Superior Reduction Research Articles

Decoupled Spatial and Temporal Processing for Resource Efficient Multichannel Speech Enhancement

—Speech enhancement is essential in various modern applications, including telecommunications, hearing aids, and voice-controlled systems. The presence of background noise and reverberation can significantly degrade speech quality, making it challenging to understand. Traditional single-channel speech enhancement techniques often struggle in complex acoustic envi- ronments. In contrast, multichannel speech enhancement, which utilizes multiple microphones, offers improved performance by leveraging spatial information to better separate speech from noise. An original model intended for asset effective multichannel discourse upgrade the time space, with an emphasis on low inert- ness, lightweight, and low computational necessities The support- ive of presented model consolidates express spatial and transient handling in inside profound brain organization (DNN) layers. Motivated by recurrence subordinate of multichannel separating, our spatial sifting process applies different teachable channels to each secret unit across the spatial dimensions, coming about in a multichannel yield. The fleeting handling is applied over a solitary channel yield stream from the spatial favorable to accessing utilizing a Long Transient Memory (LSTM) organization. Model fundamentally beats powerful gauge models while requesting far less boundaries and for calculations, while accomplishing a super low algorithmic idleness of only 2 milliseconds. By leveraging the spatial diversity of multichannel recordings and combining it with advanced temporal algorithms, the proposed system achieves superior noise reduction and speech enhancement. This approach has the potential to improve the performance of various applications, including telecommunications, hearing aids, and voice-controlled systems, providing clearer and more intelligible speech in challenging acoustic environments. Index Terms—Multichannel Speech Enhancement, Deep Neu- ral Networks (DNN), Signal-to-Noise Ratio (SNR), Spatial Pro- cessing, Temporal Processing, Low Latency.

DEVELOPMENT OF A WEBSITE-BASED FETAL MOVEMENT MONITORING TOOL

Background: The development of technology by utilizing sensors to record body bioelectricity is a challenge in the transformation of health in the pillar of biotechnology that can be used to detect fetal movement with a non-transmission and inexpensive system. Advances in sensor technology can be used for long-term fetal movement recording. This study discusses the development of a system that can be used by pregnant women to monitor fetal movement using an accelerometer that has a fairly high accuracy in detecting fetal movement. Mobile phone applications to monitor fetal movement using an accelerometer in areas with limited resources have a fairly high level of accuracy and can be used as a tool in monitoring fetal movement. Research on fetal movement recording devices can provide valuable information about the condition of the fetus and its health, and has developed into a reliable technology that can be used in clinical settings. Objective: to develop a website-based fetal recording device. Method: The research method used is the research and development (R&D) method, this method is used to develop certain products and test the effectiveness of the product. Results: The results displayed in the software are in the form of excel which will be processed into a logarithm of fetal movement recorded by the electrode and then an analysis of fetal movement signals will be carried out compared to the movement results from the mother's perception. Conclusion: The prototype demonstrates superior noise reduction capabilities but may require optimized preprocessing to preserve event-related signal variations.

Pt2Gd Alloy Nanoparticles from Organometallic Pt and Gd Complexes and Hollow Mesoporous Carbon Spheres: Enhanced Oxygen Reduction Reaction Activity and Durability.

Pt2Gd alloy nanoparticles supported in hollow mesoporous carbon spheres (HMCS; Pt2Gd/HMCS) were successfully prepared by the thermal reduction of organometallic Pt and Gd complexes without oxygen atoms supported in the pores of HMCS. The structures of Pt2Gd alloy nanoparticles were fully characterized by TEM, HAADF-STEM-EDS, XRD, XAFS, and XPS, suggesting the formation of uniform Pt2Gd alloy nanoparticles with an average particle size of 5.9 nm. Pt2Gd/HMCS showed superior oxygen reduction reaction activity (2.4 times higher mass-specific activity to Pt nanoparticles on HMCS (Pt/HMCS)) and remarkable durability even after the 100,000 cycles of accelerated degradation tests compared to Pt/HMCS and a commercial Pt/C catalyst. The structure of the Pt2Gd alloy nanoparticles after initial aging and the durability tests suggested that the Pt2Gd core-Pt shell structure with a particle size similar to the pore size of HMCS was stably formed inside the porous structure of HMCS and maintained under the oxygen-reduction working conditions.

