In order to further understand the excitation process of inductively coupled plasma (ICP) and improve the etching efficiency of silicon carbide (SiC), the effect of temperature and atmospheric pressure on plasma etching of silicon carbide was investigated. Based on the infrared temperature measurement method, the temperature of the plasma reaction region was measured. The single factor method was used to study the effect of the working gas flow rate and the RF power on the plasma region temperature. Fixed-point processing of SiC wafers analyzes the effect of plasma region temperature on the etching rate. The experimental results showed that the plasma temperature increased with increasing Ar gas until it reached the maximum value at 15 slm and decreased with increasing flow rate; the plasma temperature increased with a CF4 flow rate from 0 to 45 sccm until the temperature stabilized when the flow rate reached 45 sccm. The higher the RF power, the higher the plasma region’s temperature. The higher the plasma region temperature, the faster the etching rate and the more pronounced the effect on the non-linear effect of the removal function. Therefore, it can be determined that for ICP processing-based chemical reactions, the increase in plasma reaction region temperature leads to a faster SiC etching rate. By processing the dwell time in sections, the nonlinear effect caused by the heat accumulation on the component surface is effectively improved.

Silicon carbide wafer serves as an ideal substrate material for manufacturing semiconductor devices, holding immense potential for the future. However, its ultra-hardness and remarkable chemical inertness pose significant challenges for the surface processing of wafers, and a highly efficient and damage-free method is required to meet the processing requirements. In this study, atmospheric plasma processing was used to conduct point-residence experiments on silicon carbide wafers by varying process parameters such as Ar, CF4, and O2 flow rate, as well as processing power and the distance between the plasma torch and the workpiece. We investigate the effects of these on the surface processing function of atmospheric plasma etching and technique for surface modification of silicon carbide wafers, evaluating the material removal rates. Then, according to the experimentally derived influence law, suitable parameter ranges were selected, and orthogonal experiments were designed to determine the optimal processing parameters that would enable rapid and uniform removal of the wafer surface. The results indicate that the volume removal rate of the plasma on the silicon carbide wafer achieves its maximum when the input power is 550 W, the processing distance between the plasma torch and workpiece is 3.5 mm, and when the Ar, CF4, and O2 flow rates are 15 SLM, 70 SCCM, and 20 SCCM, respectively.

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Therapeutic drug monitoring (TDM) of busulfan (BU) is currently performed by plasma sampling in patients undergoing hematopoietic stem cell transplantation (HSCT). Saliva samples are considered a noninvasive TDM matrix. Currently, no salivary population pharmacokinetics (PopPKs) model for BU available. This study aimed to develop a PopPK model that can describe the relationship between plasma and saliva kinetics in patients receiving intravenous BU. The performance of the model in predicting the area under the concentration‐time curve at steady state (AUCss) based on saliva samples is evaluated. Sixty‐six patients with HSCT were recruited and administered 0.8 mg/kg BU intravenously. A PopPK model for saliva and plasma was developed using the nonlinear mixed effects model. Bayesian maximum a posteriori (MAP) optimization was used to estimate the model's predictive performance. Plasma and saliva PKs were adequately described with a one‐compartment model and a scaled central compartment. Body surface area correlated positively with both clearance and apparent volume of distribution (Vd), whereas alkaline phosphatase correlated negatively with Vd. Simulations demonstrated that the percentage root mean squared prediction error and lower and upper limits of agreements reduced to 10.02% and −16.96% to 22.86% based on five saliva samples. Saliva can be used as an alternative matrix to plasma in TDM of BU. The AUCss can be predicted from saliva concentration by Bayesian MAP optimization, which can be used to design personalized dosing for BU.