Two-step Strategy Research Articles

Privacy-preserving top-[formula omitted] spatio-temporal keyword preference query

With the rapid advancement of location-based services and mobile devices, spatial keyword query attracts increasing attention. In this paper, we focus on a new query type known as top-k spatio-temporal keyword preference query. This kind of query considers both the spatial object itself and other spatial objects in the neighborhood to return k spatial objects with the highest score. These k spatial objects satisfy spatial and temporal constraints, while their scores are determined by the keyword similarity of the neighboring spatial objects. We propose a scheme to enable privacy-preserving top-k spatio-temporal keyword preference queries. To effectively represent temporal information, we employ time vectors to denote time periods, allowing us to assess whether the data satisfies temporal constraints based on the inner product of time vectors. Furthermore, we adopt a two-step strategy to execute the query. The first step is to find all Points of Interest (POIs) that meet the spatial and temporal constraints. The second step is to calculate the score of each POI and return the top-k POIs with the highest score. To enhance query efficiency, we build a tree index structure that can achieve sub-linear query complexity. Additionally, we utilize EASPE algorithm to encrypt both the index and the query, ensuring privacy-preserving capabilities. Security analysis proves that our scheme satisfies CQA2-security. At the same time, experimental evaluation validates the query performance of our scheme.

High production of enantiopure (R,R)-2,3-butanediol from crude glycerol by Klebsiella pneumoniae with an engineered oxidative pathway and a two-stage agitation strategy

Background(R,R)-2,3-butanediol (BDO) is employed in a variety of applications and is gaining prominence due to its unique physicochemical features. The use of glycerol as a carbon source for 2,3-BDO production in Klebsiella pneumoniae has been limited, since 1,3-propanediol (PDO) is generated during glycerol fermentation.ResultsIn this study, the inactivation of the budC gene in K. pneumoniae increased the production rate of (R,R)-2,3-BDO from 21.92 ± 2.10 to 92.05 ± 1.20%. The major isomer form of K. pneumoniae (meso-2,3-BDO) was shifted to (R,R)-2,3-BDO. The purity of (R,R)-2,3-BDO was examined by agitation speed, and 98.54% of (R,R)-2,3-BDO was obtained at 500 rpm. However, as the cultivation period got longer, the purity of (R,R)-2,3-BDO declined. For this problem, a two-step agitation speed control strategy (adjusted from 500 to 400 rpm after 24 h) and over-expression of the dhaD gene involved in (R,R)-2,3-BDO biosynthesis were used. Nevertheless, the purity of (R,R)-2,3-BDO still gradually decreased over time. Finally, when pure glycerol was replaced with crude glycerol, the titer of 89.47 g/L of (R,R)-2,3-BDO (1.69 g/L of meso-2,3-BDO), productivity of 1.24 g/L/h, and yield of 0.35 g/g consumed crude glycerol was achieved while maintaining a purity of 98% or higher.ConclusionsThis study is meaningful in that it demonstrated the highest production and productivity among studies in that produced (R,R)-2,3-BDO with a high purity in Klebsiella sp. strains. In addition, to the best of our knowledge, this is the first study to produce (R,R)-2,3-BDO using glycerol as the sole carbon source.

Designing a cost-effective policy mix for transition toward net-zero emissions: a case study of the mid-term plan by 2035 of Taiwan

This study explores the design of a cost-effective climate policy mix for transition toward net-zero emissions in the mid-term (by 2035). Its novelty lies in the methodological advancements through the development of a soft-linking procedure that integrates a top-down computable general equilibrium model with a bottom-up energy system model. The integrated assessment focuses on the climate policy mix under the framework of Taiwan’s Climate Change Response Act passed in 2023. The analysis shows that a policy mix combining investments in energy transition with user-pay hybrid pricing measures can facilitate cost-effective mitigation and enhance public acceptance. The proposed hybrid pricing includes cost-based electricity pricing and a two-step carbon pricing strategy that encourages early action (before 2030) for larger emitters. Finally, we demonstrate that accelerating the transition is crucial, as it reduces the economic costs of meeting emission targets by inducing further investments in energy efficiency improvement and lowering the carbon pricing levels required to close the abatement gap.

