Lattice Strain Engineering Boosts CO2 Electroreduction to C2+ Products.

Regulating the binding effect between the surface of an electrode material and reaction intermediates is essential in highly efficient CO2 electro-reduction to produce high-value multicarbon (C2+) compounds. Theoretical study reveals that lattice tensile strain in single-component Cu catalysts can reduce the dipole-dipole repulsion between *CO intermediates and promotes *OH adsorption, and the high *CO and *OH coverage decreases the energy barrier for C-C coupling. In this work, Cu catalysts with varying lattice tensile strain were fabricated by electro-reducing CuO precursors with different crystallinity, without adding any extra components. The as-prepared single-component Cu catalysts were used for CO2 electro-reduction, and it is discovered that the lattice tensile strain in Cu could enhance the Faradaic efficiency (FE) of C2+ products effectively. Especially, the as-prepared CuTPA catalyst with high lattice tensile strain achieves a FEC2+ of 90.9% at -1.25 V vs. RHE with a partial current density of 486.1 mA cm-2.

Catalytic effect of nickel oxide nanoparticles from Lupinus Albus extract on green synthesis and photocatalytic reduction of methylene blue: kinetics and mechanism.

Green synthesis of nanomaterials is advancing due to their ease of synthesis, cheapness, nontoxicity, and renewability. An environmentally friendly biogenic method has been developed for the green synthesis of nickel oxide nanoparticles (NiO NPs) using phytochemical-rich bioextract. They are rich in bioextract phenolics, flavonoids, and berberine. These phytochemicals successfully reduce and stabilize NiNO3 into NiO NPs. In this study, NiO NPs were synthesized by the green synthesis method from Lupinus Albus. Characterization of NiO NPs was carried out by TEM, XRD, SEM, UV, XRF, BET, and EDX analyses. According to XRD analysis, TEM results also support this, where the NiO NPs particle size diameter is 5 nm. It was determined by the Tauc equation that the band energy gap of NiO NPs is 1.69 eV. It was determined that the BET surface area of NiO NPs was 49.6 m2/g. NiO nanoparticles synthesized from Lupinus Albus extract by the green synthesis method were used as catalysts in the photocatalytic reduction of methylene blue with NaBH4. In the photocatalytic reduction of methylene blue with NaBH4, it was determined that there was no color change in 48 h without a catalyst, and in the presence of NiO nanoparticle catalyst, methylene blue was reduced by 97% in 8 min. The kinetics of the photocatalytic reduction of methylene blue with NaBH4 is a pseudo-first-order kinetic model and the kinetic rate constant is determined as 0.66 min-1, indicating that the catalytic effect of NiO NPs is very high at this value. NiO NPs were used five times in the photocatalytic reduction of methylene blue with NaBH4 and it was determined that the reduction of methylene blue was over 90% in each use.

NiO nanoparticles were synthesized from Lupinus Albus extract by green synthesis, which is an easily applied, cost-effective, and environmentally friendly method. The synthesized NiO nanoparticles were characterized using various characterization techniques. NiO nanoparticles have a high catalytic effect in the photocatalytic reduction of methylene blue with NaBH₄. Photocatalytic reduction of methylene blue with uncatalyzed NaBH₄ could not be achieved, and 97% reduction of methylene blue was completed in 8 min in the presence of NiO nanoparticle catalyst.

Compressively Strained Fe3O4 in Core-Shell Oxygen Reduction Electrocatalyst Boosts Zinc-Air Battery Performance.

Fe3O4 is barely taken into account as an electrocatalyst for oxygen reduction reaction (ORR), an important reaction for metal-air batteries and fuel cells, due to its sluggish catalytic kinetics and poor electron conductivity. Herein, how strain engineering can be employed to regulate the local electronic structure of Fe3O4 for high ORR activity is reported. Compressively strained Fe3O4 shells with 2.0% shortened Fe─O bond are gained on the Fe/Fe4N cores as a result of lattice mismatch at the interface. A downshift of the d-band center occurs for compressed Fe3O4, leading to weakened chemisorption energy of oxygenated intermediates, and lower reaction overpotential. The compressed Fe3O4 exhibits greatly enhanced electrocatalytic ORR activity with a kinetic current density of 27 times higher than that of pristine one at 0.80 V (vs reversible hydrogen electrode), as well as potential application in zinc-air batteries. The findings provide a new strategy for tuning electronic structures and improving the catalytic activity of other metal catalysts.

