In the present work, hybrid Cellulose Nanocrystal–MXene (CNC–MXene) nanolubricants were prepared via a two-step method and investigated as potential heat-transfer hybrid nanofluids for the first time. CNC–MXene nanolubricants were synthesized via a two-step method by varying the weight percentage of CNC–MXene nanoparticles (ranging from 0.01 to 0.05 wt%) and characterized using Fourier-Transform Infrared Spectroscopy and TGA (Thermogravimetric Analysis). Response surface methodology (RSM) was used in conjunction with the miscellaneous design model to identify prediction models for the thermophysical properties of the hybrid CNC–MXene nanolubricant. Minitab 18 statistical analysis software and Response Surface Methodology (RSM) based on Central Composite Design (CCD) were utilized to generate an empirical mathematical model investigating the effect of concentration and temperature. The analysis of variance (ANOVA) results indicated significant contributions from the type of nanolubricant (p < 0.001) and the quadratic effect of temperature (p < 0.001), highlighting non-linear interactions that affect viscosity and thermal conductivity. The findings showed that the predicted values closely matched the experimental results, with a percentage of absolute error below 9%, confirming the reliability of the optimization models. Additionally, the models could predict more than 85% of the nanolubricant output variations, indicating high model accuracy. The optimization analysis identified optimal conditions for maximizing both dynamic viscosity and thermal conductivity. The predicted optimal values (17.0685 for dynamic viscosity and 0.3317 for thermal conductivity) were achieved at 30 °C and a 0.01% concentration, with a composite desirability of 1. The findings of the percentage of absolute error (POAE) reveal that the model can precisely predict the optimum experimental parameters. This study contributes to the growing field of advanced nanolubricants by providing insights into the synergistic effects of CNC and MXene in enhancing thermophysical properties. The developed models and optimization techniques offer valuable tools for tailoring nanolubricant formulations to specific tribological applications, potentially leading to improved efficiency and durability in various industrial settings.

Friction coefficient and wear rate are crucial performance for the development of nanolubricants, which are of great significance for realizing energy conservation and prolonging service life of mechanical components. Herein, this review systematically describes the progress on nanocellulose (CNC) and Copper (II) oxide (CuO) as effective nanoparticles for developments of nanolubricant as oil additives for improving the anti-friction and wear resistant for piston ring-cylinder liner contact. Additionally, we point out several problems existing in the applications of CuO and CNC additives propose the possible solutions. Finally, the research prospects of CNC-CuO in the field of tribology are prospected.

Stress is a critical physiological and psychological response that affects individuals across various domains, including healthcare, workplaces, and academic settings. Conventional stress assessment methods, such as self-report surveys and heart rate monitoring, often lack objectivity or require complex instrumentation. This necessitates the development of alternative, non-invasive stress monitoring techniques. This study aims to develop and evaluate a portable Galvanic Skin Response (GSR) sensor prototype for real-time stress monitoring. The device is designed to improve the accuracy and accessibility of stress detection through continuous skin conductance measurements. The Inner Peace Meter prototype was developed using a GSR sensor, an Arduino Uno microcontroller, and LED indicators to visualize stress levels. The system was tested on volunteers under controlled conditions, including relaxation and stress-inducing tasks. A moving average filter was applied to stabilize sensor readings, and calibration was performed to account for individual differences in skin conductivity. The device successfully detected stress fluctuations, with red LEDs indicating heightened stress, green LEDs signaling relaxation, and yellow LEDs representing stable conditions. Quantitative analysis showed a strong correlation between stress-inducing activities and increased GSR readings. Challenges related to environmental factors and individual variability were addressed through calibration and optimized sensor placement. The Inner Peace Meter demonstrates a reliable and cost-effective approach to stress monitoring, with potential applications in mental health, biofeedback therapy, and wearable health technology. Future enhancements, such as smartphone integration and wireless data logging, could further improve usability and expand its practical applications.