Impact of tree growth form on temporal and spatial patterns of particulate matter with various particle sizes in urban street canyons

ContextTrees play a vital role in reducing street-level particulate matter (PM) pollution in metropolitan areas. However, the optimal tree growth type for maximizing the retention of various sizes of PM remains uncertain.ObjectivesThis study assessed the PM reduction capabilities of evergreen and deciduous broadleaf street trees, focusing on how leaf phenology influences the dispersion of pollutants across particle sizes.MethodsWe collected data on six PM size fractions from 72 sites along streets lined with either evergreen or deciduous broadleaf trees in Wuhan, China, during the summer and winter of 2017–2018.ResultsEvergreen trees demonstrated superior PM reduction capabilities compared to deciduous trees, with evergreen street canyons showing 27.2% and 12.6% lower PM2.5 and PM10 concentrations in summer, and 13% and 5.5% lower concentrations in winter. During summer, evergreen streets predominantly contained fine particles (PM1, PM2.5), posing potential health risk due to their ability to infiltrate the human respiratory system. In contrast, deciduous streets primarily harbored coarser particles (PM4, PM7, PM10, and total suspended particulate [TSP]). During winter, larger particles were dominant, regardless of the tree growth form.ConclusionsEvergreen trees showed superior PM reduction capabilities compared to deciduous trees due to their year-round leaf retention, enhanced surface properties, and denser canopies that maximize PM capture. We recommend prioritizing evergreen broadleaf trees as the primary street trees while interspersing deciduous trees at appropriate intervals. This approach will ensure that urban greenery provides maximum ecological benefits while reducing the PM concentration.

Ordered Mesoporous Fe-Nx/C Materials as Highly Efficient Self-supporting Electrocatalysts for Oxygen Reduction

A new mesostructured non-precious-metal catalyst (NPMC) was easily created using a straightforward nanocasting technique with 1,10-phenanthroline iron chelates as the sole precursor. The resulting Fe-Nx/C material, characterized by its Hexagonal mesostructures with an ordered arrangement, Extensive surface extent, expansive pore structure, and evenly Scattered Fe-Nx groups within Graphite-like carbon structures, Established to be an remarkable self-supporting cathode catalyst for the oxygen reduction reaction (ORR). It demonstrated superior specific oxygen reduction activity in 0.1M KOH alkaline solution compared to commercial Pt/C catalysts (20wt%Pt, JM), showing a more positive onset potential (0.0V vs Ag/AgCl), approximately 1.7 times the kinetic-limiting current density of Pt-C-JM, and higher current density across the entire potential range. Additionally, it exhibited an onset potential of 0.93V (vs RHE) for ORR and about 1.3 times the current density at 0.6V (4.9mA cm-2) compared to the best NPMC materials reported in the literature, with a catalyst loading of 0.6mg cm-2 in 0.5M H2SO4 media (approximately 3.8mA cm-2).

Rational Design of Atomically Immobilized Fe‐N4 Sites via Acid‐Amine Chemistry: Leveraging Metal Ionic Assistance for Precise Morphology Control

