High Cost Research Articles

Versatile separators toward advanced lithium-sulfur batteries: status, recent progress, challenges and perspective.

Lithium-sulfur batteries (LSBs) have recently gained extensive attention due to their high energy density, low cost, and environmental friendliness. However, serious shuttle effect and uncontrolled growth of lithium dendrites restrict them from further commercial applications. As "the third electrode", functional separators are of equal significance as both anodes and cathodes in LSBs. The challenges mentioned above are effectively addressed with rational design and optimization in separators, thereby enhancing their reversible capacities and cycle stability. The review discusses the status/operation mechanism of functional separators, then primarily focuses on recent research progress in versatile separators with purposeful modifications for LSBs, and summarizes the methods and characteristics of separator modification, including heterojunction engineering, single atoms, quantum dots, and defect engineering. From the perspective of the anodes, distinct methods to inhibit the growth of lithium dendrites by modifying the separator are discussed. Modifying the separators with flame retardant materials or choosing a solid electrolyte is expected to improve the safety of LSBs. Besides, in-situ techniques and theoretical simulation calculations are proposed to advance LSBs. Finally, future challenges and prospects of separator modifications for next-generation LSBs are highlighted. We believe that the review will be enormously essential to the practical development of advanced LSBs.

Centimeter-scale nanomechanical resonators with low dissipation.

High-aspect-ratio mechanical resonators are pivotal in precision sensing, from macroscopic gravitational wave detectors to nanoscale acoustics. However, fabrication challenges and high computational costs have limited the length-to-thickness ratio of these devices, leaving a largely unexplored regime in nano-engineering. We present nanomechanical resonators that extend centimeters in length yet retain nanometer thickness. We explore this expanded design space using an optimization approach which judiciously employs fast millimeter-scale simulations to steer the more computationally intensive centimeter-scale design optimization. By employing delicate nanofabrication techniques, our approach ensures high-yield realization, experimentally confirming room-temperature quality factors close to theoretical predictions. The synergy between nanofabrication, design optimization guided by machine learning, and precision engineering opens a solid-state path to room-temperature quality factors approaching 10 billion at kilohertz mechanical frequencies - comparable to the performance of leading cryogenic resonators and levitated nanospheres, even under significantly less stringent temperature and vacuum conditions.

Low hunting costs in an expensive marine mammal predator.

Many large terrestrial mammalian predators use energy-intensive, high-risk, high-gain strategies to pursue large, high-quality prey. However, similar-sized marine mammal predators with even higher field metabolic rates (FMRs) consistently target prey three to six orders of magnitude smaller than themselves. Here, we address the question of how these active and expensive marine mammal predators can gain sufficient energy from consistently targeting small prey during breath-hold dives. Using harbor porpoises as model organisms, we show that hunting small aquatic prey is energetically cheap (<20% increase in FMR) for these marine predators, but it requires them to spend a large proportion (>60%) of time foraging. We conclude that this grazing foraging strategy on small prey is viable for marine mammal predators despite their high FMR because they can hunt near continuously at low marginal expense. Consequently, cessation of foraging due to human disturbance comes at a high cost, as porpoises must maintain their high thermoregulation costs with a reduced energy intake.

Pretreatment of wastepaper with an aqueous solution of amino acid-derived ionic liquid for biochar production as adsorbent.

The carbonization of lignocellulosic biomass with ionic liquids (ILs) are considered as an advantageous approach for the preparation of carbonaceous materials. The commonly used imidazolium and pyridinium based ILs have drawbacks such as toxicity, resistance to biodegradation, high cost and viscosity. These issues can be mitigated by diluting ILs with water, although excessive water content above 1wt% can reduce the solubility of biomass. This research aims to investigate the potential of pretreating wastepaper with a "fully green" ILs, amino acid-based IL with high water content, followed by pyrolysis without IL, in enhancing the properties of biochar. For this purpose, the paper was treated with an aqueous solution of IL cysteine nitrate ([Cys][NO3]), and the IL was not involved in the pyrolysis process to prevent the formation of secondary gaseous pollutants. The findings revealed that the hemicellulose and mineral filler in the paper were eliminated during pretreatment, leading to higher carbon content but lower oxygen content. As a result, the biochar exhibited micropores of 0.42 cm3g-1 and a specific surface area of 1011.21m2g-1. The biochar demonstrated high adsorption capacities for Cd2+, enrofloxacin, bisphenol A, ciprofloxacin, and tetracycline, with values of 45.20mgg-1, 49.82mgg-1, 49.90mgg-1, 49.88mgg-1, and 49.65mgg-1, respectively. The proposed mechanism for the adsorption of enrofloxacin by the biochar primarily involves physical adsorption such as pore filling and electrostatic interactions, along with chemical adsorption facilitated by graphitic nitrogen.

