Efficient Reduction Research Articles

Influence of nano-encapsulated Yucca schidigera extract on ruminal anaerobic gases of methane, carbon monoxide, and hydrogen sulfide production of different carbohydrate-based diets

Saponins, the primary component of Yucca schidigera extract (YSE), are known to influence microbial activity in the rumen, which can affect various fermentation parameters. Moreover, encapsulating YSE with Chitosan (CS) at varying carbohydrate levels (CHO) adds another layer of complexity that can provide valuable insights into the use of additives in mitigating greenhouse gases. This study investigated the impact of both crude and encapsulated forms of YSE on the production of ruminal anaerobic gases in cattle fed different levels of CHO-based diets. Ruminal contents were obtained from four slaughtered, crossbreed bulls (Charollais × Limousin) with a live weight of 400 ± 25 kg. The experimental design followed a completely randomized factorial arrangement, with factors including CHO level (25%, 45%, and 55% DM), YSE forms (without extract, CS, crude, and nano-capsules), and dose of extract (0, 0.25, 0.5 and 1.0 mL/g of DM). Results showed that the type of extract significantly affected asymptotic total gas production, methane (CH4), Hydrogen sulfide (H2S), Carbon monoxide (CO), and dry matter digestibility. Nano-chitosan increased gas production and exhibited greater efficiency in reducing CH4 production by up to 61.36%. While crude YSE reduced CH4 production by 38%, nano-capsules increased production by 17.7% to 42.8%. Furthermore, a significant interaction effect among CHO levels, type of extract, and extract dose was observed, particularly impacting H2S and CH4 production after 48 hours of incubation, alongside an increase of about 25.3% in metabolizable energy compared to the control. The use of CS and YSE significantly (p <0.0001) improved the CH4 conversion efficiency by 71.3% and 23.4% respectively, and at some point, the encapsulation of YSE resulted in a significant reduction in efficiency by up to about 49.5%. The study concluded that both CS and YSE have the potential to enhance digestibility in cattle and reduce CH4 production and its conversion efficiency. However, encapsulating YSE with CS may reduce the efficiency of either compound in optimizing ruminal fermentation, unless other influencing factors are carefully considered. Therefore, the optimal efficacy of nano-encapsulated YSE depends on finding a balance between extract type, dosage, and dietary CHO level.

Effects of polyphenol-rich extracts and compounds on methane and ammonia emissions from pig slurry during 28-day incubation

Animal manure in livestock production facilities is a major source of methane (CH4) and ammonia (NH3) emissions from agriculture. In this study, we investigated different polyphenol-rich extracts and chemicals including commercial chestnut tannin (CT), shea meal extract (SME) and lignosulfonic acid (LSA) in combination with 1 mM of urease inhibitors acetohydroxamic acid (AHA) and/or sodium fluoride (NaF) to mitigate CH4 and NH3 in an in-vitro setup with 0.5 L slurry reactors mimicking slurry pits in livestock facilities. Daily addition of pig feces and urine along with additives were conducted for 28 days at room temperature, while CH4 and NH3 emissions were monitored by cavity ring-down spectroscopy (CRDS) continuously. SME and SME+NaF added to slurry reduced CH4 emission by 72% and 88%, and NH3 emission by 45% and 52%, respectively. CT+NaF at the CT concentration of 2.6, 4.25 and 8.5 mg mL-1, resulted in 48-55%, 70-72% and 81% reduction in CH4 emission, and 17-29%, 40-49% and 49% reduction in NH3 emission, respectively. Emission reduction based on the dosage of CT revealed that the relationship between dosage and emission reduction is not linear, therefore the reduction efficiency was calculated by emission reduction divided by concentration. CH4 reduction efficiency at the CT concentration of 2.6, 4.25 and 8.5 mg mL-1 were 18-21, 16-17 and 10 % (mg mL-1)-1. Therefore, the optimum dosage of CT was 4.25 mg mL-1. In conclusion, addition of CT and SME along with NaF resulted in significant reductions of both CH4 and NH3 emissions, while supplementation of LSA and AHA to CT+NaF did not enhance the mitigation effect.

Experimental and numerical investigation of a three-blade horizontal axis hydrokinetic water turbine (HAHWT) in high blockage conditions

Horizontal-axis hydrokinetic water turbines (HAHWT) represent a cost-effective and low-maintenance opportunity for hydropower generation in low-velocity channels. To enhance understanding of the potentiality of an original HAHWT prototype working at high blockage operations (e.g. tidal channels, man-made water infrastructure, offshore energy platforms), an experimental and numerical investigation was carried out to assess its performance curves under sub-critical flow conditions.The dependence of the turbine efficiency on the Froude Number (Fr), Reynolds Number (Re) and Blockage Ratio (Br) was thus investigated, showing their relevance for the overall turbine assessment. The overall energy exploitation was strictly dependent on the contribution of the potential head, thus showing the significant reduction of the turbine efficiency for partially submerged operations.

