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High‐performance planar‐heterojunction (PHJ) solar cells based on large band‐gap (1.70–1.83 eV) perovskites are demonstrated. By incorporating Cl− into the MAPb(I1‐YBrY)3, the crystallinity and homogeneity of the solution‐processed perovskite thin films are much improved, which results in an increased charge carrier diffusion length from ≈160 nm to ≈700–800 nm. This enhancement leads to a high power conversion efficiency (PCE) of 10% in the low‐temperature solution‐processed PHJ devices. [ABSTRACT FROM AUTHOR]

A new synthetic method tofabricate Ti3+-modified, highly stable TiO2 photoanodes for H2O oxidation is reported. With Ti foil as both the conducting substrate and the Ti3+/Ti4+ source, one-dimensional blue Ti3+/TiO2 crystals were grown by a one-step hydrothermal reaction. The concentration of Ti3+ was further tuned by N2H4 reduction, leading to a greater photoelectrocatalytic activity, as evidenced by a high photocurrent density of 0.64 mAcm-2 at 1.0 V vs RHE under simulated AM 1.5 G illumination. Electron paramagnetic resonance and Mott-Schottky plots reveal that higher chargecarrier density owing to N2H4 reduction contributes to the observed improvement. The generality of this synthesis method was demonstrated by its effectiveness in improving the performance of other types of photoanodes. By integrating the advantages of the 1D TiO2 architecture with those of Ti3+ self-doping, this work provides a versatile tool toward the fabrication of efficient TiO2 photoanodes. [ABSTRACT FROM AUTHOR]

Abstract: Self-doped TiO2 shows visible light photocatalytic activity, while pristine TiO2 is only UV responsive. Ti3+ has been demonstrated to be responsible for this improvement. We systematically studied various experimental parameters, such as the amount of reducing agent imidazole, types of imidazoles and Ti sources, and determined effects of these parameters on the combustion process and final materials. The phase composition, Ti3+ concentration, light absorption, surface area, and crystallinity of the product are significantly affected by the amount of imidazoles. Through comparing different imidazoles, we found that only flammable/combustible imidazoles are able to convert Ti4+ into Ti3+. This result is very helpful in understanding the mechanism and reactions in combustion process. Titanium precursors also have a great influence in production of Ti3+ doped TiO2 materials. Titanium alkoxides allow the successful synthesis of blue partially reduced TiO2, while TiCl4 only lead to white pristine TiO2. [Copyright &y& Elsevier]

Hitting water with nanotrees: A three‐dimensional branched heterojunction array for water oxidation based on cobalt‐doped α‐Fe2O3 nanorod cores and MgFe2O4 shells has been synthesized. Such an architecture not only has a large surface area and excellent light absorption properties, and shows fast charge transport, but also exhibits a very high photoelectrochemical activity for water oxidation and a high stability. [ABSTRACT FROM AUTHOR]

In urban traffic management, the primary challenge of dynamically and efficiently monitoring traffic conditions is compounded by the insufficient utilization of thousands of surveillance cameras along the intelligent transportation system. This paper introduces the multi-level Traffic-responsive Tilt Camera surveillance system (TTC-X), a novel framework designed for dynamic and efficient monitoring and management of traffic in urban networks. By leveraging widely deployed pan–tilt-cameras (PTCs), TTC-X overcomes the limitations of a fixed field of view in traditional surveillance systems by providing mobilized and 360-degree coverage. The innovation of TTC-X lies in the integration of advanced machine learning modules, including a detector–predictor–controller structure, with a novel Predictive Correlated Online Learning (PiCOL) methodology and the Spatial–Temporal Graph Predictor (STGP) for real-time traffic estimation and PTC control. The TTC-X is tested and evaluated under three experimental scenarios (e.g., maximum traffic flow capture, dynamic route planning, traffic state estimation) based on a simulation environment calibrated using real-world traffic data in Brooklyn, New York. The experimental results showed that TTC-X captured over 60% total number of vehicles at the network level, dynamically adjusted its route recommendation in reaction to unexpected full-lane closure events, and reconstructed link-level traffic states with best MAE less than 1.25 vehicle/hour. Demonstrating scalability, cost-efficiency, and adaptability, TTC-X emerges as a powerful solution for urban traffic management in both cyber–physical and real-world environments. • This study proposes a traffic-responsive tilt camera system to dynamically monitor traffic. • A Predictive Correlated Online Learning for real-time traffic data decisions. • A Spatial-temporal learning system integrates road structure and traffic dynamics. • Three scenarios are tested for max flow capture, route planning, and traffic state estimation. [ABSTRACT FROM AUTHOR]