Progress and Challenges in Polystyrene Recycling and Upcycling.

Polystyrene is a staple plastic in the packaging and insulation market. Despite its good recyclability, the willingness of PS recycling remains low, largely due to the high recycling cost and limited profitability. This review examines the research progresses, gaps, and challenges in areas that affect the recycling costs, including but not limited to logistics, packaging design, and policymaking. We critically evaluate the recent developments in upcycling strategies, and we particularly focus on tandem and hydrogen-atom transfer (HAT) upcycling strategies. We conclude that future upcycling studies should focus on not only reaction chemistry and mechanisms but also economic viability of the processes. The goal of this review is to stimulate the development of innovative recycling strategies with low recycling costs and high economic output values. We hope to stimulate the economic and technological momentum of PS recycling towards a sustainable and circular economy.

A multi-criteria decision-making model for heavy construction equipment replacement in Saudi Arabia

Purpose This paper aims to propose a novel model that addresses the limitations of current practices, through considering quantitative and qualitative criteria in the decision-making process for equipment replacement. Design/methodology/approach Literature review and consultation with professionals in the heavy construction industry was conducted to identify the criteria influencing the replacement of construction machines. A questionnaire survey using analytic hierarchy process and multi-attribute utility theory was used to rank these criteria and establish their utility scores. Sensitivity analysis was performed to assess how adjustments in the weights of main criteria would impact equipment replacement decisions. Findings The identified criteria were classified into three categories: economic, technical and socioenvironmental, encompassing a total of 15 criteria. The findings indicated that salvage value/meeting payback period/maximizing profitability held the highest importance in the replacement process, followed by considerations like high repair and maintenance cost; working condition and economic conditions. Safety and social benefits scored the least among all criteria and categories. Research limitations/implications This study focuses on earth-moving equipment and involves experts from the Eastern Province of Saudi Arabia. The model introduces a novel methodology to aid decision-makers, particularly contractors and project managers, in determining when to replace heavy construction equipment, which results in resource efficiency and time saving. Originality/value The model integrates expertise and knowledge from experts to establish criteria for replacing construction equipment. This research aims to improve the functionality of the decision-making process regarding the acquisition or replacement of equipment throughout its lifespan.

Numerical investigation on the convergence of self-consistent Schrödinger-Poisson equations in semiconductor device transport simulation.

Semiconductor devices at the nanoscale with low-dimensional materials as channels exhibit quantum transport characteristics, thereby their electrical simulation relies on the self-consistent solution of the Schrödinger-Poisson equations. While the non-equilibrium Green's function (NEGF) method is widely used for solving this quantum many-body problem, its high computational cost and convergence challenges with the Poisson equation significantly limit its applicability. In this study, we investigate the stability of the NEGF method coupled with various forms of the Poisson equation, encompassing linear, analytical nonlinear, and numerical nonlinear forms Our focus lies on simulating carbon nanotube field-effect transistors (CNTFETs) under two distinct doping scenarios: electrostatic doping and ion implantation doping. The numerical experiments reveal that nonlinear formulas outperform linear counterpart. The numerical one demonstrates superior stability, particularly evident under high bias and ion implantation doping conditions. Additionally, we investigate different approaches for presolving potential, leveraging solutions from the Laplace equation and a piecewise guessing method tailored to each doping mode. These methods effectively reduce the number of iterations required for convergence.

Double base mismatches mediated catalytic hairpin assembly for enzyme-free single-base mutation detection: integrating signal recognition and amplification in one.

