As water pollution from dyes, pharmaceuticals, heavy metals, and other emerging contaminants continues to rise at an alarming rate, ensuring access to clean and safe water has become a pressing global challenge. Conventional water treatment methods, though widely used, often fall short in effectively addressing these complex pollutants. In response, researchers have turned to Advanced Functional Membranes (AFMs) as promising alternatives, owing to their customizable structures and enhanced performance. Among the most explored AFMs are those based on metal–organic frameworks (MOFs), carbon nanotubes (CNTs), and electro–catalytic systems, each offering unique advantages such as high permeability, selective pollutant removal, and compatibility with advanced oxidation processes (AOPs). Notably, hybrid systems combining AFMs with electrochemical or photocatalytic technologies have demonstrated remarkable efficiency in laboratory settings. However, translating these successes to real-world applications remains a challenge due to issues related to cost, scalability, and long-term stability. This review explores the recent progress in AFM development, particularly MOF-based, CNT-based, and electro-Fenton (EF)-based membranes, highlighting their material aspects, pollutant filtration mechanisms, benefits, and limitations. It also offers insights into how these next-generation materials can contribute to more sustainable, practical, and economically viable water purification solutions in the near future.

Safe and reliable mobility over different kinds of ground is important for planetary rovers on space missions. Since terrain changes might affect the mobility of the rover, energy consumption, and safety, detecting the type of ground in real‐time is vital. While past methods utilized cameras, motion sensors, or traditional machine learning, achieving consistent and accurate outputs on mixed terrains has been found to be challenging. Here, a wireless sensor module was developed and placed in the rover wheel, and sampled data from a Force‐Sensitive Resistor and an Inertial Measurement Unit at a 100‐samples/s sampling rate. A suite of supervised machine learning algorithms is used for terrain type labeling: rocky, sandy, hard, and grassy. The system could classify terrains in real‐time with good accuracy. Random Forest performed the best among them with 88% accuracy and an F 1 score of 0.87. As a simple and efficient model, Random Forest is chosen for real‐time classification. This sensor‐based system enables real‐time, lighting‐independent terrain classification, overcoming the visual limitations and latency of deep learning image‐based methods. The solution helps the rover plan its movement based on the terrain sensed, making it energy efficient and responsive. Key contributions of this study include the design of a wireless multisensor module, high‐frequency data acquisition, and the integration of machine learning models for rapid terrain‐adaptive mobility.

Triply periodic minimal surface (TPMS) gyroid lattices are attractive for lightweight energy-absorbing architectures in additive manufacturing owing to smooth geometry and stress-concentration-free load transfer. However, PETG-CF gyroid research focuses on RD, with limited investigation of FDM parameters layer height (LH) and printing speed (PS). Here, a full factorial design of 3 LH × 2 PS × 3 RD levels (54 specimens) was used to compress PETG-CF gyroids and extract eight mechanical properties. Three-way ANOVA showed that RD dominated variation (78–96% for compressive modulus, yield strength, plateau stress, and specific energy absorption), followed by LH (3–14%) and PS (<4%), with significant interactions. Gibson-Ashby scaling yielded modulus and strength exponents of 2.03 and 2.93, respectively, close to bending-dominated wall collapse. SEM confirmed geometry and revealed fiber clustering and extrusion defects. Power-law Ridge, quadratic Ridge, Gaussian process regression, and linear models were evaluated for four properties using design and as-printed RD configurations. The best model achieved mean R² = 0.962 (0.910–0.992) under leave-one-condition-out cross-validation, with power-law scaling most prevalent for stiffness- and strength-based properties. As-printed RD correction reduced error for modulus and yield strength (17.6% and 24.5%) but increased it for plateau stress and energy absorption (35.6% and 21.9%).

Triply periodic minimal surface (TPMS) gyroid lattices offer smooth, self-supporting geometry with stress-concentration-free load transfer, making them attractive for lightweight, energy-absorbing structures in additive manufacturing. Yet most studies on carbon fiber-reinforced PETG (PETG-CF) gyroids vary relative density (RD) alone, leaving the combined influence of fused deposition modeling parameters layer height (LH) and printing speed (PS) largely unexplored. Here, a full factorial design (3 LH × 2 PS × 3 RD levels, 54 specimens) was used to compress PETG-CF gyroid lattices and extract eight mechanical properties per specimen. Three-way ANOVA showed RD dominated mechanical variation (78-96% across compressive modulus, yield strength, plateau stress, and specific energy absorption), with LH a distant second (3-14%) and PS the smallest (<4%); small but significant interaction effects confirmed the parameters do not act independently. Gibson-Ashby scaling gave modulus and strength exponents of 2.03 and 2.93, which is quite close to the bending-dominated wall collapse. SEM imaging confirmed the printed gyroid geometry and revealed localized fiber clustering and minor extrusion artifacts. For each of the four mechanical properties, power-law Ridge, quadratic Ridge, Gaussian process regression, and linear models were compared using both design and as-printed relative density as configurations; the best-performing model for each property-configuration pairing achieved a mean R² of 0.962 (range 0.910-0.992) under leave-one-condition-out cross-validation, with power-law scaling emerging as the dominant relationship for stiffness- and strength-related properties. Together, these results offer clear process-selection guidance for PETG-CF gyroid lattices: prioritize RD, then LH, and provide a validated machine learning tool for predicting compressive performance.