Spatiotemporal Resolution Research Articles

Exploring the effects of climate change and urban policies on lake water quality using remote sensing and explainable artificial intelligence

Climate change and urban policies significantly impact lake water quality, especially in rapidly developing regions. Existing impact assessments are limited by the duration and resolution of water quality data, while long-term impacts and contributions of climate and socioeconomic drivers have rarely been quantified. In this study, we utilized remote sensing imagery to obtain high spatiotemporal resolution data and Explainable Artificial Intelligence (XAI) to reveal how the comprehensive impacts vary over time. We obtained Trophic State Index (TSI) data between 2000 and 2022 from remote sensing imagery. XAI was employed to elucidate the intricate connections between TSI and the multifaceted factors that encompass climate and socioeconomic dynamics. Six typical lakes in East China were selected for this study. Results showed that the TSI of these lakes had generally decreased over the past two decades, being typically higher in the littoral zones compared to the center. Moreover, higher temperatures increased lake TSI. Meanwhile, higher rainfall increased TSI through increased lake nutrient inputs from surrounding watersheds where agricultural and industrial activities are intensive. It was also found that economic development impacts on lake TSI shifted around 2013. Economic development caused severe negative effects on lake water quality before 2013, while this phenomenon decreased thereafter. This is attributed to the implementation of a series of green transformation policies in the region. This study demonstrates the long-term contribution of climate change and urban policies in driving lake water quality variations, and provides implications for policy makers to establish positive economic, social, and environmental linkages.

Variation of surface solar radiation components from 2016 to 2020 in China: Perspective from geostationary satellite observation with a high spatiotemporal resolution

At present, traditional satellite datasets still grapple with inadequacies in terms of capturing solar radiation with fine spatiotemporal granularity. This study utilizes the high spatiotemporal resolution of CARE data, which is developed based on geostationary satellite observations, and employs multivariate analysis techniques to conduct an in-depth investigation into the multidimensional spatiotemporal variations of different types of solar radiation across various regions in China from 2016 to 2020. In addition, the potential of solar energy resources was also assessed using cluster analysis method. The results revealed an upward trend in different components of solar radiation across most of China, with shortwave radiation exhibiting a significantly negative correlation with PM2.5 concentrations (R = −0.91, p < 0.05). This finding suggests that the increase in SWR may be attributed to the effective implementation of China's Air Pollution Prevention and Control Action Plan. It suggests that China's endeavors to mitigate air pollution have not only resulted in improvements in national air quality but have also had an indirect positive effect on enhancing the potential for photovoltaic power generation. The assessment of solar energy resources potential indicated, with 99 % statistical confidence, that western China constitutes the core region for solar energy resources development, whereas northeastern and southeastern regions face certain constraints in solar energy resources utilization. Furthermore, we examined the specific influence of atmospheric circulation patterns on solar energy resources, uncovering that the El Niño phenomenon, by altering cloud distribution and variability, indirectly affects solar radiation intensity in specific regions. This study aids in understanding China's solar radiation variations, crucial for shaping effective energy policies towards carbon neutrality.

Whole-Head Noninvasive Brain Signal Measurement System with High Temporal and Spatial Resolution Using Static Magnetic Field Bias to the Brain.

Noninvasive brain signal measurement techniques are crucial for understanding human brain function and brain-machine interface applications. Conventionally, noninvasive brain signal measurement techniques, such as electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and near-infrared spectroscopy, have been developed. However, currently, there is no practical noninvasive technique to measure brain function with high temporal and spatial resolution using one instrument. We developed a novel noninvasive brain signal measurement technique with high temporal and spatial resolution by biasing a static magnetic field emitted from a coil on the head to the brain. In this study, we applied this technique to develop a groundbreaking system for noninvasive whole-head brain function measurement with high spatiotemporal resolution across the entire head. We validated this system by measuring movement-related brain signals evoked by a right index finger extension movement and demonstrated that the proposed system can measure the dynamic activity of brain regions involved in finger movement with high spatiotemporal accuracy over the whole brain.

Extraction of Molecular-Frame Electron-Ion Differential Scattering Cross Sections Based on Elliptical Laser-Induced Electron Diffraction.