ABSTRACTTailoring atomically dispersed single‐atom catalyst (Fe‐SAC) holding well‐defined coordination structure (Fe‐N4) along with precise control over morphology is a critical challenge. Herein, we propose a novel acid‐amine coupling reaction between metal‐chelated ionic liquid ([1‐(3‐aminopropyl) 3‐methylimidazolium tetrachloroferrate(III)] [APIM]+[FeCl4]−) and carboxylic groups of carbon allotropes (C = GO, CNT, CNF, and vX‐72) to precisely immobilize Fe‐Nx sites. Out of designed single‐atom catalyst (IL‐Fe‐SAC‐C), Fe‐N4 on graphene (IL‐Fe‐SAC‐Gr) delivered superior oxygen reduction reaction (ORR) activity by holding higher halfwave potential of 0.882 V versus RHE in 1.0 M KOH akin to Pt/C (0.878 V vs. RHE) and surpassing recently reported M–N–C catalysts with superior ethanol tolerance. Thanks to higher graphitization degree, enhanced surface characteristics, and richness in high‐density Fe‐N4 sites of IL‐Fe‐SAC‐Gr confirmed by XPS, X‐ray absorption spectroscopy (XAS), and HAADF analysis. The IL‐Fe‐SAC‐Gr catalyst‐coated cathode on testing in flexible direct ethanol fuel cells (f‐DEFC) delivered higher peak power density of 18 mW cm−2 by outperforming Pt/C‐based cathode by 3.5 times as a result of excellent ethanol tolerance. Further, the developed f‐DEFC successfully powered the Internet of Things (IoT)‐based health monitoring system. This method demonstrates novel strategy to tailor high‐performance single‐atom (Fe‐SAC‐C) sites on desired morphologies to meet specific application requirements with feasibility and versatility.

Axisymmetric cavity noise control using passive protrusion

This paper introduces a passive protrusions to reduce undesirable structural loading and high-amplitude noise radiation in axisymmetric cavities, are common in industries, including pipelines and aerospace. A comprehensive experimental investigation of the pipe-cavity noise control system is conducted, exploring various protrusion locations and lengths at different Nozzle Pressure Ratios (NPRs). The study examines protrusions placed at the leading, trailing, and both leading and trailing edges of the axisymmetric cavity. Additionally, detailed experiments are performed to analyze the impact of protrusion lengths on unsteady cavity pressure and far-field noise radiation. Cavity pressure fluctuations and far-field noise levels are measured for both cases: with and without protrusions in the pipe-cavity setup. The Proper Orthogonal Decomposition (POD) technique is applied to the time-resolved Schlieren images to show the effect of passive protrusions on the exit jet flow structure. Results demonstrate that a protrusion located at the trailing edge yields superior noise reduction compared to other configurations. The efficacy of this protrusion in mitigating cavity noise is attributed to its ability to efficiently alleviate the feedback mechanism responsible for cavity noise generation and break down the recirculation region within the cavity system. Significant noise reduction, approximately 4 dB, is achieved at lower NPRs, and substantial noise reductions are also observed for the underexpanded jet condition.

Word embedding empowered topic recognition in news articles

Advancements in technology have placed global news at our fingertips, anytime, anywhere, through social media and online news sources. Analyzing the extensive electronic text collections is urgently needed. According to the scholars, combining the topic and word embedding models could improve text representation and help with downstream tasks related to natural language processing. However, the field of news topic recognition lacks a standardized approach to integrating topic models and word embedding models. This presents an exciting opportunity for research, as existing algorithms tend to be overly complex and miss out on the potential benefits of fusion. To overcome limitations in news text topic recognition, this research suggests a new technique word embedding latent Dirichlet allocation that combines topic models and word embeddings for better news topic recognition. This framework seamlessly integrates probabilistic topic modeling using latent Dirichlet allocation with Gibbs sampling, semantic insights from Word2Vec embeddings, and syntactic relationships to extract comprehensive text representations. Popular classifiers leverage these representations to perform automatic and precise news topic identification. Consequently, our framework seamlessly integrates document-topic relationships and contextual information, enabling superior performance, enhanced expressiveness, and efficient dimensionality reduction. Our word embedding method significantly outperforms existing approaches, reaching 88% and 97% accuracy on 20NewsGroup and BBC News in news topic recognition.

