Textile industry waste contains high concentrations of heavy metals such as Pb(II) and Cr(VI) that must be reduced before they are released to the environment. The adsorption method is one way to reduce the heavy metal content. In this work, we develop a porous cellulose nanofiber (CNF) aerogel modified with graphene oxide (GO) as an alternative aerogel adsorbent for Pb(II) and Cr(VI). Cellulose was extracted from banana peduncle, a biomass waste that remains largely underutilized. The addition of GO aims to increase the adsorption properties. The aerogel adsorbents were synthesized by varying the ultrasonication time to 45 min for CNF 45 and 60 min for CNF 60, and the amount of GO added to 1 mL and 2 mL. The aerogel adsorbents were successfully prepared using the freeze-drying method with CNF45, CNF60, CNF45/GO1, CNF45/GO2, CNF60/GO1, and CNF60/GO2 variations. CNF was successfully isolated from a banana peduncle with an average diameter of 44.16 nm for 45 min (CNF 45) and an average diameter of 14.6 nm for 60 min (CNF 60) of ultrasonication. Chemical treatment and ultrasonication reduced the crystallinity index value of cellulose by 73% and 61% for CNF 45 and CNF 60, respectively. CNF aerogel has a very low shrinkage rate (<7%), resulting in a larger surface area. CNF60/GO2 obtained the optimum adsorption ability for Pb(II) metal at a concentration of 100 ppm and 27.27 mg/g at 30 min. On the other hand, the adsorption ability of Cr(VI) metal was obtained by CNF60/GO2 at a concentration of 100 ppm and 13.48 mg/g at 30 min. SEM images show that all aerogel adsorbents are porous, with a porosity value range of 96–98%. In conclusion, CNF60/GO2 proved to be the most effective aerogel adsorbent, offering the potential for heavy metal removal from industrial wastewater.

The increase in the elderly population in Indonesia raises concerns about fall accidents. Many mitigation strategies are available to reduce fall accidents among the elderly. Two main strategies have been widely explored, i.e., fall detection and impact reduction. Despite its disadvantages, fall detection devices remain useful and important for future research. In addition, it is also urgent to address the impact reduction method by providing cushioning to minimize the physical impact on the subject of the fall. This study proposes a wearable safety vest equipped with a pre-impact fall-detection system and an automatically inflating airbag to protect the wearer during a fall. The main contributions of this study are the design and implementation of a lightweight wearable airbag vest for elderly protection, the development of a machine-learning-based pre-impact fall detection, and a quantitative evaluation of impact reduction using accelerometer-based measurements. The proposed safety vest with airbag has been trained, validated, and tested, with the subject achieving fall detection accuracy beyond 90% and the airbag deployment delay of less than 1 second after detection. The experiment results also show that the use of the airbag could reduce the impact shock down to 40-50% based on the accelerometer measurements.