CO 2 electroreduction is a promising technology to produce chemicals and fuels from renewable resources. Polycrystalline and nanostructured metals have been tested extensively while less effort has been spent on understanding the performance of bimetallic alloys. In this work, we study compositionally variant, smooth Au−Pd thin film alloys to discard any morphological or mesoscopic effect on the electrocatalytic performance. We find that the onset potential of CO formation exhibits a strong dependence on the Pd content of the alloys. Strikingly, palladium, a hydrogen evolution catalyst with reasonable exchange current density, suppresses hydrogen evolution when alloyed with gold in the presence of CO 2 . Cyclic voltammetry, in situ surface enhanced infrared absorption spectroscopy, and potential-dependent online product analysis strongly suggest that by alloying Au with Pd a significant increase in the surface coverage of adsorbed CO occurs with increasing Pd content at low overpotentials (e.g., approximately −0.35 V vs RHE). Such an increase in CO coverage suppresses H 2 evolution due to the lack of vacant active sites. Moreover, the overall increase in the binding energy with the CO 2 intermediates gained with the addition of Pd increases the CO production at low overpotentials, where polycrystalline Au suffers from poor CO 2 adsorption and poor selectivity for CO production. These results show that promising CO 2 reduction electrode materials (e.g., Au) can be alloyed not only to tune the catalyst's activity but also to deliberately decrease the availability of surface sites for competitive H 2 evolution.

Objective: Stress is a significant risk factor for various diseases such as hypertension, heart attack, stroke, and even sudden death. Stress can also lead to psychological and behavioral disorders. Heart rate variability (HRV) can reflect changes in stress levels while other physiological factors, like blood pressure, are within acceptable ranges. Electroencephalogram (EEG) is a vital technique for studying brain activities and provides useful data regarding changes in mental status. This study incorporates EEG and a detailed HRV analysis to have a better understanding and analysis of stress. Investigating the correlation between EEG and HRV under stress conditions is valuable since they provide complementary information regarding stress. Methods: Simultaneous electrocardiogram (ECG) and EEG recordings were obtained from fifteen subjects. HRV /EEG features were analyzed and compared in rest, stress, and meditation conditions. A one-way ANOVA and correlation coefficient were used for statistical analysis to explore the correlation between HRV features and features extracted from EEG. Results: The HRV features LF (low frequency), HF (high frequency), LF/HF, and rMSSD (root mean square of the successive differences) correlated with EEG features, including alpha power band in the left hemisphere and alpha band power asymmetry. Conclusion: This study demonstrated five significant relationships between EEG and HRV features associated with stress. The ability to use stress-related EEG features in combination with correlated HRV features could help improve detecting stress and monitoring the progress of stress treatments/therapies. The outcomes of this study could enhance the efficiency of stress management technologies such as meditation studies and bio-feedback training.

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Structured Abstract—Objective : Abnormal elevation of intracranial pressure (ICP) can cause dangerous or even fatal outcomes. The early detection of high intracranial pressure events can be crucial in saving lives in an intensive care unit (ICU). Despite many applications of machine learning (ML) techniques related to clinical diagnosis, ML applications for continuous ICP detection or short-term predictions have been rarely reported. This study proposes an efficient method of applying an artificial recurrent neural network on the early prediction of ICP evaluation continuously for TBI patients. Methods : After ICP data preprocessing, the learning model is generated for thirteen patients to continuously predict the ICP signal occurrence and classify events for the upcoming 10 minutes by inputting the previous 20-minutes of the ICP signal. Results : As the overall model performance, the average accuracy is 94.62%, the average sensitivity is 74.91%, the average specificity is 94.83%, and the average root mean square error is approximately 2.18 mmHg. Conclusion : This research addresses a significant clinical problem with the management of traumatic brain injury patients. The machine learning model data enables early prediction of ICP continuously in a real-time fashion, which is crucial for appropriate clinical interventions. The results show that our machine learning-based model has high adaptive performance, accuracy, and efficiency.

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