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Regenerative fuel cells can operate alternately as an electrolyzer and as a fuel cell, frequently involving water as a reactant or product. Modifying the electrode surface to manipulate water can prevent electrode flooding and enhance the electrode's mass transfer efficiency by facilitating better contact with gaseous reactants. However, conventional electrodes face difficulties in allowing water droplets to penetrate in a single direction leaving electrodes. In this work to address this issue, a wettability gradient electrode is designed and fabricated for efficient water manipulation in regenerative fuel cells. The findings demonstrate that the water removal strategy in the electrolyzer mode yields the highest ammonia yield and Faradaic efficiency of 3.39 × 10-10 mol s-1 cm-2 and 0.49 %, respectively. Furthermore, in the fuel cell mode, the discharging process sustains for approximately 20.5 h, which is six times longer than the conventional strategy. The ability to sustain the discharging process for extended periods is particularly advantageous in regenerative fuel cells, as it enables the cells to operate for longer periods without the need for regeneration.

Silicon nitride (SiN) plays a critical role in silicon photonics because of its lower refractive index, low waveguide loss, broad operating bandwidth and compatibility with complementary metal oxide semiconductor (CMOS) fabrication process. Here, we propose a polarization-independent sub-wavelength grating (SWG) edge coupler with high efficiency based on SiN-Si dual-stage structure with a length of only 315.8 μm. Such a structure can be fabricated on 8-inch silicon photonics pilot lines and doesn't require special fabrication processes for making it suspended. We simulate the minimum TE/TM light coupling loss from a standard SMF-28 fiber to be 0.61 dB/0.95 dB with polarization dependent loss (PDL) less than 0.4 dB in the whole band between 1500 nm and 1600 nm.

Background: Phthalates are widely used plasticizers, which were identified as risk factors in the development of many human diseases. However, the effects of phthalates in the periodontitis are unknown. We aimed to investigated the relationship of periodontitis and phthalate exposure as well as the underlying mechanisms. Materials and methods: Univariate and multivariate logistic regressions were employed to evaluate the association between phthalate metabolites and periodontitis. The generalized additive model and piecewise logistic regression were conducted to investigate the dose-response relationship. Cell and animal models were used to explore the role and mechanism of DEHP in the development of periodontitis. Transcriptome sequencing, bioinformatics analysis, western blot, immunofluorescence and mice model of periodontitis were also employed. Results: MEHP (OR 1.14, 95% CI 1.05–1.24), MCPP (OR 1.08, 95% CI 1.00–1.17), MEHHP (OR 1.18, 95% CI 1.08–1.29), MEOHP (OR 1.18, 95% CI 1.07–1.29), MiBP (OR 1.15, 95% CI 1.04–1.28), and MECPP (OR 1.20, 95% CI 1.09–1.32) were independent risk factors. And MEHHP, the metabolite of DEHP, showed the relative most important effects on periodontitis with the highest weight (0.34) among all risk factors assessed. And the increase of inflammation and the activation of NFκB pathway in the periodontitis model mice and cells were observed. Conclusion: Exposure to multiple phthalates was positively associated with periodontitis in US adults between 30 and 80 years old. And DEHP aggravated inflammation in periodontitis by activating NFκB pathway.

The application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotator cuff injuries are a commonly encountered condition in joint motion. One of the most severe complications following rotator cuff repair surgery is the recurrence of tears, which has a significant impact on both patients and healthcare professionals. To address this issue, we utilized the innovative EV-GCN algorithm to train a predictive model. We collected medical records of 1,631 patients who underwent rotator cuff repair surgery at a single center over a span of 5 years. In the end, our model successfully predicted postoperative re-tear before the surgery using 62 preoperative variables with an accuracy of 96.93%, and achieved an accuracy of 79.55% on an independent external dataset of 518 cases from other centers. This model outperforms human doctors in predicting outcomes with high accuracy. Through this methodology and research, our aim is to utilize preoperative prediction models to assist in making informed medical decisions during and after surgery, leading to improved treatment effectiveness. This research method and strategy can be applied to other medical fields, and the research findings can assist in making healthcare decisions.