• The B-site cations in perovskite is manipulated to facilitate water splitting. • The decoration of CoS 2 and oxygen deficiency enhance the catalytic activity. • The evolution of reactive sites was studied by experiments and DFT calculation. The family of perovskite oxides is a promising class of catalysts for diverse energy conversion processes including water splitting. In this work, a facile two-step manipulation (in-situ exsolution and post-sulfurization) strategy was proposed and applied to LaCo 0.2 Fe 0.8 O 3 (LCF) perovskite parent, through which, the electronic state, spatial immersion and intrinsic activity of B-site cobalt (Co) were stepwise tuned at nanoscale proximity accordingly (i.e., lattice Co ions → segregated Co0 → embedded CoS 2). Impressively, the as-prepared catalyst (S-LCF) obtains an emergent oxygen deficient microstructure seamlessly pinned with uniformly distributed CoS 2 nanoparticles (NPs), which demonstrates enhanced performance toward both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), and shows good stability in overall water splitting. The density functional theory (DFT) calculations illustrate the optimized metal-oxygen covalency and hydrogen adsorption Gibbs free energy (ΔG H*) on S-LCF, which further buttresses the prominence of our B-site cation engineering tactics. [ABSTRACT FROM AUTHOR]

• Operational and safety measures can be calibrated simultaneously in microsimulation. • Severity distributions of TTC encode rich information for safety calibration. • Aerial imagery can be good data source for traffic conflict extraction and safety calibration. • Driver behaviors under a CV environment demand for more rigorous calibration efforts. Proper calibration process is of considerable importance for traffic safety evaluations using simulation models. Allowing for a pure with and without comparison under identical circumstances that is not directly testable in the field, microsimulation-based approach has drawn considerable attention for the performance evaluation of emerging technologies, such as connected vehicle (CV) safety applications. Different from the traditional approaches to evaluate mobility impacts, safety evaluations of such applications demand the simulation models to be well calibrated to match real-world safety conditions. This paper proposes a novel calibration framework which combines traffic conflict techniques and multi-objective stochastic optimization so that the operational and safety measures can be calibrated simultaneously. The conflict distribution of different severity levels categorized by time-to-collision (TTC) is applied as the safety performance measure. Simultaneous perturbation stochastic approximation (SPSA) algorithm, which can efficiently approximate the gradient of the multi-objective stochastic loss function, is used for model parameters optimization that minimizes the total simulation error of both operational and safety performance measures. The proposed calibration methodology is implemented using an open-source software SUMO on a simulation network of the Flatbush Avenue corridor in Brooklyn, NY. 17 key parameters are calibrated using the SPSA algorithm and are compared with the real-world traffic conflicts extracted using vehicle trajectories from 14 h' high-resolution aerial and traffic surveillance videos. Representative days are identified to create variation envelopes for performance measures. Four acceptability criteria, including control for time-variant outliers and inliers, bounded dynamic absolute and system errors are adopted for results analysis. The results show that the calibrated parameters can significantly improve the performance of the simulation model to represent real-world safety conditions (i.e., traffic conflicts) as well as operational conditions. The case study also demonstrates the usefulness of aerial imagery and the applicability of the proposed model calibration framework, so the calibrated model can be used to evaluate the safety benefits of CV applications more accurately. [ABSTRACT FROM AUTHOR]