We extracted the molecular-frame elastic differential cross sections (MFDCSs) for electrons scattering from N_{2}^{+} based on elliptical laser-induced electron diffraction (ELIED), wherein the structural evolution is initialized by the same tunneling ionization and probed by incident angle-resolved laser-induced electron diffraction imaging. To establish ELIED, an intuitive interpretation of the ellipticity-dependent rescattering electron momentum distributions was first provided by analyzing the transverse momentum distribution. It was shown that the incident angle of the laser-induced returning electrons could be tuned within 20° by varying the ellipticity and handedness of the driving laser pulses. Accordingly, the incident angle-resolved DCSs of returning electrons for spherically symmetric targets (Xe^{+} and Ar^{+}) were successfully extracted as a proof-of-principle for ELIED. The MFDCSs for N_{2}^{+} were experimentally obtained at incident angles of 4° and 7°, which were well reproduced by the simulations. The ELIED approach is the only successful method so far for obtaining incident angle-resolved ionic MFDCS, which provides a new sensitive observable for the transient structure retrieval of N_{2}^{+}. Our results suggest that the ELIED has the potential to extract the structural tomographic information of polyatomic molecules with femtosecond and subangstrom spatiotemporal resolutions that can enable the visualization of the nuclear motions in complex chemical reactions as well as chiral recognition.

Ultra-fast Digital DPC Yielding High Spatio-temporal Resolution for Low-Dose Phase Characterization.

In the scanning transmission electron microscope, both phase imaging of beam-sensitive materials and characterization of a material's functional properties using in situ experiments are becoming more widely available. As the practicable scan speed of 4D-STEM detectors improves, so too does the temporal resolution achievable for both differential phase contrast (DPC) and ptychography. However, the read-out burden of pixelated detectors, and the size of the gigabyte to terabyte sized data sets, remain a challenge for both temporal resolution and their practical adoption. In this work, we combine ultra-fast scan coils and detector signal digitization to show that a high-fidelity DPC phase reconstruction can be achieved from an annular segmented detector. Unlike conventional analog data phase reconstructions from digitized DPC-segment images yield reliable data, even at the fastest scan speeds. Finally, dose fractionation by fast scanning and multi-framing allows for postprocess binning of frame streams to balance signal-to-noise ratio and temporal resolution for low-dose phase imaging for in situ experiments.

Large-scale data processing platform for laser absorption tomography

Abstract Laser absorption tomography (LAT) has been widely employed to capture two/three-dimensional reactive flow-field parameters with a penetrating spatiotemporal resolution. In industrial environments, LAT is generally implemented by measuring multiple, e.g. 30 to more than 100, wavelength modulated laser transmissions at high imaging rates, e.g. tens to thousands of frames per second (fps). A short-period LAT experiment can generate extensive load of data, which require massive computational source and time for data post-processing. In this work, a large-scale data processing platform is designed for industrial LAT. The platform significantly speeds up LAT signal processing by introducing a parallel computing architecture. By identifying the discrepancy between the measured and theoretical spectra, the new platform enables indexing of the laser-beam measurements that are disturbed by harsh-environment noise. Such a scheme facilitates effective removal of noise-distorted beams, which can lead to artefacts in the reconstructed images. The designed platform is validated by a lab-based LAT experiment, which is implemented by processing the laser transmissions of a 32-beam LAT sensor working at 250 fps. To process a 60-second LAT experimental dataset, the parallelism enabled by the platform saves computational time by 40.12% compared to the traditional single-thread approach. The error-detection scheme enables the successful accurate identification of noise-distorted measurements, i.e. 0.59% of overall laser-beam measurements that fall out of the physical model.

Analyzing Fusion Pore Dynamics and Counting the Number of Acetylcholine Molecules Released by Exocytosis.

Acetylcholine (ACh) is a critical neurotransmitter influencing various neurophysiological functions. Despite its significance, quantitative methods with adequate spatiotemporal resolution for recording a single exocytotic ACh efflux are lacking. In this study, we introduce an ultrafast amperometric ACh biosensor that enables 50 kHz electrochemical recording of spontaneous single exocytosis events at axon terminals of differentiated cholinergic human SH-SY5Y neuroblastoma cells with sub-millisecond temporal resolution. Characterization of the recorded amperometric traces revealed seven distinct current spike types, each displaying variations in shape, time scale, and ACh quantities released. This finding suggests that exocytotic release is governed by complex fusion pore dynamics in these cells. The absolute number of ACh molecules released during exocytosis was quantified by calibrating the sensor through the electroanalysis of liposomes preloaded with varying ACh concentrations. Notably, the largest quantal release involving approximately 8000 ACh molecules likely represents full exocytosis, while a smaller release of 5000 ACh molecules may indicate partial exocytosis. Following a local administration of bafilomycin A1, a V-ATPase inhibitor, the cholinergic cells exhibited both a larger quantity of ACh released and a higher frequency of exocytosis events. Therefore, this ACh sensor provides a means to monitor minute amounts of ACh and investigate regulatory release mechanisms at the single-cell level, which is vital for understanding healthy brain function and pathologies and optimizing drug treatment for disorders.

