Significantly enhanced enzymatic hydrolysis of waste rice hull through a novel surfactant-based deep eutectic solvent pretreatment.

The potential of green solvents, specifically deep eutectic solvents (DESs), has piqued the interest of researchers in the field of lignocellulose pretreatment. To enhance the enzymatic digestion efficiency of waste rice hull (RCH), an effective pretreatment approach was developed using the DES [AA][CATB], which was made with acetic acid (AA) and cetyltrimethylammonium bromide (CTAB). The results showed that [AA][CATB] improved enzymatic saccharification by 3.7 times compared to raw RCH and efficiently eliminated lignin and removed xylan. The improvement in enzymatic hydrolysis efficiency was then interpreted by a series of characterizations that showed a great morphological changed RCH with an obvious accessibility increase and a lignin surface area and hydrophobicity reduction. This work demonstrates that functional, and easily recoverable DESs have potential for improving the efficiency of lignocellulose pretreatment in biorefineries, providing a promising approach for developing green solvents and achieving more sustainable and efficient biorefinery processes.

Enhanced conversion of biomass to furfurylamine with high productivity by tandem catalysis with sulfonated perlite and ω-transaminase whole-cell biocatalyst.

Production of bio-based chemicals from renewable bioresource is a key driver for moving towards sustainable industry. Furfurylamine is known as an important furfural-upgrading product in organic synthesis, as well as monolithic synthetic pharmaceuticals, fibers, additives and polymers. In one-pot manner, biomass was tandemly catalyzed to furfurylamine with sulfonated Sn-PL catalyst and recombinant ω-transaminase biocatalyst. Sn-PL (2.4 wt%) catalyzed bamboo shoot shell, corncob and rice straw (75.0 g/L) to 76.5-113.0 mM furfural at 44.7-58.5 % yield in γ-valerolactone-water (2:8, v:v) at 170 ℃. The obtained biomass slurries containing furfural were biotransformed to furfurylamine at high yield (0.39-0.42 g furfurylamine/g xylan in biomass) with ω-transaminase biocatalyst using isopropylamine (3.0 mol isopropylamine/mol furfural) as amine donor at 35 ℃. Such a chemoenzymatic one-pot process combined the advantages of both solid acids and whole-cells catalysts, which provided an efficient and sustainable approach for preparing an important bio-based furan chemical furfurylamine.

[Mechanisms and applications of transcutaneous electrical nerve stimulation in analgesia].

Transcutaneous electrical nerve stimulation (TENS), as a non-pharmacological and non-invasive analgesic therapy with low-cost, has been widely used to relieve pain in various clinical applications, by delivering current pulses to the skin area to activate the peripheral nerve fibers. Nevertheless, analgesia induced by TENS varied in the clinical practice, which could be caused by the fact that TENS with different stimulus parameters has different biological mechanisms in relieving pain. Therefore, to advance our understanding of TENS in various basic and clinical studies, we discussed (1) neurophysiological and biochemical mechanisms of TENS-induced analgesia; (2) relevant factors that may influence analgesic effects of TENS from the perspectives of stimulus parameters, including stimulated position, pulse parameters (current intensity, frequency, and pulse width), stimulus duration and used times in each day; and (3) applications of TENS in relieving clinical pain, including post-operative pain, chronic low back pain and labor pain. Finally, we propose that TENS may involve multiple and complex psychological neurophysiological mechanisms, and suggest that different analgesic effects of TENS with different stimulus parameters should be taken into consideration in clinical applications. In addition, to optimize analgesic effect, we recommend that individual-based TENS stimulation parameters should be designed by considering individual differences among patients, e.g., adaptively adjusting the stimulation parameters based on the dynamic ratings of patients' pain.