Spatial–Temporal Joint Design and Optimization of Phase-Coded Waveform for MIMO Radar

By simultaneously transmitting multiple different waveform signals, a multiple-input multiple-output (MIMO) radar possesses higher degrees of freedom and potential in many aspects compared to a traditional phased-array radar. The spatial–temporal characteristics of waveforms are the key to determining their performance. In this paper, a transmitting waveform design method based on spatial–temporal joint (STJ) optimization for a MIMO radar is proposed, where waveforms are designed not only for beam-pattern matching (BPM) but also for minimizing the autocorrelation sidelobes (ACSLs) of the spatial synthesis signals (SSSs) in the directions of interest. Firstly, the STJ model is established, where the two-step strategy and least squares method are utilized for BPM, and the L2p-Norm of the ACSL is constructed as the criterion for temporal characteristics optimization. Secondly, by transforming it into an unconstrained optimization problem about the waveform phase and using the gradient descent (GD) algorithm, the hard, non-convex, high-dimensional, nonlinear optimization problem is solved efficiently. Finally, the method’s effectiveness is verified through numerical simulation. The results show that our method is suitable for both orthogonal and partial-correlation MIMO waveform designs and efficiently achieves better spatial–temporal characteristic performances simultaneously in comparison with existing methods.

Improving Typhoon Muifa (2022) Forecasts with FY-3D and FY-3E MWHS-2 Satellite Data Assimilation under Clear Sky Conditions

This study investigates the impacts of assimilating the Microwave Humidity Sounder II (MWHS-2) radiance data carried on the FY-3D and FY-3E satellites on the analyses and forecasts of Typhoon Muifa in 2022 under clear-sky conditions. Data assimilation experiments are conducted using the Weather Research and Forecasting (WRF) model coupled with the Three-Dimensional Variational (3D-Var) Data Assimilation method to compare the different behaviors of FY-3D and FY-3E radiances. Additionally, the data assimilation strategies are assessed in terms of the sequence of applying the conventional and MWHS-2 radiance data. The results show that assimilating MWHS-2 data is able to enhance the dynamic and thermal structures of the typhoon system. The experiment with FY-3E MWHS-2 assimilated demonstrated superior performance in terms of simulating the typhoon’s structure and providing a prediction of the typhoon’s intensity and track than the experiment with FY-3D MWHS-2 did. The two-step assimilation strategy that assimilates conventional observations before the radiance data has improved the track and intensity forecasts at certain times, particularly with the FY-3E MWHS-2 radiance. It appears that large-scale atmospheric conditions are more refined by initially assimilating the Global Telecommunication System (GTS) data, with subsequent satellite data assimilation further adjusting the model state. This strategy has also confirmed improvements in precipitation prediction as it enhances the dynamic and thermal structures of the typhoon system.

Improving deep groundwater aquifer characterization with deep learning inversion of audio-frequency magnetotelluric data

Deep groundwater is a crucial resource for drinking, industry, and ecosystems. However, its extensive subsurface distribution poses challenges for traditional hydrological sampling methods. Audio-frequency Magnetotelluric (AMT) is commonly used to image shallow subsurface resistivity distribution, but its application for quantifying deep groundwater aquifers is challenging. In this study, we propose a two-step strategy deep learning (DL) to estimate subsurface water information from AMT data. First, we employ an inversion DL network to directly predict subsurface resistivity distribution from AMT data. To assess the impact of data and prior information on DL inversions, we progressively include more input data during training, from apparent resistivity to apparent resistivity, phase, and Bostick transformed initial models. In the second step, we use another DL network to predict subsurface structure from AMT inversion results. We then estimate water content based on unit-specific petrophysical relationships. Evaluating our algorithm with synthetic cases, we find that including more information generally improves network performance, particularly when incorporating initial Bostick transformed models. Applying the algorithm to a field hydrogeological survey, we compare the inversion network trained with apparent resistivity, phase, and Bostick transformed initial models to traditional regularization inversions. The new approach shows better consistency with borehole data. Another network extracts structure information, enabling the estimation of subsurface water content and gaining valuable hydrogeological insights. To summarize, our study presents a novel DL-based approach to quantitatively delineate deep groundwater aquifers using AMT data. The proposed algorithm demonstrates promising performance in estimating subsurface hydrologic properties, providing valuable insights for hydrogeological investigations.

Rational Construction of a 3D Self-Supported Electrode Based on ZIF-67 and Amorphous NiCoP for an Enhanced Oxygen Evolution Reaction.