Estimation of daily XCO2 at 1 km resolution in China using a spatiotemporal ResNet model

Carbon dioxide (CO2) serves as a crucial greenhouse gas that traps heat and regulates the Earth's temperature. High spatiotemporal resolution CO2 estimation can provide valuable information to understand the characteristics of fine-scale climate change trends and to formulate more effective emission reduction strategies. This study presents a spatiotemporal ResNet model (ST-ResNet) specifically developed to estimate the highest resolution (1 km × 1 km) daily column-averaged dry-air mole fraction of CO2 (XCO2) in China from 2015 to 2020. The ST-ResNet model excels in estimating XCO2 by comprehensively considering the complex relationships between XCO2 and its various influencing factors, while efficiently capturing both temporal and spatial correlations, thereby demonstrating remarkable generalization capability. The results show that the ST-ResNet generates a highly accurate XCO2 dataset, outperforming the traditional ResNet. Ground-based validation results further confirm the high accuracy and spatiotemporal resolution of our estimated data product. Using this dataset, the spatial and temporal characteristics of XCO2 across the entire China and several urban agglomerations have been analyzed. The high spatiotemporal resolution estimated XCO2 dataset for China is made publicly available at [https://doi.org/10.6084/m9.figshare.25272868], offering substantial potential for fine-scale carbon research.

Deep intravital brain tumor imaging enabled by tailored three-photon microscopy and analysis

Intravital 2P-microscopy enables the longitudinal study of brain tumor biology in superficial mouse cortex layers. Intravital microscopy of the white matter, an important route of glioblastoma invasion and recurrence, has not been feasible, due to low signal-to-noise ratios and insufficient spatiotemporal resolution. Here, we present an intravital microscopy and artificial intelligence-based analysis workflow (Deep3P) that enables longitudinal deep imaging of glioblastoma up to a depth of 1.2 mm. We find that perivascular invasion is the preferred invasion route into the corpus callosum and uncover two vascular mechanisms of glioblastoma migration in the white matter. Furthermore, we observe morphological changes after white matter infiltration, a potential basis of an imaging biomarker during early glioblastoma colonization. Taken together, Deep3P allows for a non-invasive intravital investigation of brain tumor biology and its tumor microenvironment at subcortical depths explored, opening up opportunities for studying the neuroscience of brain tumors and other model systems.

Inflammation-free electrochemical in vivo sensing of dopamine with atomic-level engineered antioxidative single-atom catalyst

Electrochemical methods with tissue-implantable microelectrodes provide an excellent platform for real-time monitoring the neurochemical dynamics in vivo due to their superior spatiotemporal resolution and high selectivity and sensitivity. Nevertheless, electrode implantation inevitably damages the brain tissue, upregulates reactive oxygen species level, and triggers neuroinflammatory response, resulting in unreliable quantification of neurochemical events. Herein, we report a multifunctional sensing platform for inflammation-free in vivo analysis with atomic-level engineered Fe single-atom catalyst that functions as both single-atom nanozyme with antioxidative activity and electrode material for dopamine oxidation. Through high-temperature pyrolysis and catalytic performance screening, we fabricate a series of Fe single-atom nanozymes with different coordination configurations and find that the Fe single-atom nanozyme with FeN4 exhibits the highest activity toward mimicking catalase and superoxide dismutase as well as eliminating hydroxyl radical, while also featuring high electrode reactivity toward dopamine oxidation. These dual functions endow the single-atom nanozyme-based sensor with anti-inflammatory capabilities, enabling accurate dopamine sensing in living male rat brain. This study provides an avenue for designing inflammation-free electrochemical sensing platforms with atomic-precision engineered single-atom catalysts.

