Arsenite (As(III)) contamination in drinking water has become a worldwide problem in recent years, which leads to development of various As(III) remediation approaches. In this study, two biomass-based nanostructured materials, microscale dialdehyde cellulose–cysteine (MDAC–cys) and nanoscale dialdehyde cellulose–cysteine (NDAC–cys) fibers, have been prepared from wood pulp. Their As(III) removal efficiencies and mechanism were determined by combined adsorption, atomic fluorescence spectrometry, microscopy (scanning electron microscopy, transmission electron microscopy, and atomic force microscopy), and spectroscopy (Fourier transform infrared, 13C CPMAS NMR) methods. The adsorption results of these materials could be well described by the Freundlich isotherm model, where the maximum adsorption capacities estimated by the Langmuir isotherm model were 344.82 mg/g for MDAC–cys and 357.14 mg/g for NDAC–cys, respectively. Both MDAC–cys and NDAC–cys materials were further characterized by X-ray diffraction and thermogravimetric analysis, where the results indicated that the thiol groups (the S content in MDAC–cys was 12.70 and NDAC–cys was 17.15%) on cysteine were primarily responsible for the adsorption process. The nanostructured MDAC–cys system appeared to be more suitable for practical applications because of its high cost-effectiveness.

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We report the first demonstration of fabric-based microfluidics for wearable sensing. A new technology to develop microfluidics on fabrics, as a part of an undergarment, is described here. Compared to conventional microfluidics from polydimethylsiloxane, fabric-based microfluidics are simple to make, robust, and suitable for efficient sweat delivery. Specifically, acrylonitrile butadiene styrene (ABS) films with precut microfluidic patterns were infused through fabrics to form hydrophobic areas in a specially controlled sandwich structure. Experimental tests and simulations confirmed the sweat delivery efficiency of the microfluidics. Electrodes were screen-printed onto the fabric-based microfluidic. A novel wearable potentiometer based on Arduino was also developed as the transducer and signal readouts, which was low-cost, standardized, open-source, and capable of wireless data transfer. We applied the sensor system as a standalone or as a module of a T-shirt to quantify [Ca 2+ ] in a wearer's sweat, with physiological and accurate results generated. Overall, this work represents a critical step in turning regular undergarments into biochemically smart platforms for health monitoring, which will broadly benefit human healthcare.

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Thallium(I) (Tl(I)) pollution has become a pressing environmental issue due to its harmful effect on human health and aquatic life. Effective technology to remove Tl(I) ions from drinking water can offer immediate societal benefits especially in the developing countries. In this study, a bio-adsorbent system based on nitro-oxidized nanocellulose (NOCNF) extracted from sorghum stalks was shown to be a highly effective Tl(I) removal medium. The nitro-oxidation process (NOP) is an energy-efficient, zero-waste approach that can extract nanocellulose from any lignocellulosic feedstock, where the effluent can be neutralized directly into a fertilizer without the need for post-treatment. The demonstrated NOCNF adsorbent exhibited high Tl(I) removal efficiency (>90% at concentration < 500 ppm) and high maximum removal capacity (Qm = 1898 mg/g using the Langmuir model). The Tl(I) adsorption mechanism by NOCNF was investigated by thorough characterization of NOCNF-Tl floc samples using spectroscopic (FTIR), diffraction (WAXD), microscopic (SEM, TEM, and AFM) and zeta-potential techniques. The results indicate that adsorption occurs mainly due to electrostatic attraction between cationic Tl(I) ions and anionic carboxylate groups on NOCNF, where the adsorbed Tl(I) sites become nuclei for the growth of thallium oxide nanocrystals at high Tl(I) concentrations. The mineralization process enhances the Tl(I) removal efficiency, and the mechanism is consistent with the isotherm data analysis using the Freundlich model.