The development of efficient and Earth-abundant electrocatalysts for the oxygen evolution reaction (OER) is an urgent requirement in the field of electrochemical water splitting. The electrocatalytic performance of the OER can be greatly enhanced by the synergistic combination of zeolite imidazolate frameworks (ZIFs) and transition-metal phosphides, both of which individually exhibit promising capabilities in this regard. In this study, a novel amorphous NiCoP deposited on ZIF-67 sheets supported on Ni foam (labeled as NiCoP/ZIF-67/NF) as an OER electrocatalytic material was successfully synthesized using a simple, secure, and time-efficient two-step strategy. The experimental results demonstrate that NiCoP/ZIF-67/NF possesses a large active surface area with abundant active sites. Also, the synergistic effect and interaction between NiCoP and ZIF-67, as well as between Ni and Co within NiCoP, effectively enhance its electrochemical performance under alkaline conditions. Consequently, NiCoP/ZIF-67/NF exhibits outstanding catalytic activity for OER with an overpotential (η) of 175 mV at a current density of 10 mA cm-2 and a long-term stability over 40 h at 20 mA cm-2 in a 1.0 M KOH electrolyte. The corresponding analyses suggest that the real active sites responsible for the OER are identified as NiOOH and CoOOH species within the structure of NiCoP/ZIF-67/NF. Additionally, the catalytic function and stability of ZIF-67 toward the OER under alkaline conditions were also briefly discussed. This work provides a novel catalytic material for the OER along with a facile strategy to fabricate superior, efficient, and noble metal-free catalysts suitable for energy-related applications.

Synergistic nitrate removal: Coupling electrocatalysis and chemical reduction for exceptional nitrogen selectivity

Nitrate pollution in natural water bodies poses a severe threat to human health and the environment. Developing efficient and environmentally friendly nitrate removal methods has become a global need. Electrochemical techniques offer a cost-effective strategy for nitrate remediation; however, the complexity of the reaction and the formation of multiple by-products make the selective production of harmless nitrogen a challenging task. This study proposes a novel two-step strategy to achieve high efficiency and selectivity in converting nitrate into nitrogen. First, a membrane electrode consisting of silver nanowires (Ag NWs) is used to electrochemically reduce nitrate to nitrite through a direct electron transfer pathway. Whether in a static electrolytic cell or a flow cell, the process has very high nitrite selectivity and Faraday efficiencies, with Faraday efficiency up to 98.40 % and selectivity up to 99.90 % at −1.0 V vs Ag/AgCl. Subsequently, the nitrite is converted into nitrogen through a specific reaction with sulfamic acid with near 100 % conversion and selectivity. By coupling these two reactions, the selectivity for converting nitrate to nitrogen can reach more than 99 %. In addition, the membrane electrode can be readily used in a flow-through electrolytic cell to treat nitrates at a maximum rate of 150 L h−1 m−2. When operated at 50 L h−1 m−2, the membrane electrode demonstrates relatively stable performance for 100 h, reducing nitrates at 1.61 mM to below 0.81 mM (50 ppm), as recommended by the World Health Organization for drinking water. This novel synergistic approach not only enhances nitrogen selectivity but also simplifies the process of electrocatalytic nitrate reduction, making it a promising method for nitrate removal.

Promoting physical activity and healthy diets by modifying the social and/or physical environment at local level: a scoping review of evidence-based policy actions.

Background We aimed to map evidenced-based policy actions proposed by public health institutes or organizations to promote physical activity and healthy diets by modifying the social and/or physical environment at the local level. Methods We conducted a scoping review to identify relevant evidence-based policy actions proposed by public health institutions. We used a two-step strategy to identify government-supported public health institutes or organizations that generate evidence-based recommendations for policy actions. We included policy actions if they 1) aimed at increasing physical activity or improving diet; 2) focused on modifying the physical or social environments; 3) were implemented at the local or community level; 4) were expressed as a concrete action rather than a general aim; and 5) were described explicitly as being based on evidence. Results Starting from 121 public health institutes, we identified 8 relevant organizations and reviewed 63 guidelines or reports that included actions to promote healthy diets and physical activity. Of the 540 proposed actions on diet and 358 on physical activity, 118 met the inclusion criteria. Given that many of the actions were recommended by multiple institutes, we synthesized the information in the infographics to provide recommendations on diet and physical activity in outdoor and indoor settings and schools. Conclusions Public health institutes have generated a wide range of evidence-based recommendations for the promotion of healthy diets and physical activity by modifying the physical and social environment that can be implemented in local settings. Future actions should address barriers to implementing these recommendations and analyze the determinants of local policy decisions.

