Venlafaxine (VF) and its active metabolite desvenlafaxine (DVF) are widely prescribed antidepressants that are only partially metabolized and excreted in significant amounts, making them clinically important analytes and environmentally relevant contaminants. In this study, a free-standing boron-doped diamond (BDD) electrode is exploited in a dual role for the electrochemical detection and degradation of VF and DVF, integrated into a custom 3D-printed dual-function electrochemical cell. The nucleation (BDD_NS) and growth (BDD_GS) sides of the BDD plate were systematically compared under different surface terminations. Oxidized BDD_NS (O-BDD_NS) provided three well-resolved oxidation peaks for VF, whereas hydrogen-terminated BDD_NS (H-BDD_NS) yielded a single distinct peak for DVF in 0.1 M H₂SO₄. Differential pulse voltammetric (DPV) methods were developed with limits of detection of 0.35 µM for VF (peak 1) and 0.34 µM for DVF and wide linear ranges in the low-to-high micromolar region. By exploiting the different surface-termination preferences and multi-peak behaviour of VF, simultaneous determination of VF and DVF was achieved. The methods showed good selectivity toward common interferents and were successfully applied to spiked river water and pharmaceutical capsules using the standard addition approach, giving recoveries close to 100 %. In the 3D-printed cell, BDD_GS was used for electrochemical advanced oxidation, achieving ∼97 % degradation of 1 mM VF and DVF in 0.1 M H₂SO₄ within 20 min under galvanostatic conditions, following pseudo-first-order kinetics. In situ DPV on BDD_NS enabled real-time monitoring of VF decay, demonstrating an integrated detect-and-degrade platform based on BDD and additive manufacturing.

Aerodynamic drag force and projected frontal area (A) are commonly used indicators of aerodynamic cycling efficiency. This study investigated the accuracy of estimating these quantities using easy-to-acquire anthropometric and pose measures. In the first part, computational fluid dynamics (CFD) drag force calculations and A (m²) values from photogrammetry methods were compared using predicted 3D cycling models for 10 male amateur cyclists. The shape of the 3D models was predicted using anthropometric measures. Subsequently, the models were reposed from a standing to a cycling pose using joint angle data from an optical motion capture (mocap) system. In the second part, a linear regression analysis was performed to predict A using 26 anthropometric measures combined with joint angle data from two sources (optical and inertial mocap, separately). Drag calculations were strongly correlated with benchmark projected frontal area (coefficient of determination R² = 0.72). A can accurately be predicted using anthropometric data and joint angles from optical mocap (root mean square error (RMSE) = 0.037 m²) or inertial mocap (RMSE = 0.032 m²). This study showed that aerodynamic efficiency can be predicted using anthropometric and joint angle data from commercially available, inexpensive posture tracking methods. The practical relevance for cyclists is to quantify and train posture during cycling for improving aerodynamic efficiency and hence performance.

In times of online shopping, it is a challenge to select the right size of the desired clothing without fitting it before ordering. Therefore, this study describes three techniques to predict 3D upper body dimensions. The first method used basic personal parameters (gender, age, weight and length), the second technique used also the shoulder width and the last method used a 3D Styku scan to add extra input parameters. The accuracy of the three prediction methods was compared against hand measurements for 17 upper body dimensions of 37 subjects. The Intraclass Correlation Coefficient increases with 11.2% for the Styku method compared to the other methods. For chest, hip and waist measurements, the basic method is reliable to predict 3D body dimensions and indicate the right size from an existing collection. For more accurate upper body dimensions as needed for producing custom made clothing, a 3D Styku scan can supply the desired input.