Machine Learning-Based Precise Monitoring of Aluminium-Magnesium Alloy Dust

： Al-Mg alloys are widely used in industrial production, which can lead to occupational health issues and explosion hazards. The study focuses on applying a machine learning-enhanced Kalman filtering algorithm to detect the concentration of Al-Mg alloy dust, significantly reducing dust hazards and constructing an efficient and safe dust reduction and removal system. A machine learning-based Kalman filter algorithm is proposed for fast and accurate detection of high Al-Mg dust concentrations (200-1200 g/m³). The results show that the KFGRU approach outperforms the traditional line filter method, achieving answer times between 2.6 s and 6 s—an improvement of 62.5% over the traditional method. As far as the forecast accuracy is concerned, the KFGRU method yields a minimal curve deviation value, reaching as low as 0.097, which represents a significant improvement compared to the 0.151 of the Kalman filter algorithm, the 0.217 of the sliding average method, and the 0.177 of the median filter methods.

Natural resource assets management and urban carbon emission efficiency: Evidence from quasi-natural experiment in China

Efficient management of natural resources plays an important role in improving urban low-carbon development. Based on panel data from 277 prefecture-level cities in China, this paper investigates the impact of the “Natural Resource Assets Management Audit” (NRAMA) policy on urban carbon emission efficiency (CEE) using the staggered DID approach. Our findings reveal that the NRAMA policy significantly improves urban CEE and shows heterogeneity from the perspective of regional features, city size, resource dependency, government intervention, and marketization level. The mechanism test results suggest that the pilot policy affects urban CEE through efficient natural resource utilization, better environmental protection, government audit, fiscal expenditures, and urban green innovation. Additionally, population density plays a significant moderating role. Our study supplements new evidence on the relationship between natural resource assets management and efficient carbon emission reduction and provides key insights regarding the effective implementation of the NRAMA policy.

Strategic optimization of dual-fuel diesel/gas engines by numerical approach for environmental and economic benefits

The study introduces a framework for optimizing eco-friendly dual-fuel engines, combining CFD and ANN, to reduce carbon emissions and improve sustainable transportation.We achieved optimal performance with a 52–65 % NG substitution ratio. SOI timing had a big effect on NOx emissions; later injection decreased NOx formation because it made the mixture more homogeneous. We developed a radial basis function neural network model to predict engine performance and emissions with 99 % accuracy. The study examines the effect of fuel droplet spraying on cylinder walls on engine performance and emissions, utilizing response surface methodology to create engine maps. Numerical experiments revealed that a lowered natural gas replacement ratio of less than 50 % resulted in decreased flame propagation and the attainment of a burn mixture mode. The decrease in flame propagation speed during CA10 led to a fivefold increase in CO emissions. As the NG substitution ratio exceeded 50 %, the air/fuel ratio neared equilibrium, resulting in a reduction in NOx emissions and in-cylinder combustion temperature. Nevertheless, the highest RoHR level decreased by 25 %. The downward trend proceeded until the natural gas substitution ratio reached to 90 %. The TOPSIS method was utilized to identify optimal operating conditions for DFDI engines, offering valuable insights for efficiency and emissions reduction.

Construction of an S-scheme AgBr/BiOBr heterojunction by in situ hydrolysis for highly efficient photocatalytic reduction of CO2 into CO

To improve the performance of CO2 photoreduction to CO, S-scheme AgBr/BiOBr heterojunctions with strong interfacial interactions were prepared using a simple in situ hydrolysis strategy by regulating the stoichiometric ratio of BiOBr and AgNO3. Furthermore, the synthesized AgBr/BiOBr heterojunctions were subjected to microstructure analysis and electrochemical properties testing. The experimental date show that AgBr/BiOBr heterojunctions, which have tight interfacial contact characteristics, boost visible light utilization and facilitate the separation and shift of photogenerated carriers, leading to strong CO2 redox capability. Among the AgBr/BiOBr heterojunctions, the AB-2 specimen has the best selectivity for the product CO. The rate of CO2 photoreduction to CO for AB-2 is 25.36 μmol·g-1·h-1, which is about 2.48 and 11.91 times higher than those of acquired BiOBr and AgBr, respectively. After several cycles, it still exhibits high stability. Furthermore, the electric charge transfer and CO2 reduction mechanism for the S-scheme AgBr/BiOBr heterojunction were investigated. A promising method for the construction of efficient photoreduction CO2 catalyst is proposed in this study.