Exploring spatiotemporal dynamics, seasonality, and time-of-day trends of PM2.5 pollution with a low-cost sensor network: Insights from classic and spatially explicit Markov chains

Fine particulate matter (PM2.5) is a major health and environmental concern, with significant spatiotemporal dynamics in urban areas. Low-cost air quality sensor (LCS) networks offer a paradigm-changing opportunity to acquire high spatiotemporal resolution data, revealing the urban pollution landscape with sufficient detail for effective policymaking and health assessment. This study advances geospatial air quality research by using classic and spatial Markov chains to analyze the seasonality and intra-daily variations of PM2.5 using LCS data. Results highlight distinctive PM2.5 seasonality, with the “Good” state predominating in summer and being least common in winter. Midday is the peak period for the “Good” state, while mornings and nights have poorer conditions, suggesting a need for stricter pollution control during evening traffic rush hours. Notably, the impact of temporal scale on spatial Markov analysis is substantial, showing a broader range of air pollution states, increased stability, and reduced variation between time intervals compared to daily assessments. Site-level analysis reveals that rural sites are more likely to maintain “Good” state and less likely to transition out of it. Overall, this study highlights the effectiveness of high spatiotemporal resolution data and demonstrates the capacity of Markov chains to reveal nuances in phenomena such as air pollution.

A Live-Cell Epigenome Manipulation by Photo-Stimuli-Responsive Histone Methyltransferase Inhibitor.

Post-translational modifications on the histone H3 tail regulate chromatin structure, impact epigenetics, and hence the gene expressions. Current chemical modulation tools, such as unnatural amino acid incorporation, protein splicing, and sortase-based editing, have allowed for the modification of histones with various PTMs in cellular contexts, but are not applicable for editing native chromatin. The use of small organic molecules to manipulate histone-modifying enzymes alters endogenous histone PTMs but lacks precise temporal and spatial control. To date, there has been no achievement in modulating histone methylation in living cells with spatiotemporal resolution. In this study, a new method is presented for temporally manipulating histone dimethylation H3K9me2 using a photo-responsive inhibitor that specifically targets the methyltransferase G9a on demand. The photo-caged molecule is stable under physiological conditions and cellular environments, but rapidly activated upon exposure to light, releasing the bioactive component that can immediately inhibit the catalytic ability of the G9a in vitro. Besides, this masked compound could also efficiently reactivate the inhibition of methyltransferase activity in living cells, subsequently suppress H3K9me2, a mark that regulates various chromatin functions. Therefore, the chemical system will be a valuable tool for manipulating the epigenome for therapeutic purposes and furthering the understanding of epigenetic mechanisms.

Investigating the relative accuracy of GPS, GSM and CDR data for inferring spatiotemporal travel trajectories

AbstractThe potential of passively generated big data sources in transport modelling is well‐recognised. However, assessing their accuracy and suitability for policymaking remains challenging due to the lack of ground‐truth (GT) data for validation. This study evaluates the accuracy of inferring human mobility patterns from global positioning system (GPS), call detail records (CDR), and global system for mobile communication (GSM) data. Using outputs from an agent‐based simulation platform (MATSim) as ‘synthetic GT’ (SGT), synthetic GPS, CDR, and GSM data were generated, considering their positional disturbances and conventional spatiotemporal resolutions. Mobility information, including activity location, departure time, and trajectory distance, derived from the synthetic data, was compared with SGT to evaluate the accuracy of passive trajectory data at both disaggregate and aggregate levels. The results indicated a higher accuracy of GPS data in identifying stay locations at high resolution. But, GSM data at a lower resolution effectively accounted for over 80% of the variability in stay locations. Comparisons of departure time distribution and travel distance revealed higher measurement errors in GSM and CDR data than in GPS data. The proposed simulation‐based accuracy assessment framework will aid transport planners select the most suitable data for specific analyses and understand the potential margin of error involved.

Multiplexed Microfluidic Platform for Parallel Bacterial Chemotaxis Assays.