Single nucleotide polymorphism (SNP) biosensors are emerging rapidly for their promising applications in human disease prevention diagnosis, treatment, and prognosis. However, it remains a bottleneck in equipping simple and stable biosensors with the traits of high sensitivity, non-enzyme, and low cost. Double base mismatches mediated chain displacement reactions have attracted fascinating advantages of tailorable thermodynamics stability, non-enzyme, and excellent assembly compliance to involvement in SNP identification. As the base mismatch position and amount in DNA sequence can be artificially adjusted, it provides plenty of selectivity and specificity for exploring perfect biosensors. Herein, abiosensor with double base mismatches mediated catalytic hairpin assembly (CHA) is designed via one base mismatch in the toehold domain and the other base mismatch in the stem sequence of hairpin 1 (H1) by triggering CHA reaction to achieve selective amplification of the mutation target (MT) and fluorescence resonance energy transfer (FRET) effect that is composed of Cy3 and Cy5 terminally attached H1 and hairpin 2 (H2). Depending on the rationally designed base mismatch position and toehold length, the fabricated biosensors show superior SNP detection performance, exhibiting a good linearity with high sensitivity of 6.6 fM detection limitand a broad detection abundance of 1%. The proposed biosensor can be used to detect the KRAS mutation gene in real samples and obtain good recoveries between 106 and 116.99%. Remarkably, these extendible designs of base mismatches can be used for more types of SNP detection, providing flexible adjustment based on base mismatch position and toehold length variations, especially for their thermodynamic model for DNA-strand displacement reactions.

A reactive neural network framework for water-loaded acidic zeolites.

Under operating conditions, the dynamics of water and ions confined within protonic aluminosilicate zeolite micropores are responsible for many of their properties, including hydrothermal stability, acidity and catalytic activity. However, due to high computational cost, operando studies of acidic zeolites are currently rare and limited to specific cases and simplified models. In this work, we have developed a reactive neural network potential (NNP) attempting to cover the entire class of acidic zeolites, including the full range of experimentally relevant water concentrations and Si/Al ratios. This NNP has the potential to dramatically improve sampling, retaining the (meta)GGA DFT level accuracy, with the capacity for discovery of new chemistry, such as collective defect formation mechanisms at the zeolite surface. Furthermore, we exemplify how the NNP can be used as a basis for further extensions/improvements which include data-efficient adoption of higher-level (hybrid) references via Δ-learning and the acceleration of rare event sampling via automatic construction of collective variables. These developments represent a significant step towards accurate simulations of realistic catalysts under operando conditions.

Fast computational approach with prior dimension reduction for three-dimensional chemical component analysis using CT data of spectral imaging.

Spectral image (SI) measurement techniques, such as X-ray absorption fine structure (XAFS) imaging and scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDS) or electron energy loss spectroscopy (EELS), are useful for identifying chemical structures in composite materials. Machine-learning techniques have been developed for automatic analysis of SI data, and their usefulness has been proven. Recently, an extended measurement technique combining SI with a computed tomography (CT) technique (CT-SI), such as CT-XAFS and STEM-EDS/EELS tomography, was developed to identify the three-dimensional (3D) structures of chemical components. CT-SI analysis can be conducted by combining CT reconstruction algorithms and chemical component analysis based on machine learning techniques. However, this analysis incurs high computational costs owing to the size of the CT-SI datasets. To address this problem, this study proposed a fast computational approach for 3D chemical component analysis in an unsupervised learning setting. The primary idea for reducing the computational cost involved compressing the CT-SI data prior to CT computation and performing 3D reconstruction and chemical component analysis on the compressed data. The proposed approach significantly reduced the computational cost without losing information about the 3D structure and chemical components. We experimentally evaluated the proposed approach using synthetic and real CT-XAFS data, which demonstrated that our approach achieved a significantly faster computational speed than the conventional approach while maintaining analysis performance. As the proposed procedure can be implemented with any CT algorithm, it is expected to accelerate 3D analyses with sparse regularized CT algorithms in noisy and sparse CT-SI datasets.

Machine Learning-Enabled Image Classification for Automated Electron Microscopy.

Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of "big data" and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

Machine Learning Prediction of Hospitalization Costs for Coronary Artery Bypass Grafting Operations

With the steady rise in health care expenditures, the examination of factors that may influence the costs of care has garnered much attention. Although machine learning models have previously been applied in health economics, their application within cardiac surgery remains limited. We evaluated several machine learning algorithms to model hospitalization costs for coronary artery bypass grafting. All adult hospitalizations for isolated coronary artery bypass grafting were identified in the 2016 to 2020 Nationwide Readmissions Database. Machine learning models were trained to predict expenditures and compared with traditional linear regression. Given the significance of postoperative length of stay, we additionally developed models excluding postoperative length of stay to uncover other drivers of costs. To facilitate comparison, machine learning classification models were also trained to predict patients in the highest decile of costs. Significant factors associated with high cost were identified using SHapley Additive exPlanations beeswarm plots. Among 444,740 hospitalizations included for analysis, the median cost of hospitalization in coronary artery bypass grafting patients was $43,103. eXtreme Gradient Boosting most accurately predicted hospitalization costs, with R2= 0.519 over the validation set. The top predictive features in the eXtreme Gradient Boosting model included elective procedure status, prolonged mechanical ventilation, new-onset respiratory failure or myocardial infarction, and postoperative length of stay. After removing postoperative length of stay, eXtreme Gradient Boosting remained the most accurate model (R2= 0.38). Prolonged ventilation, respiratory failure, and elective status remained important predictive parameters. Machine learning models appear to accurately model total hospitalization costs for coronary artery bypass grafting. Future work is warranted to uncover other drivers of costs and improve the value of care in cardiac surgery.

Modulating the hAM/PCL Biocomposite for Expedited Wound Healing: A Chemical-Free Approach for Boosting Regenerative Potential.

There is an arising need for effective wound dressings that retain the bioactivity of a cellular treatment, but without the high costs and complexities associated with manufacturing, storing, and applying cell-based products. As skin wound recovery is a dynamic and complicated process, a significant obstacle to the healing of skin wounds is the lack of an appropriate wound dressing that can imitate the microenvironment of healthy skin and prevent bacterial infection. It requires the well-orchestrated integration of biological and molecular events. In this study, we have fabricated full-thickness skin graft biocomposite membranes to target full-thickness skin excision wounds. We reinforced human amniotic membrane (hAM) with electrospun polycaprolactone (PCL) to develop composite membranes, namely, PCL/hAM and PCL/hAM/PCL. Composite membranes were compared for physical, biological, and mechanical properties with the native counterpart. PCL/hAM and PCL/hAM/PCL displayed improved stability and delayed degradation, which further synergically improved the rapid wound healing property of hAM, driven primarily by wound closure analysis and histological assessment. Moreover, PCL/hAM displayed a comparable cellular interaction to hAM. On application as a wound dressing, histological analysis demonstrated that hAM and PCL/hAM promoted early epidermis and dermis formation. Studies on in vivo wound healing revealed that although hAM accelerates cell development, the overall wound healing process is similar in PCL/hAM. This finding is further supported by the immunohistochemical analysis of COL-1/COL-3, CD-31, and TGF-β. Overall, this conjugated PCL and hAM-based membrane has considerable potential to be applied in skin wound healing. The facile fabrication of the PCL/hAM composite membrane provided the self-regenerating wound dressing with the desired mechanical strength as an ideal regenerative property for skin tissue regeneration.

Catastrophic costs incurred by tuberculosis affected households from Thailand's first national tuberculosis patient cost survey.

Tuberculosis (TB) causes an economic impact on the patients and their households. Although Thailand has expanded the national health benefit package for TB treatment, there was no data on out-of-pocket payments and income losses due to TB from patients and their household perspectives. This national TB patient cost survey was conducted to examine the TB-related economic burden, and assess the proportion of TB patients and their households facing catastrophic total costs because of TB disease. A cross-sectional TB patient cost survey was employed following WHO methods. Structured interviews with a paper-based questionnaire were conducted from October 2019 to July 2021. Both direct and indirect costs incurred from the patient and their household perspective were valued in 2021 and estimated throughout pre- and post-TB diagnosis episodes. We assessed the proportion of TB-affected households facing costs > 20% of household expenditure due to TB. We analyzed 1400 patients including 1382TB (first-line treatment) and 18 drug-resistant TB patients (DR-TB). The mean total costs per TB episode for all study participants were 903 USD (95% confident interval; CI 771-1034 USD). Of these, total direct non-medical costs were the highest costs (mean, 402 USD, and 95%CI 334-470 USD) incurred per TB-affected household followed by total indirect costs (mean, 393 USD, and 95%CI 315-472 USD) and total direct medical costs (mean, 107 USD, and 95%CI 81-133 USD, respectively. The proportion of TB-affected households facing catastrophic costs was 29.5% (95%CI 25.1-34.0%) for TB (first-line), 61.1% (95%CI 29.6-88.1%) for DR-TB and 29.9% (95%CI 25.6-34.4%) overall. This first national survey highlighted the economic burden on TB-affected households. Travel, food/nutritional supplementation, and indirect costs contribute to a high proportion of catastrophic total costs. These suggest the need to enhance financial and social protection mechanisms to mitigate the financial burden of TB-affected households.