Zr-MOF/MXene composite for enhanced photothermal catalytic CO2 reduction in atmospheric and industrial flue gas streams

In this study, a novel composite was engineered by integrating Zr-MOF (NH2-UIO-66) with MXene layers through electrostatic self-assembly. Under simulated sunlight and at 80 °C, this composite material achieved nearly complete conversion of low-concentration atmospheric CO2 to CO and CH4 without additional sacrificial agents or alkaline absorption liquids, marking one of the few reports demonstrating near-complete reduction of low-concentration CO2 directly from the air. For high-concentration CO2 in industrial flue gas, the composite utilized residual heat at 80 °C without additional energy input, exhibiting excellent CO2 reduction efficiency with CO and CH4 production rates of 127 μmol·g-1·h-1 and 330 μmol·g-1·h-1, respectively, resulting in a total production rate 4.76 times higher than that in the air. Compared to most reports on thermocatalytic CO2 reduction (>300 °C), this material shows significant advantages below 100 °C. The performance improvement is attributed to the introduction of Zr-MOF, which provides additional active sites and reduces activation energy. Additionally, the localized surface plasmon resonance (LSPR) effect of MXene facilitates the migration of thermal charge carriers to Zr4+ sites within the MOF. Density Functional Theory (DFT) calculations validate these findings. Overall, Zr-MOF/MXene composite holds potential for reducing CO2 in air and industrial settings, advancing energy conversion and environmental management.

Electrocatalytic reduction of nitrate by Cu valence-regulated heterojunction electrode: A key role of Cu(I/II) composites and high nitrate reduction efficiency

Cu – based composite electrode has aroused great concern among NO3− reduction. There are few reports on the synergistic effect of different Cu valence states. It is not clear how to regulate the valence ratio of copper by or controlling the dosage of reducing agent in the synthesis process. Herein, a Cu(I) and Cu(II) heterogeneous electrode with different Cu valences was prepared to build a Cu(I/II)@NF-UX composite electrode on nickel foam. Urea was used as a gentle reductant to construct the heterogeneous structure of Cu(I) and Cu(II) during a one-step hydrothermal process for in – situ growth of Cu(I/II)@NF-UX, and the ratio between Cu(I) and Cu(II) and the microstructure were adjusted by changing the amount of urea added during the preparation process. The strong NO3− adsorption effect of traditional Cu – based electrode is still maintained in the Cu(I/II)@NF composite. The strong interfacial electric field constructed in the Cu(I/II) heterostructure efficiently promoted the electron transfer, and the electrons subsequently reduced the NO3− directly or helped form the atomic H* for NO3− indirect reduction. More importantly, there is almost no accumulation of NO2− and NH4+, efficient and stable nitrate reduction (93.2%) was achieved and 96.2% N2 selectivity was achieved with the help of electric chlorination. It is worth noting that the high pH adaptability, low energy consumption, good stability, and low Cu/Ni leaching would make the Cu(I/II)@NF composite electrode have an excellent application prospect for NO3− removal engineering.

Survey Paper on IoT-Enabled Seed Dispensing Robot

As a result, the IoT-Enabled Seed Dispensing Robot is used to automate the seed planting process with precision technologies. Based on real time environmental conditions such as soil moisture and temperature it optimizes seed placement through GPS tracking and IoT sensor data. Remote monitoring and control of the robot is via a cloud-based platform resulting in significant reduction in labour cost and superior farming efficiency. Having the ability to handle terrain and obstacle mapping, it is well suited for any agricultural application and is a reliable, scalable way to meet the challenges of modern farming.

Conceptual framework for AI-driven tax compliance in fintech ecosystems

This paper comprehensively reviews the integration of Artificial Intelligence (AI) into tax compliance processes within fintech ecosystems. It explores the theoretical foundations of AI technologies, such as machine learning and predictive analytics, and how they can automate tax reporting, auditing, and compliance monitoring. Conceptual models for AI-driven tax compliance are proposed, highlighting the potential for increased efficiency, accuracy, and cost reduction. The paper also examines challenges associated with AI adoption, including algorithmic biases, ethical concerns, data privacy issues, and regulatory hurdles. Strategies for overcoming these challenges and fostering broader adoption are discussed. Finally, the paper offers recommendations for fintech companies and policymakers, emphasizing the need for transparent AI models, bias mitigation, data privacy, and updated regulatory frameworks to ensure fair and effective AI-enabled tax systems.

