Efficient Electrochemical Co-Reduction of Carbon Dioxide and Nitrate to Urea with High Faradaic Efficiency on Cobalt-Based Dual-Sites.

Renewable electricity-powered nitrate/carbon dioxide co-reduction reaction toward urea production paves an attractive alternative to industrial urea processes and offers a clean on-site approach to closing the global nitrogen cycle. However, its large-scale implantation is severely impeded by challenging C-N coupling and requires electrocatalysts with high activity/selectivity. Here, cobalt-nanoparticles anchored on carbon nanosheet (Co NPs@C) are proposed as a catalyst electrode to boost yield and Faradaic efficiency (FE) toward urea electrosynthesis with enhanced C-N coupling. Such Co NPs@C renders superb urea-producing activity with a high FE reaching 54.3% and a urea yield of 2217.5 µg h-1 mgcat. -1, much superior to the Co NPs and C nanosheet counterparts, and meanwhile shows strong stability. The Co NPs@C affords rich catalytically active sites, fast reactant diffusion, and sufficient catalytic surfaces-electrolyte contacts with favored charge and ion transfer efficiencies. The theoretical calculations reveal that the high-rate formation of *CO and *NH2 intermediates is crucial for facilitating urea synthesis.

An Energy-Efficient Wireless Power Receiver With One-Step Adiabatic-Bipolar-Supply Generating for Implantable Electrical Stimulation Applications.

This paper presents an energy-efficient wireless power receiver for implantable electrical stimulation applications, which can achieve one-step adiabatic bipolar-supply that is generated by a hybrid single-stage dual-output regulating (SSDOR) rectifiers. The structure using only four switches overcomes the disadvantages that the two output voltage values in the traditional dual-output rectifiers are close to each other. A constant-current (CC) controlled adiabatic dynamic voltage scaling (DVS) technique is proposed to minimize the voltage headroom of the stimulating drivers and improve the stimulation efficiency significantly. In addition, the receiver adopts only one general constant on-time (COT) low-frequency control to adjust the stimulation current, reducing both the power consumption and the complexity of the control circuits. The proposed receiver has been fabricated in a 0.18 µm BCD process with ±6 V voltage compliance and 2.5 mA maximum stimulating current. With a current range from ±1.5 mA to ±2.5 mA, the measured maximum average headroom voltage is only 80 mV and the peak total efficiency of the receiver is 85.6%. The functionalities of the proposed receiver have been successfully verified through in vitro experiments.

Event-Related EEG Desynchronization Reveals Enhanced Motor Imagery From the Third Person Perspective by Manipulating Sense of Body Ownership With Virtual Reality for Stroke Patients.

Virtual reality (VR)-based rehabilitation training holds great potential for post-stroke motor recovery. Existing VR-based motor imagery (MI) paradigms mostly focus on the first-person perspective, and the benefit of the third-person perspective (3PP) remains to be further exploited. The 3PP is advantageous for movements involving the back or those with a large range because of its field coverage. Some movements are easier to imagine from the 3PP. However, the 3PP training efficiency may be unsatisfactory, which may be attributed to the difficulty encountered when generating a strong sense of ownership (SOO). In this work, we attempt to enhance a visual-guided 3PP MI in stroke patients by eliciting the SOO over a virtual avatar with VR. We propose to achieve this by inducing the so-called out-of-body experience (OBE), which is a full-body illusion (FBI) that people misperceive a 3PP virtual body as his/her own (i.e., generating the SOO to the virtual body). Electroencephalography signals of 13 stroke patients are recorded while MI of the affected upper limb is being performed. The proposed paradigm is evaluated by comparing event-related desynchronization (ERD) with a control paradigm without FBI induction. The results show that the proposed paradigm leads to a significantly larger ERD during MI, indicating a bilateral activation pattern consistent with that in previous studies. In conclusion, 3PP MI can be enhanced in stroke patients by eliciting the SOO through induction of the "OBE" FBI. This study offers more possibilities for virtual rehabilitation in stroke patients and can further facilitate VR application in rehabilitation.

A 3-mV Precision Dual-Mode-Controlled Fast Charge Balancing for Implantable Biphasic Neural Stimulators.