Volumetric mix-design modification and vibration attenuation analysis of rubberised epoxy asphalt track for railway

Rubberized asphalt mixture effectively reduces vibrations in railway track due to the elasticity of crumb rubber (CR) and the viscoelasticity of asphalt binder. This study aims to design a rubberized epoxy asphalt mixture using the modified volumetric mix-design method named the multi-point supported skeleton for asphalt mixtures (V-S method). The feasibility and vibration attenuation of the precast epoxy asphalt track was evaluated through a 3D FEM simulation at a 350 km/h speed. By combining the V-S method with the equivalent volume replacement principle between CR and coarse aggregate, four types of rubberized epoxy asphalt mixtures were proposed using the dry method. Indoor test results confirm that these mixtures meet the required specifications. The FEM results demonstrate that the designed epoxy asphalt track meets the design criteria and the components of the EA-4CR track structure demonstrate superior vibration reduction performance among the cases with the same increment in CR content.

Efficacy and safety of guselkumab in patients with active lupus nephritis: results from a Phase 2, randomized, placebo-controlled study.

Evaluate the efficacy and safety of guselkumab, an interleukin (IL)-23 inhibitor, in a Phase 2, multicentre, randomized, double-blind, placebo-controlled study of patients with active lupus nephritis (LN). Adults (18-75 years) with active LN (Class III-IV proliferative nephritis [kidney biopsy] and urine protein-to-creatinine ratio [UPCR)] of ≥ 1 mg/mg despite standard-of-care therapy) were randomized (1:1; planned sample = 60) to receive intravenous infusions of guselkumab 400 mg or placebo at Weeks 0, 4, and 8, then subcutaneous injections (guselkumab 200 mg or placebo) at Week12 and every 4 weeks through Week48 in addition to their background therapy. The primary end point was achievement of ≥ 50% decrease in proteinuria from baseline at Week24. Major secondary endpoints (Week24) were achievement of complete renal response (CRR), sustained reduction in steroid dose (≤10 mg/day prednisone/equivalent) from Weeks 16-24, UPCR <0.5 mg/mg; <0.75 mg/mg, time to achieving CRR, and time to treatment failure. Safety was assessed through end-of-study. Following enrolment challenges (COVID-19 pandemic; Ukraine/Russia crisis), the sponsor terminated the study early; 33 participants were randomized (placebo, n = 16; guselkumab, n = 17). At Week24, 56.3% (9/16) in the placebo group and 35.3% (6/17) in the guselkumab group achieved the primary end point. No apparent differences were observed in the secondary endpoints. Through end-of-study, 75% of placebo patients and 71% of guselkumab patients reported ≥1 adverse event; most were mild-to-moderate severity. Guselkumab+background therapy did not demonstrate superior reduction in proteinuria vs placebo+background in this small cohort of patients with active LN. Safety results were consistent with the known safety profile of guselkumab. clinicaltrials.gov: NCT04376827.

Emulsifying and ethanol-induced gelling properties of capsicum pectin: The impact of capsicum variety and deproteinization using the Sevag method.

This study investigated the emulsifying and ethanol-induced gelling properties of capsicum pectins from bell pepper and chili pepper, and also examined the effect of deproteinization (using the Sevag method). The research revealed that bell pepper pectin (BPP) formed smaller aggregates (337nm at pH2) with a higher hydrophobicity in solution due to its lower molecular weight (2.02×105g/mol) and higher degree of esterification (82.15%) compared to chili pepper pectin (CPP) (3.29×105g/mol, 78.33%). Consequently, BPP exhibited superior interfacial tension reduction ability, resulting in emulsion with smaller droplet size (483nm at pH2). Conversely, CPP showed superior ethanol-induced gelling property in comparison to BPP, as its lower degree of esterification and higher molecular weight facilitated hydrogen bonding interactions and crosslinking during gelation. Deproteinization with the Sevag method reduced protein content, molecular weight and esterification degree of BPP and CPP, but its impact on their emulsifying properties differed significantly. Interestingly, deproteinization notably improved the ethanol-induced gelling property of both pectins, since the increased carboxyl groups in the deproteinized products promoted hydrogen bonding during gelation. This study could facilitate the comprehensive utilization of capsicum and also advance our understanding of the impact of the Sevag method.

ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training.