• Pore structure was studied to analyse the defects in 3DPC at a meso level. • Relationship between pore structure and compressive strength of 3DPC was analysed. • Ultimate compressive strength of 3DPC and its damage mechanism were analysed. • Three-dimensional stress field distribution at the 3DPC interface was predicted. 3D printed concrete (3DPC) technology is one of the fastest-growing technologies in the construction industry. In this study, the pore structure of 3DPC and its parameters were investigated. The variation patterns of compressive strength, splitting tensile strength, and flexural strength of 3DPC were studied with the increase of curing time in different loading directions. Characterisation of the meso -destructive mechanism and ultimate strength of 3DPC by X-ray computed tomography (X-CT) and the digital image correlation (DIC) techniques is conducted. The results showed that the internal pore structure of 3DPC was different from that of cast concrete, showing irregular shapes and the pores were prone to stress concentration, resulting in the mechanical strength of 3DPC being generally lower than that of cast specimens. Moreover, the mechanical strength of 3DPC showed significant anisotropy. 3DPC showed variability in the relationship between its pore structure and strength when compressed in the X-, Y- and Z-directions. The ultimate compressive strength of 3DPC and its damage mechanism were analysed based on the parallel pore connection model. The three-dimensional stress field distribution at the 3DPC interface was predicted when the cracks extend along the direction parallel or perpendicular to the interface. [ABSTRACT FROM AUTHOR]

Hardware-in-the-loop (HIL) is a testing paradigm where physical sensors (e.g., monitoring sensors such as cameras and proximity sensors, and alarm sensors such as screaming traffic cones) are connected to a virtual test system that simulates reality (e.g., virtual work zone with simulated dangerous situations). This paradigm is well-suited for conducting user studies for work zone safety because virtual test systems can be implemented using virtual reality (VR), which allows for safe and realistic testing of a sensing system without putting workers in danger along with avoiding high upfront costs needed to generate physical research testbeds. However, when recreating physical work zones in VR, researchers face various challenges while representing traffic patterns in VR, such as the lack of bi-directional communication between traffic simulation platforms and user behaviors in VR, hardware compatibility and integration issues, and customization inflexibility during implementation. Researchers, who need to develop such platforms for research studies that involve high-risk exposure to participants, are in need of evaluating the options available for bringing together the components of such platforms. This study provides an overview of alternative ways to integrate components of platforms that enable hardware-in-the-loop for synchronous VR, traffic simulation, and sensor interactions to position researchers to make decisions based on the pros and cons of each alternative. This paper also presents the implementation of such an integrated platform that allows a two-way interface between traffic simulation and VR environments for work zone safety analysis. Outcomes of this work will lay out the steps in implementing the integrated and immersive platform to be used in work zone safety studies based on the guidance presented. [ABSTRACT FROM AUTHOR]

• A functional approach is used to analyze safety risk of signalized intersections. • Safety risk within signal cycles is modeled as time series functions. • Traffic conflicts are extracted automatically from drone-recorded videos. • New safety risk patterns at signalized intersections are identified. • Aggregation of safety risk may result in information loss. • Applications of the proposed functional approach in traffic safety are discussed. Safety evaluation of signalized intersections is often conducted by developing statistical and data-driven methods based on data aggregated at certain temporal and spatial levels (e.g., yearly, hourly, or per signal cycle; intersection or approach leg). However, such aggregations are subject to a major simplification that masks the underlying spatio-temporal safety risk patterns within the data aggregation levels. Consequently, high-resolution analysis such as safety risk within signal cycles and at traffic movement level cannot be performed. This study contributes to the literature by proposing a new functional data analysis (FDA) approach for a novel characterization of safety risk patterns of signalized intersections. Functional data smoothing methods that can mitigate overfitting and account for the nonnegative characteristics of safety risk are proposed to model the time series of safety risk within signal cycles at the traffic movement level. Functional analysis of variance method (FANOVA) that can compare the group level differences of functional curves is used to test differences of safety risk functions among different traffic movements. A typical signalized intersection with representative signal types and channelizations is selected as the study location and approximately 1-hour traffic video data recorded by an unmanned aerial vehicle are used to extract traffic conflicts. New movement-level safety risk patterns are characterized based on the safety risk functions that can reveal the temporal distribution of risk within signal cycles. Most of the tested traffic movements have significantly distinct functional risk patterns according to the FANOVA results while risk patterns for most of the traffic movements cannot be differentiated based on the data aggregated at the cycle and approach levels. The proposed functional approach has the potential to be used for facilitating proactive safety management, calibrating microsimulation models for safety evaluation, and optimizing signal timing while considering traffic safety at more disaggregated levels. [ABSTRACT FROM AUTHOR]