Scalable AI Workflow Solutions with Cloud and Micro-services

This research explores an AI-driven automation solution that streamlines operations by integrating cloud and microservices architecture [1], [9]. By connecting to widely used platforms (Discord, Google Drive, and Notion) [13], [14], we physically perform daily tasks, manage information, and commu- nicate, eliminating human intervention. The solution uses APIs for instant integration, task tracking, and reporting to facilitate team collaboration [5]. Designed for small to medium-sized teams, this automation system is scalable for future integrations and can adapt to changing business conditions [2]. Evaluation of the effectiveness of the system regarding improvements in operational efficiency, data consistency, and error reduction by focusing on product improvement through smart technology [6]. The framework aims to analyze performance management, enabling teams to focus on profitable activities while driving business growth [7].

Hypercube Pooling for Visual Semantic Embedding

Visual Semantic Embedding ( VSE ) is a primary model for cross-modal retrieval, wherein the global feature aggregator is a crucial component of the VSE model. In recent research, the General Pooling Operator ( GPO ) aggregator, which weighs the features reconstructed from the local feature set to aggregate, facilitates the related models to achieve good retrieval performance. However, the reason for the effectiveness remains to be explored. To enhance the rationality of aggregator designs, we analyze the reason from the perspective of feature space. Indeed, for each data, the local feature set forms a hypercube containing abundant data information, and the feature learned by GPO measures the hypercube, thereby representing the data. The geometric structure of the hypercube implies that the set containing all points within the hypercube is a convex set, so the feature learned by weighted aggregation is an interior point of the hypercube. However, using the interior point to measure the hypercube leads to some problems in feature representation and model optimization, as well as the reduction of retrieval efficiency caused by weight computation. For example, the related pair’s features may be far, while the unrelated ones may be close. To measure the hypercube more clearly and alleviate the problems mentioned above, we propose Hypercube Pooling ( HCP ) aggregator. Specifically, HCP concatenates the Max and Min Pooling features as the global features. This aggregation method has multiple advantages, e.g., the learned global feature represents all hyperplanes of the hypercube that contain critical information and hypercube geometric structure. Moreover, HCP adds normalization-before-concatenation and reduces the usual setting of margin in the loss function by half to avoid gradient loss caused by the difference in the feature value and dimensionality doubling. The experimental results on the Flickr30K and MSCOCO datasets show that the HCP model has excellent performance with high efficiency, confirming the correctness of the spatial analysis and the effectiveness of the HCP aggregator.

Investigation into Recycling of Iron Oxide Scale on H13 Steel by Low‐Temperature Carbothermal Reduction Method

The current method for recovering iron oxide scale in the steel industry is not economically optimal, especially for high‐alloy scales found in alloyed steel. This study focuses on iron oxide scale containing valuable metals like chromium (Cr), molybdenum (Mo), and vanadium (V). The required carbon addition is calculated based on the iron and chromium oxides in the scale. The effects of varying carbon additions and reduction temperatures on reduction efficiency are thoroughly examined. Kinetic studies show that as temperature and carbon increase, the rate‐limiting step shifts from interfacial diffusion to interfacial reaction. Reduction experiments assess carbon utilization, metallization rate, deoxidation rate, and removal of harmful elements. Results show that high temperatures hinder sulfur (S) and phosphorus (P) removal, and excess carbon reduces carbon utilization efficiency. Optimal conditions are a carbon ratio of 0.2174 and a temperature of 1150 °C. The carbothermic reduction product requires further refinement through conventional ladle slag systems to meet the quality standards for metallic materials. Over 65% of alloying elements are recovered, though phosphorus content remains slightly higher than in finished alloy steel. The materials from this study are suitable as high‐quality intermediates for alloy steel production.

One-step synthesized Pt-dispersed N, P co-doped graphene as an efficient oxygen reduction reaction ORR electrocatalyst

One-step synthesized Pt-dispersed N, P co-doped graphene as an efficient oxygen reduction reaction ORR electrocatalyst

Characterization of DsrD and its interaction with the DsrAB dissimilatory sulfite reductase.

Microbial dissimilatory sulfate reduction is a key process in the global sulfur and carbon cycles in anoxic ecosystems. In this anaerobic respiration, sulfate is phosphorylated and reduced to sulfite, which is further reduced to a DsrC-trisulfide by the dissimilatory sulfite reductase DsrAB. DsrD is a small protein that acts as an allosteric activator of DsrAB, increasing the efficiency of sulfite reduction. Here, we report a detailed study of DsrD and its interaction with DsrAB. Sequence similarity analyses show that there are three groups of DsrD in organisms with a reductive-type DsrAB. The protein regions involved in the DsrD-DsrAB interaction and activity-promoting effect were investigated through in vitro and in silico studies, including mutations of conserved DsrD residues. The results reveal that the conserved β-loop of DsrD is involved in the interaction, contributing to a better understanding of its mechanism of action.

