Effect of Different N/C Coordination Electronic Structures on the Activity of Bifunctional Rare-Earth Ytterbium Electrocatalysts for Oxygen Electrodes.

The research and development of bifunctional electrocatalysts for the oxygen electrode is of great significance to solve the problem of electrochemical energy. Herein, the effect of different structure-activity relationships on the performance of YbNxCy-gra catalysts was explored. The bifunctional activity of graphene with a vacancy defect supported by single-atom rare-earth ytterbium was studied by density functional theory (DFT) calculations. We systematically analyzed the stability, electronic properties, and catalytic performance of potential bifunctional catalysts. The results showed that all catalysts were thermodynamically and kinetically stable. Under acidic conditions, YbN2C2-oppo-gra and YbN2C2-pen-gra showed good ORR activity, and their overpotentials were 0.53 and 0.65 V, respectively. In an alkaline environment, most of the Yb(OH)NxCy-gra catalysts showed excellent ORR and OER bifunctional catalytic activity. Their overpotentials were all below 0.6 V. In particular, the ηORR and ηOER of the Yb(OH)N4C0-gra electrocatalyst were as low as 0.33 and 0.42 V. This verified the practicability and feasibility of hydroxyl-modified catalysts to enhance activity. This research provides theoretical insights into the further design and development of high-efficiency rare-earth-supported bifunctional catalysts.

Co/Ce-MOF-Derived Oxygen Electrode Bifunctional Catalyst for Rechargeable Zinc-Air Batteries.

Improving the practicality of rechargeable zinc-air batteries relies heavily on the development of oxygen electrode catalysts that are low-cost, durable, and highly efficient in performing dual functions. In the present study, a catalyst with atomic Ce and Co distribution on a nitrogen-doped carbon substrate was prepared by doping the rare earth elements Ce and Co into a metal-organic framework precursor. Rare earth element Ce, known for its unique structure and excellent oxygen affinity, was utilized to regulate the catalytic activity. The catalyst prepared in this study demonstrated an exceptional electrocatalytic performance. At a current density of 10 mA cm-2, the catalyst exhibited an overpotential of 340 mV for the oxygen evolution reaction (OER), which was lower than that of commercial IrO2 (370 mV), while achieving a half-wave potential of 0.79 V for the process of oxygen reduction reaction (ORR), exhibiting a similar level of effectiveness as commercially accessible Pt/C catalysts (0.8 V). The catalyst's porous structure, interconnected three-dimensional carbon network, and large specific surface area are the factors contributing to the significant improvement in catalytic performance. Furthermore, in comparison to commercial Pt/C+IrO2, the catalyst exhibited good cycling stability and high efficiency in rechargeable zinc-air batteries.

Machine Learning-Assisted Study of RENxC6-x-Doped Graphene as Potential Electrocatalysts for Oxygen Electrode Reactions.

In the application of renewable energy, the oxidation-reduction reaction (ORR) and oxygen evolution reaction (OER) are two crucial reactions. Single-atom catalysts (SACs) based on metal-doped graphene have been widely employed due to their high activity and high atom utilization efficiency. However, the catalytic activity is significantly influenced by different metals and local coordination, making it challenging to efficiently screen through either experimental or density functional theory (DFT) calculations. To address this issue, this study employed a combination of DFT calculations and machine learning (DFT-ML) to investigate rare earth-modified carbon-based (RENxC6-x) electrocatalysts. Based on computational data from 75 catalysts, we trained two ML models to capture the underlying patterns of physical properties and overpotential. Subsequently, the candidate catalysts were screened, leading to the discovery of four ORR catalysts, nine OER catalysts, and five bifunctional electrocatalysts, all of which were thoroughly validated for their stability. Lastly, by integrating the ML models with the SHAP analysis framework, we revealed the influence of atomic radius, Pauling electronegativity, and other features on the catalytic activity. Additionally, we analyzed the physicochemical properties of potential catalysts through DFT calculations. The revolutionary DFT-ML approach provides a crucial driving force for the design and synthesis of potential catalysts in subsequent studies.

Mechanistic study of Eu single atoms occupying four vacancy centers as potential electrocatalysts for the oxygen reduction reaction.

The oxygen reduction reaction (ORR) on the oxygen electrode plays a critical role in rechargeable metal-air batteries, and the development of electrochemical energy storage and conversion technologies for the ORR is of great significance. In this study, the catalytic performance of rare earth-doped graphene (EuNxC6-x-Gra) as an electrocatalyst for the ORR was investigated. The results showed that a majority of the catalysts exhibited good ORR catalytic activity under acidic conditions, with some approaching or even surpassing commercial Pt-based catalysts (ηORR = 0.45 V). Particularly, EuN2C4-2-Gra demonstrated an ηORR of 0.38 V. It has been observed that the f-band center of Eu atoms increases with an increasing number of N atoms, and the charge distribution exhibits a "U" shape. There is a decreasing trend from N0 to N3 and an increasing trend from N4 to N6. By incorporating the proportional relationship of the adsorption free energies of reaction intermediates (ΔG*ads), a volcano diagram was constructed to rapidly assess catalytic activity. Finally, an intrinsic characteristic descriptor φ was formulated to quantitatively describe the relationship between φ and ηORR, providing a new tool for predicting and designing catalysts. This will provide guidance for the development and design of high-performance rare earth single atom catalysts.

