sensitivity, and lower specificity in prediction of acidemia. This article is protected by copyright. All rights reserved

Candidiasis, Bacterial Vaginosis, Trichomoniasis and Other Vaginal Conditions Affecting the Vulva

info:eu-repo/semantics/publishedVersio

Atomistic Nature of Amorphous Graphite

This paper focuses on the structural, electronic, and vibrational features of amorphous graphite [R. Thapa $\textit{et. al.}$, Phys. Rev. Lett. 128, 236402 (2022)]. The structure order in amorphous graphite is discussed and compared with graphite and amorphous carbon. The electronic density of states and localization in these phases were analyzed. Spatial projection of charge densities in the $\pi$ bands showed a high charge concentration on participating atoms in connecting hexagons. A vibrational density of states was computed and is potentially an experimentally testable fingerprint of the material. An analysis of the vibrational modes was carried out using the phase quotient, and the mode stretching character. The average thermal conductivity calculated for aG was 0.85 Wcm$^{-1}$K$^{-1}$ and 0.96 Wcm$^{-1}$K$^{-1}$ at room temperature and 1000 K respectively

Structure, vibrations and electronic transport in silicon suboxides: Application to physical unclonable functions

This work focuses on the structure and electronic transport in atomistic models of silicon suboxides (a-SiOx; x = 1.3,1.5 and 1.7) used in the fabrication of a Physical Unclonable Function (PUF) devices. Molecular dynamics and density functional theory simulations were used to obtain the structural, electronic, and vibrational properties that contribute to electronic transport in a-SiOx. The percentage of Si-[Si1, O3] and Si-[Si3, O1], observed in a-SiO1.3, decrease with increasing O ratio. Vibrations in a-SiOx showed peaks that result from topological defects. The electronic conduction path in a-SiOx favored Si-rich regions and Si atoms with dangling bonds formed charge-trapping sites. For doped a-SiOx, the type of doping results in new conduction paths, hence qualifying a-SiOx as a viable candidate for PUF fabrication as reported by Kozicki [Patent-Publication-No.: US2021/0175185A1, 2021]

Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential

Multi-shell fullerenes ”buckyonions” were simulated, starting from initially random configurations, using a density-functional-theory (DFT)-trained machine-learning carbon potential within the Gaussian Approximation Potential (GAP) Framework [Volker L. Deringer and Gábor Csányi, Phys. Rev. B 95, 094203 (2017)]. Fullerenes formed from seven different system sizes, ranging from 60 ∼ 3774 atoms, were considered. The buckyonions are formed by clustering and layering starting from the outermost shell and proceeding inward. Inter-shell cohesion is partly due to interaction between delocalized π electrons protruding into the gallery. The energies of the models were validated ex post facto using density functional codes, VASP and SIESTA, revealing an energy difference within the range of 0.02 - 0.08 eV/atom after conjugate gradient energy convergence of the models was achieved with both methods

Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm

The Cardiotocography is the most broadly utilized technique in obstetrics practice to monitor fetal health condition. The foremost motive of monitoring is to detect the fetal hypoxia at early stage. This modality is also widely used to record fetal heart rate and uterine activity. The exact analysis of cardiotocograms is critical for further treatment. In this manner, fetal state evaluation utilizing machine learning technique using cardiotocogram data has achieved significant attention. In this paper, we implement a model based CTG data classification system utilizing a supervised Random Forest (RF) which can classify the CTG data based on its training data. As per the showed up results, the overall performance of the supervised machine learning based classification approach provided significant performance. In this study, Precision, Recall, F-Score and Rand Index has been employed as the metric to evaluate the performance. It was found that, the RF based classifier could identify normal, suspicious and pathologic condition, from the nature of CTG data with 94.8% accuracy

Fetal electrocardiography ST-segment analysis for intrapartum monitoring: a critical appraisal of conflicting evidence and a way forward

Background: In the past century, some areas of obstetric including intrapartum care have been slow to benefit from the dramatic advances in technology and medical care. Although fetal heart rate monitoring (cardiotocography) became available a half century ago, its interpretation often differs between institutions and countries, its diagnostic accuracy needs improvement, and a technology to help reduce the unnecessary obstetric interventions that have accompanied the cardiotocography is urgently needed. Study Design: During the second half of the 20th century, key findings in animal experiments captured the close relationship between myocardial glycogenolysis, myocardial workload, and ST changes, thus demonstrating that ST waveform analysis of the fetal electrocardiogram can provide information on oxygenation of the fetal myocardium and establishing the physiological basis for the use of electrocardiogram in intrapartum fetal surveillance. Results: Six randomized controlled trials, 10 meta-analyses, and more than 20 observational studies have evaluated the technology developed based on this principle. Nonetheless, despite this intensive assessment, differences in study protocols, inclusion criteria, enrollment rates, clinical guidelines, use of fetal blood sampling, and definitions of key outcome parameters, as well as inconsistencies in randomized controlled trial data handling and statistical methodology, have made this voluminous evidence difficult to interpret. Enormous resources spent on randomized controlled trials have failed to guarantee the generalizability of their results to other settings or their ability to reflect everyday clinical practice. Conclusion: The latest meta-analysis used revised data from primary randomized controlled trials and data from the largest randomized controlled trials from the United States to demonstrate a significant reduction of metabolic acidosis rates by 36% (odds ratio, 0.64; 95% confidence interval, 0.46\u20130.88) and operative vaginal delivery rates by 8% (relative risk, 0.92; 95% confidence interval, 0.86\u20130.99), compared with cardiotocography alone