Electrocatalysis Boosts the Methanol Thermocatalytic Dehydrogenation for High-Purity H2 and CO Production.

Hydrogen production from methanol represents an energy-sustainable way to produce ethanol, but it normally results in heavy CO2 emissions. The selective conversion of methanol into H2 and valuable chemical feedstocks offers a promising strategy; however, it is limited by the harsh operating conditions and low conversion efficiency. Herein, we realize efficient high-purity H2 and CO production from methanol by coupling the thermocatalytic methanol dehydrogenation with electrocatalytic hydrogen oxidation on a bifunctional Ru/C catalyst. Electrocatalysis enables the acceleration of C-H cleavage and reduces the partial pressure of hydrogen at the anode, which drives the chemical equilibrium and significantly enhances methanol dehydrogenation. Furthermore, a bilayer Ru/C + Pd/C electrode is designed to mitigate CO poisoning and facilitate hydrogen oxidation. As a result, a high yield of H2 (558.54 mmol h-1 g-1) with high purity (99.9%) was achieved by integrating an applied cell voltage of 0.4 V at 200 °C, superior to the conventional thermal and electrocatalytic processes, and CO is the main product at the anode. This work presents a new avenue for efficient H2 production together with valuable chemical synthesis from methanol.

Efficient Low-temperature Hydrogen Production by Electrochemical-assisted Methanol Steam Reforming.

Methanol steam reforming (MSR) provides an alternative way for efficient production and safe transportation of hydrogen but requires harsh conditions and complicated purification processes. In this work, an efficient electrochemical-assisted MSR reaction for pure H2 production at lower temperature (~140 °C) is developed by coupling the electrocatalysis reaction into the MSR in a polymer electrolyte membrane electrolysis reactor. By electrochemically assisted, the two critical steps including the methanol dehydrogenation and water-gas shift reaction are accelerated, which is attributed to decreasing the methanol dehydrogenation energy and promoting the dissociation of H2 O to OH* by the applied potential. Furthermore, the reduced H2 partial pressure by the hydrogen oxidation and reduction process further promotes MSR. The combination of these advantages not only efficiently decreases the MSR temperature but also achieves the high rate of hydrogen production of 505 mmol H2 g Pt -1 h-1 with exceptionally high H2 selectivity (99 %) at 180 °C and a low voltage (0.4 V), and the productivity is about 30-fold than that of traditional MSR. This study opens up a new avenue to design novel electrolysis cells for hydrogen production.

Supervise-Assisted Self-Supervised Deep-Learning Method for Hyperspectral Image Restoration.

Hyperspectral image (HSI) restoration is a challenging research area, covering a variety of inverse problems. Previous works have shown the great success of deep learning in HSI restoration. However, facing the problem of distribution gaps between training HSIs and target HSI, those data-driven methods falter in delivering satisfactory outcomes for the target HSIs. In addition, the degradation process of HSIs is usually disturbed by noise, which is not well taken into account in existing restoration methods. The existence of noise further exacerbates the dissimilarities within the data, rendering it challenging to attain desirable results without an appropriate learning approach. To track these issues, in this article, we propose a supervise-assisted self-supervised deep-learning method to restore noisy degraded HSIs. Initially, we facilitate the restoration network to acquire a generalized prior through supervised learning from extensive training datasets. Then, the self-supervised learning stage is employed and utilizes the specific prior of the target HSI. Particularly, to restore clean HSIs during the self-supervised learning stage from noisy degraded HSIs, we introduce a noise-adaptive loss function that leverages inner statistics of noisy degraded HSIs for restoration. The proposed noise-adaptive loss consists of Stein's unbiased risk estimator (SURE) and total variation (TV) regularizer and fine-tunes the network with the presence of noise. We demonstrate through experiments on different HSI tasks, including denoising, compressive sensing, super-resolution, and inpainting, that our method outperforms state-of-the-art methods on benchmarks under quantitative metrics and visual quality. The code is available at https://github.com/ying-fu/SSDL-HSI.

Causal associations between the gut microbiota and multiple myeloma: a two-sample Mendelian randomization study.

Background: Previous observational studies have indicated a potential association between the gut microbiota and multiple myeloma (MM). However, the relationship between the gut microbiota and MM remains unclear. This study aimed to ascertain the existence of a causal link between the gut microbiota and MM. Methods: To investigate the potential causal relationship between gut microbiota and MM, a two-sample Mendelian randomization (MR) analysis was conducted. Exposure data was obtained from the MiBioGen consortium, which provided genetic variants associated with 211 bacterial traits. MM outcome data was obtained from the FinnGen consortium. The selection of Single nucleotide polymorphisms estimates was performed through meta-analysis using inverse-variance weighting, and sensitivity analyses were conducted using weighted median, MR Egger, Simple mode, and MR-PRESSO. Results: The results of the study demonstrated a significant positive correlation between the genus Eubacterium ruminantium group and the risk of MM (OR 1.71, 95% CI 1.21 to 2.39). Conversely, the genus: Dorea (OR 0.46, 95% CI 0.24 to 0.86), Coprococcus1 (OR 0.47, 95% CI 0.22 to 1.00), RuminococcaceaeUCG014 (OR 0.57, 95% CI 0.33 to 0.99), Eubacterium rectale group (OR 0.37, 95% CI 0.18 to 0.77), and order: Victivallales (OR 0.62, 95% CI 0.41-0.94), class: Lentisphaeria (OR 0.62, 95% CI 0.41 to 0.94), exhibited a negative association with MM. The inverse variance weighting analysis provided additional support for these findings. Conclusion: This study represents an inaugural exploration of MR to investigate the connections between gut microbiota and MM, thereby suggesting potential significance for the prevention and treatment of MM.