• A novel functional approach is proposed for detecting safety-related anomalies. • General rules for identifying safety-related anomalies are defined. • Nine commonly used functional anomaly detection methods are compared. • Vehicle trajectories are automatically extracted from video recorded by a drone. • Good separation between anomalies and non-anomalies has been found. The advent of smart cities has motivated the field of transportation safety to transition towards a proactive approach that anticipates and mitigates risks before crashes occur. One of the stepping-stones to complete this transition is to monitor the risk levels of the transportation roadway system continuously. With the advancement in emerging technologies, computer-vision-based safety analysis approaches that focus on risk assessment using traffic conflicts quantified by surrogate safety measures extracted from traffic videos have been proposed. As the detailed movements of all the vehicles are logged in the video data, the quantified traffic conflicts can depict the whole picture of risk levels for a specific location. However, the capability of using traffic videos and the quantified traffic conflicts for proactive safety monitoring has yet to be investigated widely. In addition, one of the limitations of past safety research is that safety-related data are mostly aggregated into summary statistics (such as the total number of crashes/conflicts), which constitutes a major simplification of the real risk levels that change over time. Therefore, this study contributes to the literature by proposing a novel functional approach that models time series of a variant of the time exposed time-to-collision safety indicator directly to detect safety-related anomalies from traffic video data. Functional data smoothing methods that can mitigate overfitting and account for the nonnegative characteristics of safety indicator are applied. Nine commonly used functional anomaly detection methods are summarized and compared using approximately one-hour of traffic video data recorded by an unmanned aerial vehicle at a signalized intersection. Ground truth safety-related anomalies are identified by manually reviewing the video data, which is used to validate the performances of anomaly detection methods. Good separation between safety-related anomalies and non-anomalies (0.85 area under the receiver operating characteristics and 0.8 area under the precision-recall curve) has been found. By applying the proposed safety-related anomaly detection method, significant amount of human resources can be liberated. [ABSTRACT FROM AUTHOR]