This paper presents a novel charge balancing (CB) with a current-control (CC) mode and a voltage-control (VC) mode for implantable biphasic stimulators, which can achieve one-step accurate anodic pulse generating. Compared with the conventional short-pulse-injection-based CB, the proposed method could reduce the balancing time and avoid inducing undesired artifact. The CC operation compensates the majority stimulation charge at high speed, while the VC operation guarantees a high CB precision. In order to eliminate the oscillation during the mode transition, a smooth CC-VC transition method is adopted. In addition, a digital auxiliary monitoring loop is introduced against the variations of the tissue-electrode interface impedance during the stimulation process to meet long-term CB requirement. The proposed stimulator has been fabricated in a 0.18 µm BCD process with 10 V voltage compliance, and the measured CB precision is less than 3 mV. The functionalities of the proposed CB have been verified successfully through in vitro experiments.

High-efficiency electrocatalytic nitrite-to-ammonia conversion on molybdenum doped cobalt oxide nanoarray at ambient conditions.

Electrochemical conversion of nitrite (NO2-) contaminant to green ammonia (NH3) is a promising approach to achieve the nitrogen cycle. The slow kinetics of the complex multi-reaction process remains a serious issue, and there is still a need to design highly effective and selective catalysts. Herein, we report that molybdenum doped cobalt oxide nanoarray on titanium mesh (Mo-Co3O4/TM) acts as a catalyst to facilitate electroreduction of NO2- to NH3. Such a catalyst delivers an extremely high Faradaic efficiency of 96.9 % and a corresponding NH3 yield of 651.5 µmol h-1 cm-2 at -0.5 V with strong stability. Density functional theory calculations reveal that the introduction of Mo can induce the redistribution of electrons around Co atoms and further strengthen the adsorption of NO2-, which is the key to facilitating the catalytic performance. Furthermore, the assembled battery based on Mo-Co3O4/TM suggests its practical application value.

Fe3O4 nanoparticle-decorated 3D pinewood-derived carbon for high-efficiency electrochemical nitrate reduction to ammonia.

Electrochemical nitrate (NO3-) reduction is a sustainable pathway for ambient ammonia (NH3) synthesis while eliminating NO3- pollutants in water. However, the NO3- reduction reaction (NO3-RR) involves a complicated eight-electron transfer process, which needs highly selective and efficient electrocatalysts. This work describes the synthesis of Fe3O4 nanoparticle-decorated 3D pinewood-derived carbon (Fe3O4/PC) as a high-efficiency catalyst for the electroreduction of NO3- to NH3 at ambient reaction conditions. When tested in 0.1 M NaOH containing 0.1 M NO3-, the Fe3O4/PC obtains a large NH3 yield of 394.8 µmol h-1 cm-2 and high faradaic efficiency (FE) of 91.6% at -0.4 V. Significantly, Fe3O4/PC also delivers high stability.

The third-person perspective full-body illusion induced by visual-tactile stimulation in virtual reality for stroke patients.

This paper attempts to induce the third-person perspective full body illusion (3PP-FBI) with virtual reality (VR) in stroke patients. Nineteen individuals with stroke were recruited. The 3PP-FBI induction method, which was well-established in healthy individuals, using synchronous visual-tactile stimulation on one body part was used. Questionnaire scores and proprioceptive drift values were collected under different conditions for characterizing the induced 3PP-FBI. Results showed that synchronous visual-tactile stimulation of a single body part (back or upper limb) was sufficient to elicit 3PP-FBI in stroke patients, forming a sense of ownership (SOO) over the entire virtual body. Moreover, the intensity of 3PP-FBI was stronger when the back was stimulated, compared to stimulating the impaired upper limb. This study demonstrated the viability of visual-guided rehabilitation training while having a SOO to a virtual body from the third-person perspective, in anticipation of achieving better rehabilitation outcome for movements beyond the first-person perspective.

Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model.