A reduced vector model predictive controller for a three-level neutral point clamped inverter with common-mode voltage suppression.

This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this. First, a statistical approach is presented for the proactive detection of potential voltage vectors, with an emphasis on selecting and including the vectors that are most frequently used. This method lowers the computational load by limiting the search space needed to find the best voltage vectors. Then, using statistical analysis, a plan is presented to split the sectors into two separate parts, so greatly limiting the number of voltage vectors. The goal of this improved predictive control methodology is to reduce computing demands and mitigate common mode voltage. The suggested strategy's resilience is confirmed in a range of operational scenarios using simulations and empirical evaluation. The findings indicate a pronounced enhancement in computational efficiency and a notable diminution in common mode voltage, thereby underscoring the efficacy of the proposed methodology. This increases their ability to incorporate renewable energy sources into the electrical grid.

Facile synthesis of copper nitroprusside chitosan nanocomposite and its catalytic reduction of environmentally hazardous azodyes.

One of the biggest issues affecting the entire world currently is water contamination caused by textile industries' incapacity to properly dispose their wastewater. The presence of toxic textile dyes in the aquatic environment has attracted significant research interest due to their high environmental stability and their negative effects on human health and ecosystems. Therefore, it is crucial to convert the hazardous dyes such as methyl orange (MO) azo dye into environmentally safe products. In this context, we describe the use of Copper Nitroprusside Chitosan (Cu/SNP/Cts) nanocomposite as a nanocatalyst for the chemical reduction of azodyes by sodium borohydride (NaBH4). The Cu/SNP/Cts was readily obtained by chemical coprecipitation in a stoichiometric manner. The X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared (FT-IR) spectroscopy were applied to investigate chemical, phase, composition, and molecular interactions. Additionally, Scanning electron microscope (SEM) was used to examine the nanomaterial's microstructure. UV-vis spectroscopy was utilized for studying the Cu Nitroprusside Chitosan's catalytic activity for the reduction of azodye. The Cu/SNP/Cts nanocomposite demonstrated outstanding performance with total reduction time 160 s and pseudo-first order constant of 0.0188 s-1. Additionally, the stability and reusability study demonstrated exceptional reusability up to 5 cycles with minimal activity loss. The developed Cu/SNP/Cts nanocomposite act as efficient nanocatalysts for the reduction of harmful Methyl orange azodye.

Financial barriers, facilitators, and strategies among syringe services programs in the U.S., and their impact on implementation and health outcomes.

Syringe Services Programs (SSPs) provide evidence-based services like drug use equipment to prevent infectious disease, overdose prevention education, and naloxone distribution to people who use drugs (PWUD). However, inadequate funding threatens provision of these interventions. This study aimed to document how the current funding landscape impacted determinants of SSP implementation, particularly describing financial and staffing barriers, facilitators, and proposed strategies, using qualitative methods informed by three implementation research frameworks. We interviewed 20 leaders of SSPs in the United States using a semi-structured interview guide. Participants described how structural stigma against PWUD led to insufficient and restrictive funding, and burdensome reporting for SSPs. This resulted in harming program implementation outcomes like reach, fidelity, and sustainability. Inadequate funding also led to insufficient staffing and subsequent staff stress, burnout, and turnover. Taken together, these barriers threatened the implementation of evidence-based interventions that SSPs provided, ultimately harming their ability to effectively address health outcomes like infectious disease transmission and opioid overdose mortality within their communities. Interviewees described how upstream policy strategies like political advocacy might address structural stigma at the federal level. Participants also highlighted state-level efforts like harm reduction-centered funding, technical assistance and capacity-building, and clearinghouse programs that may facilitate better implementation and health outcomes. A more robust understanding of the relationship between financial barriers, facilitators, and strategies on implementation and health outcomes represents a novel and vital area of research within harm reduction literature.

Improvement in ORR Durability of Fe Single-Atom Carbon Catalysts Hybridized with CeO2 Nanozyme.

FeNC catalysts are considered one of the most promising alternatives to platinum group metals for the oxygen reduction reaction (ORR). Despite the extensive research on improving ORR activity, the undesirable durability of FeNC is still a critical issue for its practical application. Herein, inspired by the antioxidant mechanism of natural enzymes, CeO2 nanozymes featuring catalase-like and superoxide dismutase-like activities were coupled with FeNC to mitigate the attack of reactive oxygen species (ROS) for improving durability. Benefiting from the multienzyme-like activities of CeO2, ROS generated from FeNC is instantaneously eliminated to alleviate the corrosion of carbon and demetallization of metal sites. Consequently, FeNC/CeO2 exhibits better ORR durability with a decay of only 5 mV compared to FeNC (18 mV) in neutral electrolyte after 10k cycles. The FeNC/CeO2-based zinc-air battery also shows minimal voltage decay over 140 h in galvanostatic discharge-charge cycling tests, outperforming FeNC and commercial Pt/C.