Does Two-Step Infusion Improve the Pharmacokinetics/Pharmacodynamics Target Attainment of Meropenem in Critically Ill Patients?

The optimal method for administering meropenem remains controversial. This study was conducted to explore the optimal two-step infusion strategy (TIT), and to investigate whether TIT is superior to intermittent infusion therapy (IIT) and prolonged infusion therapy (PIT). A physiologically based pharmacokinetics model for critically ill patients was established and evaluated. The validated model was utilized to evaluate the pharmacokinetics/pharmacodynamics (PK/PD) target attainment of meropenem. The PK/PD target attainment of different TITs varied greatly, and the total infusion duration and the first-step dose greatly affected these values. The optimal TIT was 0.25 g (30 min) + 0.75 g (150 min) at MICs of ≤2 mg/L, and 0.25 g (45 min) + 0.75 g (255 min) at MICs of 4–8 mg/L. The PK/PD target attainment of optimal TIT, PIT, and IIT were 100 % at MICs of ≤1 mg/L. When MIC increased to 2–8 mg/L, the PK/PD target attainment of optimal TIT was similar to that of PIT and higher than IIT. In conclusion, TIT did not significantly improve the PK/PD target attainment of meropenem compared with PIT. IIT is adequate at MICs of ≤1 mg/L, and PIT may be the optimal meropenem infusion method in critically ill patients with MICs of 2–8 mg/L.

Promising approach for targeting ROBO1 with CAR NK cells to combat ovarian cancer primary tumor cells and organoids.

Aim: This study aimed to explore using peripheral blood mononuclear cell (PBMC)-derived chimeric antigen receptor (CAR) NK cells targeting ROBO1 as a personalized medicine approach for ovarian cancer. Methods: A two-step strategy generated ROBO1-targeted CAR NK cells from PBMCs of ovarian cancer patients. Efficacy was evaluated using xCELLigence RTCA, CCK-8 and Live/Dead fluorescence assays. Results: ROBO1-NK cells exhibited higher efficiency in eradicating primary ovarian cancer cells and lysing ovarian tumor organoids compared with primary NK cells without ROBO1-CAR modification. Conclusion: These findings highlight the potential of developing ROBO1-targeted CAR-NK cells from patients' PBMCs as a personalized treatment option for ovarian cancer.

Enhanced p-type conductivity of hexagonal boron nitride by an efficient two-step doping strategy

The present study proposes a two-step doping strategy for achieving efficient Mg doping of h-BN, involving an additional post-annealing treatment. This approach leads to ∼108-fold enhancement in conductivity of h-BN, compared with the as-doped h-BN grown by low-pressure chemical vapor deposition. The mechanism for large enhancement in h-BN doping efficiency after post-annealing was investigated, providing evidence that this treatment not only facilitates the nanoparticle decomposition and incorporation of Mg atoms into h-BN, but also restores its lattice defects. The efficient two-step doping strategy for p-type h-BN in this study enlightens its promising prospects for ultraviolet optoelectronic devices.

Hypervalent Iodine Promoted Selective [2 + 2 + 1] Cycloaddition of Aromatic Ketones and Methylamines: A One-Pot Access to 1-Pyrrolines.

Herein, a versatile highly regioselective three-component annulation of simple aromatic ketones and methylamines using a hypervalent iodine reagent for polyarylated 1-pyrrolines has been described in good to excellent yields. Meanwhile, unsymmetrical 1-pyrroline isomers could be realized and synthesized. Such an intriguing one-pot two-step tandem assembly strategy with green conditions and high regioselectivity shows predictable inspiration in related annulation reactions.

Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4

Large language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently, particularly in dermatological diagnosis, which is a very important task as skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases. Here we present SkinGPT-4, which is an interactive dermatology diagnostic system based on multimodal large language models. We have aligned a pre-trained vision transformer with an LLM named Llama-2-13b-chat by collecting an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors’ notes, and designing a two-step training strategy. We have quantitatively evaluated SkinGPT-4 on 150 real-life cases with board-certified dermatologists. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identify the characteristics and categories of the skin conditions, perform in-depth analysis, and provide interactive treatment recommendations.