Fabrication of Ultrahigh-Loading Dual Copper Sites in Nitrogen-Doped Porous Carbons Boosting Electroreduction of CO2 to C2H4 Under Neutral Conditions.

Synthesis of high-loading atomic-level dispersed catalysts for highly efficient electrochemical CO2 reduction reaction (eCO2RR) to ethylene(C2H4) in neutral electrolyte remain challenging tasks. To address common aggregation issues, a host-guest strategy is employed, by using a metal-azolate framework (MAF-4) with nanocages as the host and a dinuclear Cu(I) complex as the guest, to form precursors for pyrolysis into a series of nitrogen-doped porous carbons (NPCs) with varying loadings of dual copper sites, namely NPCMAF-4-Cu2-21 (21.2 wt%), NPCMAF-4-Cu2-11 (10.6 wt%), and NPCMAF-4-Cu2-7 (6.9 wt%). Interestingly, as the loading of dual copper sites increased from 6.9 to 21.2 wt%, the partial current density for eCO2RR to yield C2H4 also gradually increased from 38.7 to 93.6 mA cm-2. In a 0.1 m KHCO3 electrolyte, at -1.4 V versus reversible hydrogen electrode (vs. RHE), NPCMAF-4-Cu2-21 exhibits the excellent performance with a Faradaic efficiency of 52% and a current density of 180 mA cm-2. Such performance can be attributed to the presence of ultrahigh-loading dual copper sites, which promotes C─C coupling and the formation of C2 products. The findings demonstrate the confinement effect of MAF-4 with nanocages is conducive to the preparation of high-loading atomic-level catalysts.

Photocatalytic Upcyclingof Plastic Waste into Syngas by ZnS/Ga2O3 Z-scheme Heterojunction.

The photocatalytic conversion of plastic waste into value-added products using solar energy presents a promising approach for promoting environmental sustainability. Nonetheless, the emission of CO2 during the conventional photocatalytic degradation process remains a major hurdle that impedes its further development. In this study, we propose an efficient photocatalytic conversion of polyethylene plastic into syngas (CO + H2 mixtures) by using a ZnS/Ga2O3 Z-scheme heterojunction photocatalyst. It is found that the strong redox capability of photogenerated holes and electrons in the Z-scheme heterojunction photocatalyst can promote the oxidative depolymerization of PE plastic, concurrently enabling the efficient reduction of the intermediate product CO2 into syngas. Furthermore, this system also demonstrates applicability in the conversion and upcycling of other polyolefin plastics including polypropylene and polyvinyl chloride. Our findings highlight the potential of polyolefin plastics photoreforming for the production of syngas under environmentally benign conditions.

Efficient Reduction of Carrier Concentration in SnTe: The Case of Gd Doping

Efficient Reduction of Carrier Concentration in SnTe: The Case of Gd Doping

Enhanced Charge Transfer in Poly(Heptazine Imide) Synergistically Induced by Donor-Acceptor Motifs and Ohmic Junctions for Efficient Photocatalytic CO2 Reduction.

Poly(heptazine imide) (PHI) has received widely interest in the photocatalytic CO2 reduction due to its good crystallinity and complete in-plane structure. However, its poor photo-induced carrier separation and migration efficiency and insufficient active sites results in undesirable photocatalytic CO2 reduction performance. Herein, we designed and constructed a novel ohmic junction photocatalyst by integrating melamine edge-modified PHI (mel-PHI) with extended π-conjugated system with TiN (TiN/mel-PHI) for enhancing the photocatalytic CO2 reduction activity. Strikingly, the photocayalytic CO2 reduction yield of the optimal TiN/mel-PHI is 62.64 µmol·g-1·h-1, which is 5.6 and 2.8 times higher than PHI (11.26 µmol·g-1·h-1) and mel-PHI (22.32 µmol·g-1·h-1), respectively. The superior photocatalytic CO2 reduction activity is attributed not only to the formation of D-A structure by the introduction of melamine, which extends the π-conjugation system, alters the electronic structure of PHI, and accelerates the charge separation and migration, but also to the induced internal electric field by ohmic junction further enhances the charge separation and migration efficiency. Meanwhile, the synergistic effect of mel-PHI and TiN enriched the electron number of TiN, reducing the CO2 reduction potential. This work highlights the synergistic enhancement of charge transfer between D-A motifs and ohmic junctions, confirming their potential in optimizing photocatalysts.