Solar-Driven Sustainability: III-V Semiconductor for Green Energy Production Technologies.

Long-term societal prosperity depends on addressing the world's energy and environmental problems, and photocatalysis has emerged as a viable remedy. Improving the efficiency of photocatalytic processes is fundamentally achieved by optimizing the effective utilization of solar energy and enhancing the efficient separation of photogenerated charges. It has been demonstrated that the fabrication of III-V semiconductor-based photocatalysts is effective in increasing solar light absorption, long-term stability, large-scale production and promoting charge transfer. This focused review explores on the current developments in III-V semiconductor materials for solar-powered photocatalytic systems. The review explores on various subjects, including the advancement of III-V semiconductors, photocatalytic mechanisms, and their uses in H2 conversion, CO2 reduction, environmental remediation, and photocatalytic oxidation and reduction reactions. In order to design heterostructures, the review delves into basic concepts including solar light absorption and effective charge separation. It also highlights significant advancements in green energy systems for water splitting, emphasizing the significance of establishing eco-friendly systems for CO2 reduction and hydrogen production. The main purpose is to produce hydrogen through sustainable and ecologically friendly energy conversion. The review intends to foster the development of greener and more sustainable energy source by encouraging researchers and developers to focus on practical applications and advancements in solar-powered photocatalysis.

ReMAR: a preoperative CT angiography guided metal artifact reduction framework designed for follow-up CTA of endovascular coiling.

Objective. Follow-up computed tomography angiography (CTA) is necessary for ensuring occlusion effect of endovascular coiling. However, the implanted metal coil will introduce artifacts that have a negative spillover into radiologic assessment.Method. A framework named ReMAR is proposed in this paper for metal artifacts reduction (MARs) from follow-up CTA of patients with coiled aneurysms. It employs preoperative CTA to provide the prior knowledge of the aneurysm and the expected position of the coil as a guidance thus balances the metal artifacts removal performance and clinical feasibility. The ReMAR is composed of three modules: segmentation, registration and MAR module. The segmentation and registration modules obtain the metal coil knowledge via implementing aneurysms delineation on preoperative CTA and alignment of follow-up CTA. The MAR module consisting of hybrid convolutional neural network- and transformer- architectures is utilized to restore sinogram and remove the artifact from reconstructed image. Both image quality and vessel rendering effect after metal artifacts removal are assessed in order to responding clinical concerns.Main results. A total of 137 patients undergone endovascular coiling have been enrolled in the study: 13 of them have complete diagnosis/follow-up records for end-to-end validation, while the rest lacked of follow-up records are used for model training. Quantitative metrics show ReMAR significantly reduced the metal-artifact burden in follow-up CTA. Qualitative ranks show ReMAR could preserve the morphology of blood vessels during artifact removal as desired by doctors.Significance. The ReMAR could significantly remove the artifacts caused by implanted metal coil in the follow-up CTA. It can be used to enhance the overall image quality and convince CTA an alternative to invasive follow-up in treated intracranial aneurysm.

Customizing pyridinic nitrogen coordination in Ni-N-C for electrocatalytic CO2reduction towards CO.

Nickel anchored N-doped carbon electrocatalysts (Ni-N-C) are rapidly developed for the electrochemical reduction reaction of carbon dioxide (CO2RR). However, the high-performanced Ni-N-C analogues design for CO2RR remains bewilderment, for the reason lacking of definite guidance for its structure-activity relationship. Herein, the correlation between the proportion of nitrogen species derived from various nitrogen sources and the CO2RR activity of Ni-N-C is investigated. The x-ray photoelectron spectroscopy (XPS) spectrum combined with the CO2RR performance results show that pyridinic-N content has a positive correlation with CO2RR activity. Moreover, density functional theory (DFT) demonstrates that pyridinic-N coordinated Ni-N4sites offers optimized free energy and favorable selectivity towards CO2RR compared with pyrrolic-N. Accordingly, Ni-Na-C with highest pyridinic-N content (ammonia as nitrogen source) performs superior CO2RR activity, with the maximum carbon monoxide faradaic efficiency (FECO) of 99.8% at -0.88 V vs. RHE and the FECOsurpassing 95% within potential ranging of -0.88 to -1.38 V vs. RHE. The building of this parameter for CO2RR activity of Ni-N-C give instructive forecast for low-cost and highly active CO2RR electrocatalysts.