The sensing of and response to ambient chemical gradients by microorganisms via chemotaxis regulates many microbial processes fundamental to ecosystem function, human health, and disease. Microfluidics has emerged as an indispensable tool for the study of microbial chemotaxis, enabling precise, robust, and reproducible control of spatiotemporal chemical conditions. Previous techniques include combining laminar flow patterning and stop-flow diffusion to produce quasi-steady chemical gradients to directly probe single-cell responses or loading micro-wells to entice and ensnare chemotactic bacteria in quasi-steady chemical conditions. Such microfluidic approaches exemplify a trade-off between high spatiotemporal resolution of cell behavior and high-throughput screening of concentration-specific chemotactic responses. However, both aspects are necessary to disentangle how a diverse range of chemical compounds and concentrations mediate microbial processes such as nutrient uptake, reproduction, and chemorepulsion from toxins. Here, we present a protocol for the multiplexed chemotaxis device (MCD), a parallelized microfluidic platform for efficient, high-throughput, and high-resolution chemotaxis screening of swimming microbes across a range of chemical concentrations. The first layer of the two-layer polydimethylsiloxane (PDMS) device comprises a serial dilution network designed to produce five logarithmically diluted chemostimulus concentrations plus a control from a single chemical solution input. Laminar flow in the second device layer brings a cell suspension and buffer solution into contact with the chemostimuli solutions in each of six separate chemotaxis assays, in which microbial responses are imaged simultaneously over time. The MCD is produced via standard photography and soft lithography techniques and provides robust,repeatable chemostimulus concentrations across each assay in the device. This microfluidic platform provides a chemotaxis assay that blends high-throughput screening approaches with single-cell resolution to achieve a more comprehensive understanding of chemotaxis-mediated microbial processes. Key features • Microchannel master molds are fabricated using photolithography techniques in a clean room with a mask aligner to fabricate multilevel feature heights. • The microfluidic device is fabricated from PDMS using standard soft lithography replica molding from the master molds. • The resulting microchannel requires a one-time calibration of the driving inlet pressures, after which devices from the same master molds have robust performance. • The microfluidic platform is optimized and tested for measuring chemotaxis of swimming prokaryotes.

DropletMask: Leveraging visual data for droplet impact analysis

AbstractMachine learning‐assisted computer vision represents a state‐of‐the‐art technique for extracting meaningful features from visual data autonomously. This approach facilitates the quantitative analysis of images, enabling object detection and tracking. In this study, we utilize advanced computer vision to precisely identify droplet motions and quantify their impact forces with spatiotemporal resolution at the picoliter or millisecond scale. Droplets, captured by a high‐speed camera, are denoised through neuromorphic image processing. These processed images are employed to train convolutional neural networks, allowing the creation of segmented masks and bounding boxes around moving droplets. The trained networks further digitize time‐varying multi‐dimensional droplet features, such as droplet diameters, spreading and sliding motions, and corresponding impact forces. Our innovative method offers accurate measurement of small impact forces with a resolution of approximately 10 pico‐newtons for droplets in the micrometer range across various configurations with the time resolution at hundreds of microseconds.

Wavelet optical flow velocimetry of a scramjet combustor using high-speed frame-straddling focusing schlieren images

Scramjet combustors feature ultra-high-speed turbulent reacting flows with high temperatures and intense luminescence. Measurement of velocity fields under such extreme conditions presents great challenges. The present work demonstrated a seedless velocimetry approach using focusing schlieren images (FSIs) of high spatiotemporal resolution in a scramjet engine. Two fuel mass flow rates (Case1 and Case2) were investigated with corresponding global equivalence ratios of 0.27 and 0.13, respectively. The FSIs enabled by the employment of a high-speed pulsed LED light source are characterized by an effective exposure of 100 ns, and a 500-ns frame-straddling time interval with full resolution of 1280 × 800 pixels recorded at 76 kHz. The 100-ns exposure allows for capturing of transient high-speed flow motion without blurring, and the 500-ns time interval ensures an appropriate spatiotemporal correlation between subsequent schlieren images for high-speed reacting flows. A wavelet-based optical flow velocimetry (wOFV) algorithm was developed and applied to the FSIs. In contrast to the correlation-based algorithms widely employed in PIV for distinct particles, the wOFV algorithm suits better FSIs with continuous variation in brightness. The maximal velocities in the main duct of the scramjet combustor were measured to be approximately 550 m s-1 and 1100 m s-1 for Case 1 and Case2, suggestively corresponding to subsonic and supersonic combustion modes, respectively. The measured velocity inside the cavity is generally below 200 m s-1 for both cases. Recirculation regions and their dynamic motions inside the cavity were well resolved. In summary, the development of the novel velocimetry approach holds great potential for applications in extreme flow conditions. Novelty and Significance Statement: Present work demonstrates a seedless velocimetry approach based on high-speed frame-straddling focusing schlieren imaging coupled with the novel wavelet optical flow velocimetry (wOFV) algorithm. Velocity field measurement realized in a scramjet combustor with high spatiotemporal resolution (1280 × 800 pixels at 38 kHz) for the first time, showing great abilities to accommodate wide velocity range and to resolve dynamic flow characteristics with potentials for broader future applications.