Negative flips are errors introduced in a classification system when a legacy model is updated. Existing methods to reduce the negative flip rate (NFR) either do so at the expense of overall accuracy by forcing a new model to imitate the old models, or use ensembles, which multiply inference cost prohibitively. We analyze the role of ensembles in reducing NFR and observe that they remove negative flips that are typically not close to the decision boundary, but often exhibit large deviations in the distance among their logits. Based on the observation, we present a method, called Ensemble Logit Difference Inhibition (ELODI), to train a classification system that achieves paragon performance in both error rate and NFR, at the inference cost of a single model. The method distills a homogeneous ensemble to a single student model which is used to update the classification system. ELODI also introduces a generalized distillation objective, Logit Difference Inhibition (LDI), which only penalizes the logit difference of a subset of classes with the highest logit values. On multiple image classification benchmarks, model updates with ELODI demonstrate superior accuracy retention and NFR reduction.

Real-time hybrid testing validation of a stiffness-controllable mass damper for reducing vibrations in nonlinear structures

The design of passive tuned mass dampers (TMDs) and the evaluation of their vibration control performance typically rest on the assumption that the primary structure is a linear system. The optimized TMD is then tuned to the natural frequency of the primary structure, enabling the transfer of vibrational energy from the structure to the TMD and its absorption through the damping mechanism of the TMD, thereby reducing vibrations in the primary structure. However, if the structure exhibits nonlinear behavior during an earthquake, causing the natural frequency of the primary structure to vary, the passive TMD may experience a detuning effect, leading to significant degradation in its control performance. To address this challenge, this paper introduces a novel mass damper with controllable stiffness (MDCS) that dynamically adjusts its stiffness to adapt to changes in structural frequency due to nonlinear responses. This feature enables the MDCS to maintain optimal performance under nonlinear conditions. First, a mathematical model of a nonlinear structure with an MDCS was established, and a least input energy method (LIEM) control law suitable for nonlinear structures was developed to minimize the total energy of the nonlinear structure and the MDCS. This study verified the theoretical model and the LIEM control algorithm by conducting real-time hybrid testing (RTHT) using a physical lever-type MDCS mechanism in combination with a nonlinear numerical (substructure) model. The RTHT results demonstrated that the MDCS provides superior vibration reduction and reduced stroke in nonlinear structures compared to passive TMDs, marking a significant advancement in vibration control for seismic applications.

Plasma-Assisted Preparation of Reduced Graphene Oxide and Its Applications in Energy Storage.

Reduced graphene oxide (rGO) exhibits mechanical, optoelectronic, and conductive properties comparable to pristine graphene, which has led to its widespread use as a method for producing graphene-like materials in bulk. This paper reviews the characteristics of graphene oxide and the evolution of traditional reduction methods, including chemical and thermal techniques. A comparative analysis reveals that these traditional methods encounter challenges, such as toxicity and high energy consumption, while plasma reduction offers advantages like enhanced controllability, the elimination of additional reducing agents, and reduced costs. However, plasma reduction is complex and significantly influenced by process parameters. This review highlights the latest advancements in plasma technology for reducing graphene oxide, examining its effectiveness across various gas environments. Inert gas plasmas, such as argon (Ar) and helium (He), demonstrate superior reduction efficiency, while mixed gases facilitate simultaneous impurity reduction. Additionally, carbon-based gases can aid in restoring defects in graphene oxide. This paper concludes by discussing the future prospects of plasma-reduced graphene and emphasizes the importance of understanding plasma parameters to manage energy and chemical footprints for effective reduction.

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 photocatalytic 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.