[A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images].

OBJECTIVE: We propose a motion artifact correction algorithm (DMBL) for reducing motion artifacts in reconstructed dental cone-beam computed tomography (CBCT) images based on deep blur learning. METHODS: A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion, and the obtained motion degradation features were imported in the artifact correction module for artifact removal. The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns. Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets. RESULTS: The experimental results with the simulated dataset showed that compared with the existing methods, the PSNR of the proposed method increased by 2.88%, the SSIM increased by 0.89%, and the RMSE decreased by 10.58%. The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417 (in a 5-point scale), significantly higher than those of the comparison methods. CONCLUSION: The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.

A 6-year single-center prospective follow-up study of the efficacy of radiofrequency ablation for thyroid nodules.

Background: Radiofrequency ablation (RFA) is an alternative modality for thyroid nodules (TNs) and many studies have also confirmed its favorable efficacy and safety. The scope of RFA increases in clinical practice and the aim of our study was to evaluate the efficacy of RFA. Methods: We conducted a prospective study to evaluate the efficacy of RFA for thyroid nodules between January 2017 and December 2022 at our institution. We assessed the change in nodal volume, volume reduction ratio (VRR), technique effective (TE) rate, complete ablation (CA) rate, and nodal regrowth rate and time after RFA. Results: We performed RFA for 1703 patients with TNs between January 2017 and December 2022, of which a total of 970 eligible patients were enrolled in the study. The preoperative volume of TNs was 6.23 ± 8.11ml, with 821 benign and 149 malignant nodules. The post-RFA TE and adjusted TE rate were 80% and 88.8%, respectively. CA was achieved in 145 (14.9%) patients with a mean time of 18.32± 12.98 months; nodal regrowth occurred in 15 (1.5%) patients with a mean time of 29.80 ± 12.47 months. TNs volume and VRR changed significantly at years 1 and 2 after RFA and stabilized after 5 years. A serious postoperative adverse event occurred in one patient with cervical sympathetic chain injury resulting in Horner's syndrome. A transient or permanent damage of the recurrent laryngeal nerve could not be evaluated due to the lack of postoperative laryngoscopy, and this is a significant limitation of the study. Conclusion: The expanded RFA indications were also effective for TNs, with no significant change in long-term efficacy.

A systematic literature review on meta-heuristic based feature selection techniques for text classification.

Feature selection (FS) is a critical step in many data science-based applications, especially in text classification, as it includes selecting relevant and important features from an original feature set. This process can improve learning accuracy, streamline learning duration, and simplify outcomes. In text classification, there are often many excessive and unrelated features that impact performance of the applied classifiers, and various techniques have been suggested to tackle this problem, categorized as traditional techniques and meta-heuristic (MH) techniques. In order to discover the optimal subset of features, FS processes require a search strategy, and MH techniques use various strategies to strike a balance between exploration and exploitation. The goal of this research article is to systematically analyze the MH techniques used for FS between 2015 and 2022, focusing on 108 primary studies from three different databases such as Scopus, Science Direct, and Google Scholar to identify the techniques used, as well as their strengths and weaknesses. The findings indicate that MH techniques are efficient and outperform traditional techniques, with the potential for further exploration of MH techniques such as Ringed Seal Search (RSS) to improve FS in several applications.

Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons.

The convergence of smart technologies and predictive modelling in prisons presents an exciting opportunity to revolutionize the monitoring of inmate behaviour, allowing for the early detection of signs of distress and the effective mitigation of suicide risks. While machine learning algorithms have been extensively employed in predicting suicidal behaviour, a critical aspect that has often been overlooked is the interoperability of these models. Most of the work done on model interpretations for suicide predictions often limits itself to feature reduction and highlighting important contributing features only. To address this research gap, we used Anchor explanations for creating human-readable statements based on simple rules, which, to our knowledge, have never been used before for suicide prediction models. We also overcome the limitation of anchor explanations, which create weak rules on high-dimensionality datasets, by first reducing data features with the help of SHapley Additive exPlanations (SHAP). We further reduce data features through anchor interpretations for the final ensemble model of XGBoost and random forest. Our results indicate significant improvement when compared with state-of-the-art models, having an accuracy and precision of 98.6% and 98.9%, respectively. The F1-score for the best suicide ideation model appeared to be 96.7%.