Optimising 4D imaging of fast-oscillating structures using X-ray microtomography with retrospective gating

Imaging the internal architecture of fast-vibrating structures at micrometer scale and kilohertz frequencies poses great challenges for numerous applications, including the study of biological oscillators, mechanical testing of materials, and process engineering. Over the past decade, X-ray microtomography with retrospective gating has shown very promising advances in meeting these challenges. However, breakthroughs are still expected in acquisition and reconstruction procedures to keep improving the spatiotemporal resolution, and study the mechanics of fast-vibrating multiscale structures. Thereby, this works aims to improve this imaging technique by minimising streaking and motion blur artefacts through the optimisation of experimental parameters. For that purpose, we have coupled a numerical approach relying on tomography simulation with vibrating particles with known and ideal 3D geometry (micro-spheres or fibres) with experimental campaigns. These were carried out on soft composites, imaged in synchrotron X-ray beamlines while oscillating up to 400 Hz, thanks to a custom-developed vibromechanical device. This approach yields homogeneous angular sampling of projections and gives reliable predictions of image quality degradation due to motion blur. By overcoming several technical and scientific barriers limiting the feasibility and reproducibility of such investigations, we provide guidelines to enhance gated-CT 4D imaging for the analysis of heterogeneous, high-frequency oscillating materials.

In‐Sensor Touch Analysis for Intent Recognition

AbstractTactile intent recognition systems, which are highly desired to satisfy human's needs and humanized services, shall be accurately understanding and identifying human's intent. They generally utilize time‐driven sensor arrays to achieve high spatiotemporal resolution, however, which encounter inevitable challenges of low scalability, huge data volumes, and complex processing. Here, an event‐driven intent recognition touch sensor (IR touch sensor) with in‐sensor computing capability is presented. The merit of event‐driven and in‐sensor computing enables the IR touch sensor to achieve ultrahigh resolution and obtain complete intent information with intrinsic concise data. It achieves critical signal extraction of action trajectories with a rapid response time of 0.4 ms and excellent durability of &gt;10 000 cycles, bringing an important breakthrough of tactile intent recognition. Versatile applications prove the integrated functions of the IR touch sensor for great interactive potential in all‐weather environments regardless of shading, dynamics, darkness, and noise. Unconscious and even hidden action features can be perfectly extracted with the ultrahigh recognition accuracy of 98.4% for intent recognition. The further auxiliary diagnostic test demonstrates the practicability of the IR touch sensor in telemedicine palpation and therapy. This groundbreaking integration of sensing, data reduction, and ultrahigh‐accuracy recognition will propel the leapfrog development for conscious machine intelligence.

High spatiotemporal resolution vegetation index time series can facilitate enhanced remote sensing monitoring of soil salinization

High spatiotemporal resolution vegetation index time series can facilitate enhanced remote sensing monitoring of soil salinization

Reaction-Driven Migration Dynamics of Nano-Metal Particles Unraveled by Quantitative Electron Microscopies.

The stability of supported nano-metal catalysts holds significant importance in both scientific and economic practice, beyond the long pursuit of enhanced activity. While previous efforts have concentrated on augmenting the interaction between nano-metals and carriers, in the thermodynamic macro-perspective, to achieve optimized repression upon particle migration coalescence and Ostwald ripening, nevertheless, the microscale kinetics of migrating catalyst particles driven by the reaction remains unknown. In this work, the migration of nano-copper particles is investigated during hydrogen oxidation reaction by utilizing high spatiotemporal resolution of environmental transmission electron microscopy. It is shown that there exists a delicate correlation between the migration dynamics of nano-copper particles and the evolution of asymmetrically distributed Cu and Cu2O phases over the particle surface. It is found that the interplay of reduction and oxidation near the surface areas filled with Cu and Cu2O phases can facilitate the pressure gradient, which drives the migration of nano-particles. A driving force model is therefore established which is capable of qualitatively explaining the influences of reaction conditions such as temperature and hydrogen-to-oxygen ratio on the reaction-driven particle migration. This work adds a potential yet critical perspective to understanding particle migration and thus the nano-metal catalyst particle sintering in heterogeneous catalysis.