The Cost of Medications at a Student-Run Free Clinic in New Haven, Connecticut, 2021–2023

The Cost of Medications at a Student-Run Free Clinic in New Haven, Connecticut, 2021–2023

Encrypted data-sharing for preserving privacy in wastewater-based epidemiology.

The rapidly expanding use of wastewater for public health surveillance requires new strategies to protect privacy rights, while data are collected at increasingly discrete geospatial scales, i.e., city, neighborhood, campus, and building-level. Data collected at high geospatial resolution can inform on labile, short-lived biomarkers, thereby making wastewater-derived data both more actionable and more likely to cause privacy concerns and stigmatization of subpopulations. Additionally, data sharing restrictions among neighboring cities and communities can complicate efforts to balance public health protections with citizens' privacy. Here, we have created an encrypted framework that facilitates the sharing of sensitive population health data among entities that lack trust for one another (e.g., between adjacent municipalities with different governance of health monitoring and data sharing). We demonstrate the utility of this approach with two real-world cases. Our results show the feasibility of sharing encrypted data between two municipalities and a laboratory, while performing secure private computations for wastewater-based epidemiology (WBE) with high precision, fast speeds, and low data costs. This framework is amenable to other computations used by WBE researchers including population normalized mass loads, fecal indicator normalizations, and quality control measures. The Centers for Disease Control and Prevention's National Wastewater Surveillance System shows ~8 % of the records attributed to collection before the wastewater treatment plant, illustrating an opportunity to further expand currently limited community-level sampling and public health surveillance through security and responsible data-sharing as outlined here.

Progress on Copper-Based AnodeMaterials for Sodium-Ion Batteries.

Fossil fuels have clearly failed to meet people's growingenergy needs due to their limited reserves, potential pollution of the environment, and high costs.The development of cleaner, renewable energy sources as well as secondary batteries for energy storage is imminent, in a modern society where energy demand is soaring. Sodium-ion batteries (SIBs) have become the focus of large-scale energy storage systems as a promising alternative to lithium-ion batteries. The development of SIBs relies on the construction of high performance electrode materials. The design of low cost and high performance anode materials is a key link in this regard. Copper-based anodes are characterised by high theoretical capacity, abundant reserves, low cost and environmental friendliness. A variety of copper-based anode materials, which include cobalt oxides, sulfides, selenides and phosphides, have been synthesised and evaluated in the scientific literature for sodium storage. In detail, the preparation methods, response mechanisms, strengths and weaknesses, the relationship between morphology structure and electrochemical performance are discussed, as well as highlighting strategies to improve the electrochemical performance of copper-based anode materials. Finally, we offer our perspective on the challenges and potential for the development of copper-based anodes as a means of developing practical and high performing SIBs.

A comparison of computational algorithms for the Bayesian analysis of clinical trials.