Differences in cortical microstructure according to body mass index in neurologically healthy populations using structural magnetic resonance imaging.

Associations between brain structure and body mass index (BMI) are increasingly gaining attention. Although BMI-related regional alterations in brain morphology have been previously reported, the effect of BMI on the microstructural profiles, which provide information on the proxy of neuronal density within the cortex, is unexplored. In this study, we investigated the links between cortical layer-specific microstructural profiles and BMI in 302 neurologically healthy young adults. Using the microstructure-sensitive proxy based on the T1-and T2-weighted ratio, we estimated microstructural profile covariance (MPC) by calculating linear correlations of cortical depth-wise intensity profiles between different brain regions. Then, low-dimensional gradients of the MPC matrix were estimated using dimensionality reduction techniques, and the gradients were associated with BMI. Significant effects in the heteromodal association areas were observed. The BMI-gradient association map was related to the geodesic distance along the cortical surface, curvature, and sulcal depth, suggesting that the microstructural alterations occurred along the cortical topology. The BMI-gradient association map was further linked to cognitive states related to negative emotions. Our findings may provide insights into understanding the atypical cortical microstructure associated with BMI.

Interface Engineering of the Cu1.5Mn1.5O4/CeO2 Heterostructure for Highly Efficient Electrocatalytic Nitrate Reduction to Ammonia.

The electrochemical nitrate reduction reaction (NO3RR) is considered a sustainable technology to convert the nitrate pollutants to ammonia. However, developing highly efficient electrocatalysts is necessary and challenging given the slow kinetics of the NO3RR with an eight-electron transfer process. Here, a Cu1.5Mn1.5O4 (CMO)/CeO2 heterostructure with rich interfaces is designed and fabricated through an electrospinning and postprocessing technique. Benefiting from the strong coupling between CMO and CeO2, the optimized CMO/CeO2-2 catalyst presents excellent NO3RR performance, with NH3 Faraday efficiency (FE) up to 93.07 ± 1.45% at -0.481 V vs reversible hydrogen electrode (RHE) and NH3 yield rate up to 48.06 ± 1.32 mg cm-2 h-1 at -0.681 V vs RHE. Theoretical calculations demonstrate that the integration of CeO2 with CMO modulates the adsorption/desorption process of the reactants and intermediates, showing a reduced energy barrier in the rate determination step of NO* to N* and achieving an outstanding NO3RR performance.

Theoretical Insights into the Selectivity of Nitrite Reduction to NH2OH on Single-Atom Catalysts.

Electroreduction of nitrate/nitrite to high-value-added products, including NH2OH, is an important way to achieve sustainable production of green energy. However, this electrosynthesis of NH2OH still suffers from poor selectivity due to the various competing reactions. Here, we screen out Ni-N4 and Cu-N4 catalysts for highly efficient nitrite electroreduction to NH2OH by adopting density functional theory (DFT) calculations. DFT calculations reveal that the high selectivity of Ni-N4 and Cu-N4 is ascribed to their weak adsorption of *NH2OH and *NH intermediates, thereby preventing the further reduction of NH2OH. Moreover, using *NO as a model intermediate, we studied the relationship between the 3d orbital occupancy and adsorption strength of the intermediate. It is found that Ni-N4 and Cu-N4 with fully occupied dxz, dyz, and dz2 orbitals have poor adsorption of *NO intermediate. This work provides a new route for NH2OH synthesis and offers perspectives on the crucial factors in determining the catalytic selectivity.

MoS2/Mayenite Electride Hybrid as a Cathode Host for Suppressing Polysulfide Shuttling and Promoting Kinetics in Lithium-Sulfur Batteries.