Impact of Local Structure and Spin on the ORR Performance of Single Atom M-N-C Catalysts

Transition metal single-atom catalysts supported on N-doped graphene (M-N-Cs) offer significant advantages in metal utilization and uniformity of active sites compared to traditional heterogeneous catalysts. They are particularly notable for their superior electrocatalytic Oxygen Reduction Reaction (ORR) performance. Various modifications have been explored, including different metal combinations, different first shell elements (N, C, S, B), and coordination numbers, to enhance electrocatalytic ORR activities. Another emerging focus is the link between their local structure and spin state. The Zhenan Bao and Jaramillo groups recently found that the catalytic activity of the NiNx moiety stems from its tetrahedrally distorted structure because it stabilizes the high-spin Ni state more than the low-spin state.[1] The local structure and its distortion can be modified by adjusting the carbon nanotube diameter,[2] pore size,[3] layering,[4] or by adding ligands at the first or second shell.[5]In this study, we have conducted a computational screening of M-N-Cs with 3d, 4d, and 5d metals and local structures including square planar, tetragonal pyramidal, and tetrahedral symmetries. Metals in their divalent states within the MN4 units exhibited a V-shaped trend in formation energy correlated with increasing atomic numbers across each series. This pattern appears to be linked to the interaction between the two electrons of the N4 ligand and the d electron count of the metals, and it also emerged in the *O/*OH adsorption energy plots. Additionally, the preferred spin state varied with the metal type and structural symmetry, influencing electron distribution and crystal field splitting. These factors collectively impacted the binding energies with adsorbents, subsequently altering oxygen reduction reaction (ORR) activity. Further, we explored the adsorption energies of various intermediates (*CO, *H, *Cl, and *N2H) relevant to carbon dioxide reduction reaction (CO2RR), hydrogen evolution reaction (HER), chlorine evolution reaction (ClER), and nitrogen reduction reaction (NRR), respectively. Our primary objective was to identify optimal metal-structure combinations for enhanced catalytic performance. To achieve this, we considered several descriptors including partial Integrated Crystal Orbital Hamilton Population (ICOHP), metal d-band center, Madelung energy, and metal ionization potential. Moving forward, we aim to extend these findings by incorporating distorted coordination environments and integrating experimental synthesis strategies to induce such distortions, potentially unlocking new pathways for catalytic efficiency enhancement.[1] Koshy, David M., et al. "Investigation of the Structure of Atomically Dispersed NiNx Sites in Ni and N-Doped Carbon Electrocatalysts by 61Ni Mössbauer Spectroscopy and Simulations." Journal of the American Chemical Society 144.47 (2022): 21741-21750.[2] Cepitis, Ritums, et al. "Surface curvature effect on dual-atom site oxygen electrocatalysis." ACS Energy Letters 8.3 (2023): 1330-1335.[3] Glibin, Vassili P., et al. "Non-PGM electrocatalysts for PEM fuel cells: thermodynamic stability and DFT evaluation of fluorinated FeN4-based ORR catalysts." Journal of The Electrochemical Society 166.7 (2019): F3277.[4] Wu, Yahui, et al. "Boosting CO2 electroreduction over a cadmium single‐atom catalyst by tuning of the axial coordination structure." Angewandte Chemie International Edition 60.38 (2021): 20803-20810.[5] Choi, Daeeun, et al. "Bridging the Catalytic Turnover Gap Between Single‐Atom Iron Nanozymes and Natural Enzymes by Engineering the First and Second Shell Coordination." Advanced Materials (2023): 2306602.

Efficient curvilinear optical proximity correction using non-uniform B-spline curves.

The curvilinear mask has received much attention in recent years due to its better lithography imaging fidelity than the Manhattan mask. As a significant part of computational lithography techniques, the curvilinear OPC optimally designs the mask contour represented by parametric curves to generate a curvilinear mask structure. However, the current curvilinear OPC process is computationally intensive and contains redundant data. In this paper, a curvilinear OPC method using the non-uniform B-spline curve, together with a knot removal process, is proposed to improve the optimization efficiency and reduce the mask file size. The non-uniform B-spline curve is used to characterize curvilinear mask structure without a complex splicing process, which can effectively reduce the computation complexity. To our best knowledge, knot removal theory is for the first time applied to solve the redundant data problem in curvilinear OPC. Simulations and comparisons verify the superior optimization efficiency and data reduction (DRON) rate of the proposed method.

Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction.