The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and machine learning models have been developed to predict RR using physiological signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG) signals. Yet, the accuracy of these existing methods on available datasets remains limited, and their prediction on new data is also unsatisfactory for actual clinical applications. In this paper, we proposed an enhanced Transformer model with inception blocks for predicting RR based on both ECG and PPG signals. To evaluate the generalization capability on new data, our model was trained and tested using subject-level ten-fold cross-validation using data from both BIDMC and CapnoBase datasets. On the test set, our model achieved superior performance over five popular deep-learning-based methods with mean absolute error (1.2) decreased by 36.5% and correlation coefficient (0.85) increased by 84.8% compared to the best results of these models. In addition, we also proposed a new pipeline to preprocess ECG and PPG signals to improve model performance. We believe that the development of the TransRR model is expected to further expedite the clinical implementation of automatic RR estimation.

3D cauliflower-like Ni foam: a high-efficiency electrocatalyst for ammonia production via nitrite reduction.

A 3D cauliflower-like Ni foam on titanium plate (Ni foam/TP) shows high electrocatalytic performance for ambient ammonia (NH3) synthesis via nitrite (NO2-) reduction. In 0.1 M phosphate-buffered saline solution with 0.1 M NO2-, such Ni foam/TP attains a high NH3 Faradaic efficiency (FE) of 95.9% and a large NH3 yield of 742.7 µmol h-1 cm-2 at -0.8 V. Its Zn-NO2- battery offers a high power density of 6.2 mW cm-2 and an NH3 FE of 90.1%.

High-Performance Electrocatalytic Reduction of Nitrite to Ammonia under Ambient Conditions on a FeP@TiO2 Nanoribbon Array.

Electrochemical nitrite (NO2-) reduction is recognized as a promising strategy for synthesizing valuable ammonia (NH3) and degrading NO2- pollutants in wastewater. The six-electron process for the NO2- reduction reaction is complex and necessitates a highly selective and stable electrocatalyst for efficient conversion of NO2- to NH3. Herein, a FeP nanoparticle-decorated TiO2 nanoribbon array on a titanium plate (FeP@TiO2/TP) is proposed as an efficient catalyst for NH3 production under ambient conditions. In 0.1 M NaOH with 0.1 M NO2-, such a FeP@TiO2/TP affords a large NH3 yield of 346.6 µmol h-1 cm-2 and a high Faradaic efficiency of 97.1%. Additionally, it demonstrates excellent stability and durability during long-term cycling tests and electrolysis experiments.

Pd-Doped Co3 O4 Nanoarray for Efficient Eight-Electron Nitrate Electrocatalytic Reduction to Ammonia Synthesis.

Ammonia (NH3 ) is an indispensable feedstock for fertilizer production and one of the most ideal green hydrogen rich fuel. Electrochemical nitrate (NO3 - ) reduction reaction (NO3 - RR) is being explored as a promising strategy for green to synthesize industrial-scale NH3 , which has nonetheless involved complex multi-reaction process. This work presents a Pd-doped Co3 O4 nanoarray on titanium mesh (Pd-Co3 O4 /TM) electrode for highly efficient and selective electrocatalytic NO3 - RR to NH3 at low onset potential. The well-designed Pd-Co3 O4 /TM delivers a large NH3 yield of 745.6 µmol h-1 cm-2 and an extremely high Faradaic efficiency (FE) of 98.7% at -0.3 V with strong stability. These calculations further indicate that the doping Co3 O4 with Pd improves the adsorption characteristic of Pd-Co3 O4 and optimizes the free energies for intermediates, thereby facilitating the kinetics of the reaction. Furthermore, assembling this catalyst in a Zn-NO3 - battery realizes a power density of 3.9 mW cm-2 and an excellent FE of 98.8% for NH3 .

Ruthenium doping: An effective strategy for boosting nitrite electroreduction to ammonia over titanium dioxide nanoribbon array.

Electrochemical reduction of nitrite (NO2-) not only removes NO2- contaminant but also produces high-added value ammonia (NH3). This process, however, needs efficient and selective catalysts for NO2--to-NH3 conversion. In this study, Ruthenium doped titanium dioxide nanoribbon array supported on Ti plate (Ru-TiO2/TP) is proposed as an efficient electrocatalyst for the reduction of NO2- to NH3. When operated in 0.1 M NaOH containing NO2-, such Ru-TiO2/TP achieves an ultra-large NH3 yield of 1.56 mmol h-1 cm-2 and a super-high Faradaic efficiency of 98.9%, superior to its TiO2/TP counterpart (0.46 mmol h-1 cm-2, 74.1%). Furthermore, the reaction mechanism is studied by theoretical calculation.