The commercial viability of emerging lithium-sulfur batteries (LSBs) remains greatly hindered by short lifespans caused by electrically insulating sulfur, lithium polysulfides (Li2Sn; 1 ≤ n ≤ 8) shuttling, and sluggish sulfur reduction reactions (SRRs). This work proposes the utilization of a hybrid composed of sulfiphilic MoS2 and mayenite electride (C12A7:e-) as a cathode host to address these challenges. Specifically, abundant cement-based C12A7:e- is the most stable inorganic electride, possessing the ultimate electrical conductivity and low work function. Through density functional theory simulations, the key aspects of the MoS2/C12A7:e- hybrid including electronic properties, interfacial binding with Li2Sn, Li+ diffusion, and SRR have been unraveled. Our findings reveal the rational rules for MoS2 as an efficient cathode host by enhancing its mutual electrical conductivity and surface polarity via MoS2/C12A7:e-. The improved electrical conductivity of MoS2 is attributed to the electron donation from C12A7:e- to MoS2, yielding a semiconductor-to-metal transition. The resultant band positions of MoS2/C12A7:e- are well matched with those of conventional current-collecting materials (i.e., Cu and Ni), electrochemically enhancing the electronic transport. The accepted charge also intensifies MoS2 surface polarity for attracting polar Li2Sn by forming stronger bonds with Li2Sn via ionic Li-S bonds than electrolytes with Li2Sn, thereby preventing polysulfide shuttling. Importantly, MoS2/C12A7:e- not only promotes rapid reaction kinetics by reducing ionic diffusion barriers but also lowers the Gibbs free energies of the SRR for effective S8-to-Li2S conversion. Beyond the reported applications of C12A7:e-, this work highlights its functionality as an electrode material to boost the efficiency of LSBs.

Anionic Surfactant-Tailored Interfacial Microenvironment for Boosting Electrochemical CO2 Reduction.

Both the catalyst and electrolyte deeply impact the performance of the carbon dioxide reduction reaction (CO2RR). It remains a challenge to design the electrolyte compositions for promoting the CO2RR. Here, typical anionic surfactants, dodecylphosphonic acid (DDPA) and its analogues, are employed as electrolyte additives to tune the catalysis interface where the CO2RR occurs. Surprisingly, the anionic surfactant-tailored interfacial microenvironment enables a set of typical commercial catalysts for the CO2RR to deliver a significantly enhanced selectivity of carbon products in both neutral and acidic electrolytes. Mechanistic studies disclose that the DDPA addition restructures the interfacial hydrogen-bond environment via increasing the weak H-bonded water, thus promoting the CO2 protonation to CO. Specifically, in an H-type cell, the Faradaic efficiency of CO increases from 70 to 98% at -1.0 V versus the reversible hydrogen electrode. Furthermore, in a flow cell, the DDPA-containing electrolyte maintains over 90% FECO from 50-400 mA cm-2. Additionally, this electrolyte modulation strategy can be extended to acidic CO2RR with a pH of 1.5-3.5.

Superior nitrate and chromium reduction synergistically driven by multiple electron donors: Performance and the related biochemical mechanism.

Nitrate and Cr(VI) are the typical and prevalent co-contaminants in the groundwater, how to synchronously and effectively diminish them has received growing attention. The most problem that currently limits the nitrate and Cr(VI) reduction technology for groundwater remediation is with emphasis on exploring the optimal electron donors. This study investigated the feasibility of utilizing the synergistical effect of inorganic electron donors (pyrite, sulfur) and inherently limited organics to promote synchronous nitrate and Cr(VI) removal, which meets the requirement of naturally low-carbon and eco-friendly technologies. The NO3--N and Cr(VI) removal efficiencies in the pyrite and sulfur involved mixotrophic biofilter (PS-BF: approximately 90.8 ± 0.6% and 99.1 ± 2.1%) were substantially higher than that in a volcanic rock supported biofilter (V-BF: about 49.6% ± 2.8% and 50.0% ± 9.3%), which was consistent with the spatial variations of their concentrations. Abiotic and biotic batch tests directly confirmed the decisive role of pyrite and sulfur for NO3--N and Cr(VI) removal via chemical and microbial pathways. A server decline in sulfate production correlated with decreasing COD consumption revealed that there was sulfur disproportionation induced by limited organics. Metagenomic analysis suggested that chemoautotrophic microbes like Sulfuritalea and Thiobacillus were key players responsible for sulfur oxidation, nitrate and Cr(VI) reduction. The metabolic pathway analysis suggested that genes encoding functional enzymes related to complete denitrification, S oxidation, and dissimilatory sulfate reduction were upregulated, however, genes encoding Cr(VI) reduction enzymes (e.g. chrA, chrR, nemA, and azoR) were downregulated in PS-BF, which further explained the synergistical effect of multiple electron donors. These findings provide insights into their potential cooperative interaction of multiple electron donors on greatly promoting nitrate and Cr(VI) removal and have implications for the remediation technology of nitrate and Cr(VI) co-contaminated groundwater.