To evaluate the effectiveness of super-resolution deep learning reconstruction (SR-DLR) in low-dose abdominal computed tomography (CT) imaging compared with hybrid iterative reconstruction (HIR) and conventional deep learning reconstruction (cDLR) algorithms. We retrospectively analyzed abdominal CT scans performed using a low-dose protocol. Three different image reconstruction algorithms-HIR, cDLR, and SR-DLR-were applied to the same raw image data. Objective evaluations included noise magnitude and contrast-to-noise ratio (CNR), as well as noise power spectrum (NPS) and edge rise slope (ERS). Subjective evaluations were performed by radiologists, who assessed image quality in terms of noise, artifacts, sharpness, and overall diagnostic utility. Raw CT image data were obtained from 35 patients (mean CTDIvol 11.0 mGy; mean DLP 344.8 mGy/cm). cDLR yielded the lowest noise levels and highest CNR (p < 0.001). However, SR-DLR outperformed cDLR in terms of noise texture and resolution, achieving the lowest NPS peak and highest ERS (p < 0.001 and p = 0.005, respectively). Subjectively, SR-DLR was rated highest across all categories, including noise, artifacts, sharpness, and overall image quality, with statistically significant differences compared to cDLR and HIR (p < 0.001). SR-DLR was the most effective reconstruction algorithm for low-dose abdominal CT imaging, offering superior image quality and noise reduction compared to cDLR and HIR. This suggests that SR-DLR can enhance the reliability and diagnostic accuracy of abdominal imaging, particularly in low-dose settings, making it a valuable tool in clinical practice.

UV–visible-infrared light driven photothermal synergistic catalytic reduction of CO2 over Cs3Bi2Br9/MoS2 S-scheme photocatalyst

Photothermocatalytic CO2 reduction has been considered as a green and sustainable strategy for solar-to-fuel conversion, since it can utilize the solar energy to simultaneously provide heat input and produce photogenerated charge carriers. To this end, exploring photothermal catalysts with broad-band absorption, high photo-heat conversion and charge separation efficiency is highly desirable. In this work, an innovative Cs3Bi2Br9/MoS2 (CBB/MoS2) composite has been elaborately constructed to investigate the photothermocatalytic performance towards CO2 reduction. In this composite, MoS2 plays dual roles: with photoinduced self-heating effect, it can act as an extra heater to accelerate the catalytic reaction, and meanwhile serves as a cocatalyst to promote charge separation by forming S-scheme heterojunction with CBB. As expected, the developed CBB/MoS2 composite delivered outstanding photothermocatalytic activity for CO2 reduction without any extra heat input, with the CO production rate reaching 172.79 μmol g−1h−1. As confirmed by experimental tests and theoretical calculations, the superior photothermocatalytic CO2 reduction performance of CBB/MoS2 was attributed to the synergetic effect of high photo-thermo transformation efficiency and highly improved charge separation. The present work offers a potential strategy for developing highly-efficient photothermal catalysts used in artificial photosynthesis.

Hydrothermal-mediated in-situ nitrogen doping to prepare biochar for enhancing oxygen reduction reactions in microbial fuel cells

Nitrogen-doped carbon materials are deemed promising cathode catalysts for microbial fuel cells (MFCs). The challenge lies in reducing costs and enhancing the proportion of electrocatalytically active nitrogenous functional groups. This study proposes a hydrothermal-mediated in-situ doping method to produce nitrogen-doped biochar from aquatic plants. The nitrogen atoms are anchored in the carbon structure during hydrothermal treatment. Subsequent pyrolysis converts the hydrochar into a catalyst with highly catalytically active aromatic ring structure (HC-N+PY). The as-prepared HC-N+PY electrocatalyst demonstrates superior oxygen reduction reaction activity with half-wave potentials of 0.82 V. The MFC with HC-N+PY exhibits excellent performance, with a peak power density of 1444 mW/m2. Theoretical calculations demonstrate that the synergistic effect of graphitic nitrogen and C–O groups at defect sites enhances O2 adsorption and protonation. This work highlights the potential of utilizing nitrogen-doped biochar derived from aquatic plants as an effective catalyst for enhancing the performance of microbial fuel cells.