Growth Control Strategy of Hydrogen-Containing Nanocrystalline Carbon Films during Plasma-Enhanced Chemical Vapor Deposition based on Molecular Dynamics-Monte Carlo Simulations.

Hydrogen-containing nanocrystalline carbon films (n-C:H) with amorphous-nanocrystalline hydrocarbon composite structures exhibit excellent properties in diverse applications. Plasma-enhanced chemical vapor deposition (PECVD) is commonly employed to prepare n-C:H films due to its ability to create an adjustable deposition environment and control film compositions. However, the atomic-scale growth mechanism of n-C:H remains poorly understood, obstructing the design of the appropriate deposition parameters and film compositions. This paper employs a state-of-the-art hybrid molecular dynamics-time-stamped force-biased Monte Carlo model (MD/tfMC) to simulate the plasma-assisted growth of n-C:H. Our results reveal that optimizing the energy of ion bombardments, deposition temperature, and precursor's H:C ratio is crucial for achieving the nucleation and growth of highly ordered n-C:H films. These findings are further validated through experimental observations and density functional theory calculations, which show that hydrogen atoms can promote the formation of nanocrystalline carbon through chemical catalytic processes. Additionally, we find that the crystallinity reaches its optimum when the H/C ratio is equal to 1. These theoretical insights provide an effective strategy for the controlled preparation of hydrogen-containing nanocrystalline carbon films.

Assessment of 17 clinically available renal biomarkers to predict acute kidney injury in critically ill patients.

BACKGROUND: Systematic estimation of renal biomarkers in the intensive care unit (ICU) patients is lacking. Seventeen biomarkers were assessed to predict acute kidney injury (AKI) after admission to ICU. MATERIALS AND METHODS: A prospective, observational study was conducted in the general ICU of Guangdong Provincial People's Hospital. Seventeen serum or urine biomarkers were studied for their abilities alone or in combination for predicting AKI and severe AKI. RESULTS: Of 1498 patients, 376 (25.1%) developed AKI. Serum cystatin C (CysC) showed the best performance for predicting both AKI (area under the receiver operator characteristic curve [AUC] = 0.785, mean square error [MSE] = 0.118) and severe AKI (AUC = 0.883, MSE = 0.06). Regarding biomarkers combinations, CysC plus N-acetyl-ß-d-glucosaminidase-to-creatinine ratio (NAG/Cr) was the best for predicting AKI (AUC = 0.856, MSE = 0.21). At the same time, CysC plus lactic acid (LAC) performed the best for predicting severe AKI (AUC = 0.907, MSE = 0.058). Regarding combinations of biomarkers and clinical markers, CysC plus Acute Physiology and Chronic Health Evaluation (APACHE) II score showed the best performance for predicting AKI (AUC = 0.868, MSE = 0.407). In contrast, CysC plus Multiple Organ Dysfunction Score (MODS) had the highest predictive ability for severe AKI (AUC = 0.912, MSE = 0.488). CONCLUSION: Apart from CysC, the combination of most clinically available biomarkers or clinical markers does not significantly improve the forecasting ability, and the cost-benefit ratio is not economical.