Molecularly Imprinted Electrochemical Sensors Based on Ti3C2Tx-MXene and Graphene Composite Modifications for Ultrasensitive Cortisol Detection.

The increasing pressure and unhealthy lifestyle are gradually eroding the physical and mental health of modern people. As a key hormone responsible for maintaining the normal functioning of human systems, cortisol plays a vital role in regulating physiological activities. Moreover, cortisol can serve as a marker for monitoring psychological stress. The development of cortisol detection sensors carries immense potential, as they not only facilitate timely adjustments and treatments by detecting abnormal physiological indicators but also provide comprehensive data for conducting research on the correlation between cortisol and several potential diseases. Here, we report a molecularly imprinted polymer (MIP) electrochemical biosensor that utilizes a porous composite (MXG) modified electrode. MXG composite is prepared by combining Ti3C2Tx-MXene sheets and graphene (Gr). MXG composite material with high conductive properties and large electroactive surface area promotes the charge transfer capability of the electrode surface, expands the effective surface area of the sensor, and increases the content of cortisol-imprinted cavities on the electrode, thereby improving the sensing ability of the sensor. By optimizing the preparation process, the prepared sensor has an ultralow lower limit of detection of 0.4 fM, a wide detection range of 1 fM-10 µM, and good specificity for steroid hormones and interfering substances with similar cortisol structure. The ability of the sensor to detect cortisol in saliva was also confirmed experimentally. This highly sensitive and selective cortisol sensor is expected to be widely used in the fields of physiological and psychological care.

Milling Mechanism and Chattering Stability of Nickel-Based Superalloy Inconel 718.

Nickel-based superalloy Inconel 718 is widely used in the aerospace industry for its excellent high-temperature strength and thermal stability. However, milling Inconel 718 presents challenges because of the significantly increased cutting force and vibration, since Inconel 718 is a typical difficult-to-machine material. This paper takes the milling process of Inconel 718 as the research object, initially, and a milling force model of Inconel 718 is established. Subsequently, the finite element analysis method is used to analyze the stress field, temperature field, and milling force in the milling process of Inconel 718. Building upon this, a dynamic equation of the milling of Inconel 718 is established, and based on the modal experiment, stability lobe diagrams are drawn. Furthermore, milling experiments on Inconel 718 are designed, and the results calculated using the milling force model and finite element analysis are verified through comparison to the experimental results; then, the fmincon optimization algorithm is used to optimize the processing parameters of Inconel 718. Eventually, the results of the multi-objective optimization illustrate that the best processing parameters are a spindle speed of 3199.2 rpm and a feed speed of 80 mm/min with an axial depth of cut of 0.25 mm. Based on this, the best machining parameters are determined, which point towards an improvement of the machining efficiency and quality of Inconel 718.