A two-step strategy to identify episodic sources of gravitational waves and high-energy neutrinos in starburst galaxies

A two-step strategy to identify episodic sources of gravitational waves and high-energy neutrinos in starburst galaxies

P-528 Establishing sequencing coverage depth for preimplantation genetic testing for monogenic disorders (PGT-M) using a specific targeted next generation sequencing (tNGS)

Abstract Study question What is the sufficient sequencing coverage read depth (RD) for direct variant analysis for PGT-M using a specific tNGS assay? Summary answer Downsampling modelling experiments identified a minimum sequencing depth of 50X that is recommended for PGT-M with this specific tNGS assay. What is known already Properly developed and validated multiplex PCR amplification and NGS-based analysis reliably produces PGT-M diagnoses with low allele and amplification failure at RD of hundreds of reads when analyzing a 3-10 cell trophectoderm (TE) biopsy. However, the RD can be affected by amplification efficiency in a specific genomic region and therefore affect the reproducibility of variant detection: the higher the number of aligned reads, the higher the confidence of base calling at a particular position. Downsampling is a powerful bioinformatics tool for modelling comparisons between samples or between replicates, identifying analytical sequencing depth limits for accurate clinical diagnosis. Study design, size, duration This is a retrospective comparative diagnostic test accuracy (DTA) and verification study. Twenty-three embryos from 8 patients that had undergone PGT-M with clinically established Taqman qPCR genotyping were used as positive controls. The primary outcome was to establish the lowest RD point of down sampling where allelic frequency is reliably detected at heterozygosity between 20-80% in known positive controls. A further 30 PGT-M clinical cases were used to verify analytical performances under clinical conditions. Participants/materials, setting, methods Embryo biopsies were pre-amplified with a multiplex primer pool for PGT-M and PGT-A, using a two-step PCR strategy. Sequencing was performed on Illumina NextSeq550. Reads were aligned to a human reference genome (GRCh37/hg19) and processed with an in house bioinformatic pipeline. The RD and alternative allele frequencies (AAF) for PGT-M was calculated. The reads for the heterozygous variants were downsampled to 10X, 20X, 30X, 50X, 100X, 200X, 300X, 500X, and 1000X to establish the cutoff. Main results and the role of chance One or two TE biopsies (N = 44) of 23 embryos that were 100% concordant with previous PGT-M results were tested for 1 to 3 of the 12 variants (MLH1 c.199G>A, RB1 c.1589A>G, SLC16A2 c.604G>A, GNPTAB c.1123C>T, GNPTAB c.3314A>G, WFS1 c.1391C>G, USH2A c.10073G>A, USH2A del exons 5-11, F5 c.1601G>A, GAA c.693-1G>C, GAA c.875A>G, and HEXA c.1274_1277dupTATC). PGT-A analysis of multiple biopsies from the same embryo demonstrated consistent karyotypes (N = 44). The 19 heterozygous variants from 17 biopsies exhibited average RD of 763X (238X -1980X) with average AAF of 50% (44%- 56%). The reads for the heterozygous variants were downsampled to different levels (100,000 replicates). The distribution of the AAF were compared among the different depth levels. At a RD of 50X, the AAF displayed an interquartile range (IQR) of 8% (46%-54%) between the maximum and minimum values from 20% to 78%. Compared to 30X (IQR: 43%-57%, with minimum 13% and maximum 90%), and 100X (IQR: 47%-54%, with minimum 30% and maximum 71%), the RD at 50X generated reliable heterozygous calls with minimum depth requirement. 245 biopsies from 30 clinic cases were analyzed by tNGS-based PGT-M. 90 samples with heterozygous variant calls showed average RD of 979X (57X-5803X) with average AAF of 47% (29%-585). Limitations, reasons for caution The 50X cutoff is based on the heterozygous variants with theoretical 50% AFF and is specific for this PGT-A assay. This evaluation should be corroborated for complicated variants with homologous genes, which might require different thresholds. The aneuploidy at specific chromosome can cause the deviation of AFF from 50%. Wider implications of the findings This specific tNGS-based PGT-M can provide direct variant analysis at a 50X read depth with a high level of consistency compared to established methodologies while allowing for simultaneous aneuploidy screening using the same biopsy and procedural optimization. Trial registration number not applicable

Pig blastocyst-like structure models from embryonic stem cells

Pluripotent stem cells have the potential to generate embryo models that can recapitulate developmental processes in vitro. Large animals such as pigs may also benefit from stem-cell-based embryo models for improving breeding. Here, we report the generation of blastoids from porcine embryonic stem cells (pESCs). We first develop a culture medium 4FIXY to derive pESCs. We develop a 3D two-step differentiation strategy to generate porcine blastoids from the pESCs. The resulting blastoids exhibit similar morphology, size, cell lineage composition, and single-cell transcriptome characteristics to blastocysts. These porcine blastoids survive and expand for more than two weeks in vitro under two different culture conditions. Large animal blastoids such as those derived from pESCs may enable in vitro modeling of early embryogenesis and improve livestock species’ breeding practices.