High-Efficiency Electroreduction of Nitrite to Ammonia on Ni Nanoparticles Strutted 3D Honeycomb-Like Porous Carbon Framework.

Electroreduction of nitrite (NO2 - ) to ammonia (NH3 ) provides a sustainable approach to yield NH3 , whilst eliminating NO2 - contaminants. In this study, Ni nanoparticles strutted 3D honeycomb-like porous carbon framework (Ni@HPCF) is fabricated as a high-efficiency electrocatalyst for selective reduction of NO2 - to NH3 . In 0.1 M NaOH with NO2 - , such Ni@HPCF electrode obtains a significant NH3 yield of 12.04 mg h-1 mgcat. -1 and a Faradaic efficiency of 95.1 %. Furthermore, it exhibits good long-term electrolysis stability.

Rational Construction of Heterostructured Cu3 P@TiO2 Nanoarray for High-Efficiency Electrochemical Nitrite Reduction to Ammonia.

Electroreduction of nitrite (NO2 - ) to valuable ammonia (NH3 ) offers a sustainable and green approach for NH3 synthesis. Here, a Cu3 P@TiO2 heterostructure is rationally constructed as an active catalyst for selective NO2 - -to-NH3 electroreduction, with rich nanosized Cu3 P anchored on a TiO2 nanoribbon array on Ti plate (Cu3 P@TiO2 /TP). When performed in the 0.1 m NaOH with 0.1 m NaNO2 , the Cu3 P@TiO2 /TP electrode obtains a large NH3 yield of 1583.4 µmol h-1  cm-2 and a high Faradaic efficiency of 97.1%. More importantly, Cu3 P@TiO2 /TP also delivers remarkable long-term stability for 50 h electrolysis. Theoretical calculations indicate that intermediate adsorption/conversion processes on Cu3 P@TiO2 interfaces are synergistically optimized, substantially facilitating the conversion of NO2 - -to-NH3 .

Defective TiO2- x for High-Performance Electrocatalytic NO Reduction toward Ambient NH3 Production.

Synthesis of green ammonia (NH3 ) via electrolysis of nitric oxide (NO) is extraordinarily sustainable, but multielectron/proton-involved hydrogenation steps as well as low concentrations of NO can lead to poor activities and selectivities of electrocatalysts. Herein, it is reported that oxygen-defective TiO2 nanoarray supported on Ti plate (TiO2- x /TP) behaves as an efficient catalyst for NO reduction to NH3 . In 0.2 m phosphate-buffered electrolyte, such TiO2- x /TP shows competitive electrocatalytic NH3 synthesis activity with a maximum NH3 yield of 1233.2 µg h-1  cm-2 and Faradaic efficiency of 92.5%. Density functional theory calculations further thermodynamically faster NO deoxygenation and protonation processes on TiO2- x (101) compared to perfect TiO2 (101). And the low energy barrier of 0.7 eV on TiO2- x (101) for the potential-determining step further highlights the greatly improved intrinsic activity. In addition, a Zn-NO battery is fabricated with TiO2- x /TP and Zn plate to obtain an NH3 yield of 241.7 µg h-1  cm-2 while providing a peak power density of 0.84 mW cm-2 .

RNA independent fragment partition method based on deep learning for RNA secondary structure prediction.

The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures of long RNA sequences with high precision and reasonable computational cost remains challenging. Here, we propose a deep learning model, RNA-par, which could partition an RNA sequence into several independent fragments (i-fragments) based on its exterior loops. Each i-fragment secondary structure predicted individually could be further assembled to acquire the complete RNA secondary structure. In the examination of our independent test set, the average length of the predicted i-fragments was 453 nt, which was considerably shorter than that of complete RNA sequences (848 nt). The accuracy of the assembled structures was higher than that of the structures predicted directly using the state-of-the-art RNA secondary structure prediction methods. This proposed model could serve as a preprocessing step for RNA secondary structure prediction for enhancing the predictive performance (especially for long RNA sequences) and reducing the computational cost. In the future, predicting the secondary structure of long-sequence RNA with high accuracy can be enabled by developing a framework combining RNA-par with various existing RNA secondary structure prediction algorithms. Our models, test codes and test data are provided at https://github.com/mianfei71/RNAPar .