Understanding drivers' reactions to in-vehicle forward collision warnings (FCWs) is vital for advancing FCW design and improving road safety. However, past studies often used aggregated safety measures to analyze the drivers' reactions to FCWs, thereby at the microscopic level, limiting our ability to understand drivers' reactions to FCWs at particular timestamps immediately after FCWs are issued. Additionally, there has been a notable absence of studies at the macroscopic perspective focusing on analyzing how drivers' reactions to FCWs evolve over an extended period of time. To overcome these two limitations, this study proposes a new research framework using Functional data analysis (FDA) approach to model driver behavior profile in response to FCWs at both microscopic and macroscopic longitudinal levels. Real-world FCW data collected from the New York City Connected Vehicle Pilot Deployment project is used for the case study. At the microscopic level, a sparse functional design is adopted to model driver behavior profiles, accounting for irregularly spaced functional measurements. Nonparametric functional linear regression is then used to estimate the drivers' reactions to FCWs at a particular timestamp immediately after FCWs are issued. At the macroscopic level, the functional two-sample test and a functional distance metric are used to examine changes in drivers' reactions to FCWs over the study period and quantify the magnitude of these changes. Time to collision (TTC) and modified time to collision (MTTC) measures are used to represent driver behavior profiles, and both TTC and MTTC after FCWs are issued are modeled as functions with respect to time based on the proposed FDA approach. Compared to using aggregated safety measures including minimum TTC and MTTC as well as mean TTC and MTTC, new patterns of drivers' reactions to FCWs are unveiled at both microscopic and macroscopic longitudinal levels. Study outputs reveal several key insights, including driver compensation behavior that escalates safety risk after an initial safety improvement and the diminishing safety benefits of FCWs from the beginning to the end of the after period. The proposed research framework can be generalized to analyze various types of in-vehicle driver warnings at both microscopic and macroscopic longitudinal levels. The findings of this study can support the calibration of detailed driver response behavior to in-vehicle warnings and facilitate the design of driver warning applications and further investigation of their safety benefits., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The COVID-19 pandemic has greatly impacted lifestyles and travel patterns, revealing existing societal and transportation gaps and introducing new challenges. In the context of an aging population, this study investigated how the travel behaviors of older adults (aged 60+) in New York City were affected by COVID-19, using an online survey and analyzing younger adult (aged 18-59) data for comparative analysis. The purpose of the study is to understand the pandemic's effects on older adults' travel purpose and frequency, challenges faced during essential trips, and to identify potential policies to enhance their mobility during future crises. Descriptive analysis and Wilcoxon signed-rank tests were used to summarize the changes in employment status, trip purposes, transportation mode usage, and attitude regarding transportation systems before and during the outbreak and after the travel restrictions were lifted. A Natural Language Processing model, Gibbs Sampling Dirichlet Multinomial Mixture, was adopted to open-ended questions due to its advantage in extracting information from short text. The findings show differences between older and younger adults in telework and increased essential-purpose trips (e.g., medical visits) for older adults. The pandemic increased older adults' concern about health, safety, comfort, prices when choosing travel mode, leading to reduced transit use and walking, increased driving, and limited bike use. To reduce travel burdens and maintain older adults' employment, targeted programs improving digital skills (telework, telehealth, telemedicine) are recommended. Additionally, safe, affordable, and accessible transportation alternatives are necessary to ensure mobility and essential trips for older adults, along with facilitation of walkable communities., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Introduction: The rapidly evolving COVID-19 pandemic has dramatically reshaped urban travel patterns. In this research, we explore the relationship between "social distancing," a concept that has gained worldwide familiarity, and urban mobility during the pandemic. Understanding social distancing behavior will allow urban planners and engineers to better understand the new norm of urban mobility amid the pandemic, and what patterns might hold for individual mobility post-pandemic or in the event of a future pandemic., Methods: There are still few efforts to obtain precise information on social distancing patterns of pedestrians in urban environments. This is largely attributed to numerous burdens in safely deploying any effective field data collection approaches during the crisis. This paper aims to fill that gap by developing a data-driven analytical framework that leverages existing public video data sources and advanced computer vision techniques to monitor the evolution of social distancing patterns in urban areas. Specifically, the proposed framework develops a deep-learning approach with a pre-trained convolutional neural network to mine the massive amount of public video data captured in urban areas. Real-time traffic camera data collected in New York City (NYC) was used as a case study to demonstrate the feasibility and validity of using the proposed approach to analyze pedestrian social distancing patterns., Results: The results show that microscopic pedestrian social distancing patterns can be quantified by using a generalized real-distance approximation method. The estimated distance between individuals can be compared to social distancing guidelines to evaluate policy compliance and effectiveness during a pandemic. Quantifying social distancing adherence will provide decision-makers with a better understanding of prevailing social contact challenges. It also provides insights into the development of response strategies and plans for phased reopening for similar future scenarios., Competing Interests: Each of the authors confirms that this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. The named authors have no conflict of interest, financial or otherwise., (© 2021 Elsevier Ltd. All rights reserved.)

Objective: Obesity is associated with an increased risk of developing insulin resistance and type 2 diabetes, since insulin can induce adipogenic differentiation of human adipose-derived stem cells (ADSCs). MiR-26a was reported to be highly expressed in ADSCs under induction and Forkhead box C2 (FOXC2), as a key substrate of cyclin-dependent kinase 5 (CDK5) could inhibit white adipocyte differentiation, which was mediated by miR-26a. However, the relationship between miR-26a and CDK5/FOXC2 during ADSCs differentiation remains unknown. We want to verify the regulated mechanism of miR-26a/CDK5/FOXC2 axis participating in the adipogenic differentiation of ADSCS., Methods: ADSCs were isolated and verified by flow cytometry. Oil Red O staining was performed to assess the capacity for adipogenic differentiation of ADSCs. The proliferation ability of ADSCs was verified by MTT assay. The expression of miR-26a, peroxisome proliferator-activated receptors γ (PPARγ), CDK5, and FOXC2 were tested by qRT-PCR and Western blot, and the relationship between miR-26a and CDK5 was verified by dual-luciferase reporter gene assay., Results: MiR-26a and PPARγ were upregulated and CDK5 and FOXC2 were downregulated during adipogenic differentiation of ADSCs. Knockdown of miR-26a or overexpression of CDK5 could inhibit adipogenic differentiation of ADSCs induced by insulin. MiR-26a could directly target CDK5 and the effect of miR-26a inhibitor on adipogenic differentiation of ADSCs could be blocked by si-CDK5., Conclusion: We demonstrated that miR-26a regulated insulin-induced adipogenic differentiation of ADSCs by regulating CDK5/FOXC2 pathway, which could provide the key to a comprehensive mechanistic understanding of obesity and type 2 diabetes.