A Lightweight Hybrid Model Using Multiscale Markov Transition Field for Real-Time Quality Assessment of Photoplethysmography Signals.

OBJECTIVE: The proliferation of wearable devices has escalated the standards for photoplethysmography (PPG) signal quality. This study introduces a lightweight model to address the imperative need for precise, real-time evaluation of PPG signal quality, followed by its deployment and validation utilizing our integrated upper computer and hardware system. METHODS: Multiscale Markov Transition Fields (MMTF) are employed to enrich the morphological information of the signals, serving as the input for our proposed hybrid model (HM). HM undergoes initial pre-training utilizing the MIMIC-III and UCI databases, followed by fine-tuning the Queensland dataset. Knowledge distillation (KD) then transfers the large-parameter model's knowledge to the lightweight hybrid model (LHM). LHM is subsequently deployed on the upper computer for real-time signal quality assessment. RESULTS: HM achieves impressive accuracies of 99.1% and 96.0% for binary and ternary classification, surpassing current state-of-the-art methods. LHM, with only 0.2 M parameters (0.44% of HM), maintains high accuracy despite a 2.6% drop. It achieves an inference speed of 0.023 s per image, meeting real-time display requirements. Furthermore, LHM attains a 97.7% accuracy on a self-created database. HM outperforms current methods in PPG signal quality accuracy, demonstrating the effectiveness of our approach. Additionally, LHM substantially reduces parameter count while maintaining high accuracy, enhancing efficiency and practicality for real-time applications. CONCLUSION: The proposed methodology demonstrates the capability to achieve high-precision and real-time assessment of PPG signal quality, and its practical validation has been successfully conducted during deployment. SIGNIFICANCE: This study contributes a convenient and accurate solution for the real-time evaluation of PPG signals, offering extensive application potential.

Evaluating the Oxygen Electrode Reactions of La Single-Atom Catalysts with the N/C Coordination Effect.

There is a growing demand for bifunctional electrocatalysts for oxygen electrodes in rechargeable metal-air batteries. This article investigates the bifunctional activity of La single-atom catalysts with N/C coordination (LaNxC6-x@Gra) using density functional theory (DFT). The augmentation of N coordination will result in enhanced synthetic stability. The coordination between nitrogen and carbon (N/C) has a significant influence on the working stability of the system under consideration. In the context of active atoms, the coordination between nitrogen and carbon (N/C coordination) has a significant impact on the electronic structure. This, in turn, influences the adsorption performance and catalytic activity of the catalysts. In the case of stable coordination environments, a correlation exists between the f-orbital center (εf) and the overpotential (η) via the adsorption free energy of intermediates (ΔG*ads). This correlation serves as a useful tool for predicting catalytic performance. The LaNxC6-x@Gra exhibits remarkable bifunctional activity due to its complementary performance, with an overpotential for the oxygen reduction reaction (ηORR) of 0.66 V and an overpotential for the oxygen evolution reaction (ηOER) of 0.43 V. This makes it a promising candidate for use as a bifunctional electrocatalyst in oxygen electrodes.

Biometric recognition based on scalable end-to-end convolutional neural network using photoplethysmography: A comparative study.

Photoplethysmography (PPG), as one of the most widely used physiological signals on wearable devices, with dominance for portability and accessibility, is an ideal carrier of biometric recognition for guaranteeing the security of sensitive information. However, the existing state-of-the-art methods are restricted to practical deployment since power-constrained and compute-insufficient for wearable devices. 1D convolutional neural networks (1D-CNNs) have succeeded in numerous applications on sequential signals. Still, they fall short in modeling long-range dependencies (LRD), which are extremely needed in high-security PPG-based biometric recognition. In view of these limitations, this paper conducts a comparative study of scalable end-to-end 1D-CNNs for capturing LRD and parameterizing authorized templates by enlarging the receptive fields via stacking convolution operations, non-local blocks, and attention mechanisms. Compared to a robust baseline model, seven scalable models have different impacts (-0.2%-9.9%) on the accuracy of recognition over three datasets. Experimental cases demonstrate clear-cut improvements. Scalable models achieve state-of-the-art performance with an accuracy of over 97% on VitalDB and with the best accuracy on BIDMC and PRRB datasets performing 99.5% and 99.3%, respectively. We also discuss the effects of capturing LRD in generated templates by visualizations with Gramian Angular Summation Field and Class Activation Map. This study conducts that the scalable 1D-CNNs offer a performance-excellent and complexity-feasible approach for biometric recognition using PPG.