Late Fusion Multiview Clustering via Min-Max Optimization.

Multiview clustering (MVC) sufficiently exploits the diverse and complementary information among different views to improve the clustering performance. As a representative algorithm of MVC, the newly proposed simple multiple kernel k -means (SimpleMKKM) algorithm takes a min-max formulation and applies a gradient descent algorithm to decrease the resultant objective function. It is empirically observed that its superiority is attributed to the novel min-max formulation and the new optimization. In this article, we propose to integrate the min-max learning paradigm adopted by SimpleMKKM into late fusion MVC (LF-MVC). This leads to a tri-level max-min-max optimization problem with respect to the perturbation matrices, weight coefficient, and clustering partition matrix. To solve this intractable max-min-max optimization problem, we design an efficient two-step alternative optimization strategy. Furthermore, we analyze the generalization clustering performance of the proposed algorithm from the theoretical perspective. Comprehensive experiments have been conducted to evaluate the proposed algorithm in terms of clustering accuracy (ACC), computation time, convergence, as well as the evolution of the learned consensus clustering matrix, clustering with different numbers of samples, and analysis of the learned kernel weight. The experimental results show that the proposed algorithm is able to significantly reduce the computation time and improve the clustering ACC when compared to several state-of-the-art LF-MVC algorithms. The code of this work is publicly released at: https://xinwangliu.github.io/Under-Review.

Myth versus mystery: A scoping review of the involvement of behavioral science in implementing evidence-informed decision-making initiatives

Literature suggests that using behavioral theory to inform research can improve the efficiency of knowledge translation (KT) interventions and guide their implementation. Therefore, we conducted a scoping review of the use of behavioral theories, models, and frameworks in the published literature to guide the implementation of evidence-informed decision-making (EIDM) interventions. We used a thorough two-step search strategy to retrieve articles fulfilling the inclusion criteria published through August 2022. Two reviewers initially screened the articles individually, followed by two reviewers who independently reviewed the articles’ full text. We critically appraised 90 full-text articles from 3172 retrieved articles based on inclusion and exclusion criteria. We finally included 19 articles for qualitative synthesis, which included the elements of KT intervention, underlying theories/models of behavior change and the purpose of their use, and facilitators and barriers to KT intervention. Our results showed that no study explicitly focused on EIDM and simultaneously utilized behavioral theories to design or inform the implementation of an intervention. Furthermore, we found that the Theoretical Domain Framework, the Theory of Planned Behavior, the Behavioral Change Wheel, and the Capability, Opportunity, and Motivation Behavior Model were most frequently used to guide the implementation of KT interventions. Our study identified a vital gap that must be filled in relation to EIDM research on intervention implementation. Thus, our findings can support and enrich those planning to launch an EIDM initiative in terms of selecting the base theory/model/framework, anticipating the facilitators and barriers to implementation, and determining the most appropriate intervention component.

Bimetal Oxides Anchored on Carbon Nanotubes/Nanosheets as High-Efficiency and Durable Bifunctional Oxygen Catalyst for Advanced Zn-Air Battery: Experiments and DFT Calculations.

To meet increasing requirement for innovative energy storage and conversion technology, it is urgent to prepare effective, affordable, and long-term stable oxygen electrocatalysts to replace precious metal-based counterparts. Herein, a two-step pyrolysis strategy is developed for controlled synthesis of Fe2O3 and Mn3O4 anchored on carbon nanotubes/nanosheets (Fe2O3-Mn3O4-CNTs/NSs). The typical catalyst has a high half-wave potential (E1/2 = 0.87V) for oxygen reduction reaction (ORR), accompanied with a smaller overpotential (η10 = 290mV) for oxygen evolution reaction (OER), showing substantial improvement in the ORR and OER performances. As well, density functional theory calculations are performed to illustrate the catalytic mechanism, where the in situ generated Fe2O3 directly correlates to the reduced energy barrier, rather than Mn3O4. The Fe2O3-Mn3O4-CNTs/NSs-based Zn-air battery exhibits a high-power density (153mW cm-2) and satisfyingly long durability (1650 charge/discharge cycles/550h). This work provides a new reference for preparation of highly reversible oxygen conversion catalysts.