The unprecedented challenges caused by the COVID-19 pandemic demand timely action. However, due to the complex nature of policy making, a lag may exist between the time a problem is recognized and the time a policy has its impact on a system. To understand this lag and to expedite decision making, this study proposes a change point detection framework using likelihood ratio, regression structure and a Bayesian change point detection method. The objective is to quantify the time lag effect reflected in transportation systems when authorities take action in response to the COVID-19 pandemic. Using travel patterns as an indicator of policy effectiveness, the length of policy lag and magnitude of policy impacts on the road system, mass transit, and micromobility are investigated through the case studies of New York City (NYC), and Seattle-two U.S. cities significantly affected by COVID-19. The quantitative findings show that the National declaration of emergency had no policy lag while stay-at-home and reopening policies had a lead effect on mobility. The magnitude of impact largely depended on the land use and sociodemographic characteristics of the area, as well as the type of transportation system., (© 2021 Elsevier Ltd. All rights reserved.)

Self-doped TiO2 shows visible light photocatalytic activity, while commercial TiO2 (P25) is only UV responsive. The incorporation of Ti(3+) into TiO2 structures narrows the band gap (2.90 eV), leading to significantly increased photocatalytic activity for the reduction of CO2 into a renewable hydrocarbon fuel (CH4) in the presence of water vapour under visible light irradiation.

A new synthetic method to fabricate Ti(3+)-modified, highly stable TiO2 photoanodes for H2O oxidation is reported. With Ti foil as both the conducting substrate and the Ti(3+)/Ti(4+) source, one-dimensional blue Ti(3+)/TiO2 crystals were grown by a one-step hydrothermal reaction. The concentration of Ti(3+) was further tuned by N2H4 reduction, leading to a greater photoelectrocatalytic activity, as evidenced by a high photocurrent density of 0.64 mA cm(-2) at 1.0 V vs RHE under simulated AM 1.5 G illumination. Electron paramagnetic resonance and Mott-Schottky plots reveal that higher charge-carrier density owing to N2H4 reduction contributes to the observed improvement. The generality of this synthesis method was demonstrated by its effectiveness in improving the performance of other types of photoanodes. By integrating the advantages of the 1D TiO2 architecture with those of Ti(3+) self-doping, this work provides a versatile tool toward the fabrication of efficient TiO2 photoanodes., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A hybrid WO3 /C3 N4 /CoOx system exhibits excellent photoelectrochemical activity for water oxidation. The system comprises a novel three-dimensionally branched WO3 nanosheet array coated with a layer of C3 N4 heterojunctions that are further decorated with CoOx nanoparticles. The photoelectrochemical activity arises from the effective light harvesting due to the 3D structure and "window effect," the excellent charge separation and transport in the heterojunction, and the fast interfacial charge collection and surface reactions due to the large surface area., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We report the design, synthesis, and characterization of a novel heterojunction array of α-Fe(2)O(3)/graphene/BiV(1-x)Mo(x)O(4) core/shell nanorod for photoelectrochemical water splitting. The heterojunction array was prepared by hydrothermal deposition of α-Fe(2)O(3) nanorods onto Ti substrate, with subsequent coating of graphene interlayer and BiV(1-x)Mo(x)O(4) shell by photocatalytic reduction and a spin-coating approach, respectively. The heterojunction yielded a pronounced photocurrent density of ∼1.97 mA/cm(2) at 1.0 V vs Ag/AgCl and a high photoconversion efficiency of ∼0.53% at -0.04 V vs Ag/AgCl under the irradiation of a Xe lamp. The improved photoelectrochemical properties benefited from (1) the enhanced light absorption due to behavior of the "window effect" between the α-Fe(2)O(3) cores and BiV(1-x)Mo(x)O(4) shells, and (2) the improved separation of photogenerated carriers at the α-Fe(2)O(3) nanorod/graphene/BiV(1-x)Mo(x)O(4) interfaces. Our results demonstrate the advantages of the novel graphene-mediated core/shell heterojunction array and provide a valuable insight for the further development of such materials.