High-efficiency electroreduction of nitrite to ammonia on a Cu@TiO2 nanobelt array.

Electrochemical nitrite (NO2-) reduction is a potential and sustainable route to produce high-value ammonia (NH3), but it requires highly active electrocatalysts. Herein, Cu nanoparticles anchored on a TiO2 nanobelt array on a titanium plate (Cu@TiO2/TP) are reported as a high-efficiency electrocatalyst for NO2--to-NH3 conversion. The designed Cu@TiO2/TP catalyst exhibits outstanding catalytic performance toward the NO2-RR, with a high NH3 yield of 760.5 µmol h-1 cm-2 (237.7 µmol h-1 mgcat.-1) and an excellent faradaic efficiency of 95.3% in neutral solution. Meanwhile, it also presents strong electrochemical stability during cyclic tests and long-term electrolysis.

Constructing Co@TiO2 Nanoarray Heterostructure with Schottky Contact for Selective Electrocatalytic Nitrate Reduction to Ammonia.

Electrochemical nitrate (NO3 - ) reduction reaction (NO3 - RR) is a potential sustainable route for large-scale ambient ammonia (NH3 ) synthesis and regulating the nitrogen cycle. However, as this reaction involves multi-electron transfer steps, it urgently needs efficient electrocatalysts on promoting NH3  selectivity. Herein, a rational design of Co nanoparticles anchored on TiO2  nanobelt array on titanium plate (Co@TiO2 /TP) is presented as a high-efficiency electrocatalyst for NO3 - RR. Density theory calculations demonstrate that the constructed Schottky heterostructures coupling metallic Co with semiconductor TiO2  develop a built-in electric field, which can accelerate the rate determining step and facilitate NO3 - adsorption, ensuring the selective conversion to NH3 . Expectantly, the Co@TiO2 /TP electrocatalyst attains an excellent Faradaic efficiency of 96.7% and a high NH3  yield of 800.0 µmol h-1  cm-2  under neutral solution. More importantly, Co@TiO2 /TP heterostructure catalyst also presents a remarkable stability in 50-h electrolysis test.

Unveiling selective nitrate reduction to ammonia with Co3O4 nanosheets/TiO2 nanobelt heterostructure catalyst.

Electrochemical nitrate (NO3-) reduction reaction (NO3RR) possesses two-pronged properties for sustainable ammonia (NH3) synthesis and mitigating NO3- contamination in water. However, the sluggish kinetics for the direct eight-electron NO3--to-NH3 conversion makes a formidable challenge to develop efficient electrocatalysts. Herein, we report a heterostructure of Co3O4 nanosheets decorated TiO2 nanobelt array on titanium plate (Co3O4@TiO2/TP) as an efficient NO3RR electrocatalyst. Both experimental and density theory calculations reveal that the heterostructure of Co3O4@TiO2 establishes a built-in electric field which can optimize the electron migration kinetics, as well as facilitate the adsorption and fixation of NO3- on the electrode surface, ensuring the selectivity to NH3. As expected, the designed Co3O4@TiO2/TP exhibits a remarkable Faradaic efficiency of 93.1 % and a remarkable NH3 yield as high as 875 µmol h-1 cm-2, superior to Co3O4/TP and TiO2/TP. Significantly, it also demonstrates strong electrochemical durability.

Ambient Ammonia Synthesis via Nitrate Electroreduction in Neutral Media on Fe3O4 Nanoparticles-decorated TiO2 Nanoribbon Array.

Electrochemical nitrate (NO3-) reduction is a potential approach to produce high-value ammonia (NH3) while removing NO3- pollution, but it requires electrocatalysts with high efficiency and selectivity. Herein, we report the development of Fe3O4 nanoparticles decorated TiO2 nanoribbon array on titanium plate (Fe3O4@TiO2/TP) as an efficient electrocatalyst for NO3--to-NH3 conversion. When operated in 0.1 M phosphate-buffered saline and 0.1 M NO3-, such Fe3O4@TiO2/TP achieves a prominent NH3 yield of 12394.3 µg h-1 cm-2 and a high Faradaic efficiency of 88.4%. In addition, it exhibits excellent stability during long-time electrolysis.