Clinical trials are increasingly using Bayesian methods for their design and analysis. Inference in Bayesian trials typically uses simulation-based approaches such as Markov Chain Monte Carlo methods. Markov Chain Monte Carlo has high computational cost and can be complex to implement. The Integrated Nested Laplace Approximations algorithm provides approximate Bayesian inference without the need for computationally complex simulations, making it more efficient than Markov Chain Monte Carlo. The practical properties of Integrated Nested Laplace Approximations compared to Markov Chain Monte Carlo have not been considered for clinical trials. Using data from a published clinical trial, we aim to investigate whether Integrated Nested Laplace Approximations is a feasible and accurate alternative to Markov Chain Monte Carlo and provide practical guidance for trialists interested in Bayesian trial design. Data from an international Bayesian multi-platform adaptive trial that compared therapeutic-dose anticoagulation with heparin to usual care in non-critically ill patients hospitalized for COVID-19 were used to fit Bayesian hierarchical generalized mixed models. Integrated Nested Laplace Approximations was compared to two Markov Chain Monte Carlo algorithms, implemented in the software JAGS and stan, using packages available in the statistical software R. Seven outcomes were analysed: organ-support free days (an ordinal outcome), five binary outcomes related to survival and length of hospital stay, and a time-to-event outcome. The posterior distributions for the treatment and sex effects and the variances for the hierarchical effects of age, site and time period were obtained. We summarized these posteriors by calculating the mean, standard deviations and the 95% equitailed credible intervals and presenting the results graphically. The computation time for each algorithm was recorded. The average overlap of the 95% credible interval for the treatment and sex effects estimated using Integrated Nested Laplace Approximations was 96% and 97.6% compared with stan, respectively. The graphical posterior densities for these effects overlapped for all three algorithms. The posterior mean for the variance of the hierarchical effects of age, site and time estimated using Integrated Nested Laplace Approximations are within the 95% credible interval estimated using Markov Chain Monte Carlo but the average overlap of the credible interval is lower, 77%, 85.6% and 91.3%, respectively, for Integrated Nested Laplace Approximations compared to stan. Integrated Nested Laplace Approximations and stan were easily implemented in clear, well-established packages in R, while JAGS required the direct specification of the model. Integrated Nested Laplace Approximations was between 85 and 269 times faster than stan and 26 and 1852 times faster than JAGS. Integrated Nested Laplace Approximations could reduce the computational complexity of Bayesian analysis in clinical trials as it is easy to implement in R, substantially faster than Markov Chain Monte Carlo methods implemented in JAGS and stan, and provides near identical approximations to the posterior distributions for the treatment effect. Integrated Nested Laplace Approximations was less accurate when estimating the posterior distribution for the variance of hierarchical effects, particularly for the proportional odds model, and future work should determine if the Integrated Nested Laplace Approximations algorithm can be adjusted to improve this estimation.

Exploring SureChEMBL from a drug discovery perspective.

In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well-established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this study, we delve into one such publicly available patent database, SureChEMBL, which catalogues patent documents related to life sciences. Our exploration begins by identifying patent compounds across public chemical data resources, followed by pinpointing sections in patent documents where the chemical annotations were found. Next, we exhibit the potential of compounds to serve as drug candidates by evaluating their conformity to drug-likeness criteria. Lastly, we examine the drug development stage reported for these compounds to understand their clinical success. In summary, our investigation aims at providing a comprehensive overview of the patent compounds catalogued in SureChEMBL, assessing their relevance to pharmaceutical drug discovery.

Energy-Efficiency Co-Production of Benzoquinone and H2 by Resourcing Waste Phenol in a Hybrid Alkali/Acid Flow Cell.

In both the manufacturing and chemical industries, benzoquinone is a crucial chemical product. A perfect and economical method for making benzoquinone is the electrochemical oxidation of phenol, thanks to the traditional thermal catalytic oxidation of phenol process requires high cost, serious pollution and harsh reaction conditions.Here, a unique heterostructure electrocatalyst on nickel foam (NF) consisting of nickel sulfide and nickel oxide (Ni9S8-Ni15O16/NF) was produced, and this catalyst exhibited a low overpotential (1.35 V vs. RHE) and prominent selectivity (99%) for electrochemical phenol oxidation reaction(EOP). Ni9S8-Ni15O16/NF is beneficial for lowering the reaction energy barrier and boosting reactivity in the EOP process according to density functional theory (DFT) calculations. Additionally, an alkali/acid hybrid flow cell was successfully established by connecting Ni9S8-Ni15O16/NF and commercial RuIr/Ti in series to catalyze phenol oxidationin an alkaline mediumand hydrogen evolution in an acid medium, respectively. A cell voltage of only 0.60 V was applied to produce a current density of 10 mA cm-2. Meanwhile, the system continued to operate at 0.90 V for 12 days, showing remarkable long-term stability. The unique configuration of the acid-base hybrid flow cell electrolyzer provides valuable guidance for the efficient and environmentally friendly electrooxidation of phenol to benzoquinone.