High Spatial-temporal Resolution Research Articles

A High Temporal-Spatial Resolution Power System State Estimation Method for Online DSA

The rapidly increasing integration of renewable energy sources aggravates the uncertainty and fluctuation in modern power system, which promotes the development of online dynamic security assessment (DSA). Real-time acquisition of high resolution temporal-spatial information on the system states lays the foundation for online DSA, while limited PMU installation and complex dynamic characteristics in real systems impose severe challenges to estimate system states with high quality in real time. This paper proposes a high temporal-spatial resolution state estimation (SE) method, leveraging graph convolutional network (GCN) and dense connectivity structure to estimate states of whole system at PMU reporting rate. Based on proposed SE method, an online DSA framework is developed for transient stability assessment (TSA), which only relies on the hybrid measurements accessible to the control centers in practice. Numerical experiment results in different scenarios demonstrate that the proposed SE method exhibits high SE accuracy and efficiency under different PMU observability. The performance improvement of SE-based TSA approach versus raw-measurements-based TSA approaches is also verified both theoretically and experimentally.

Towards Groundwater-Level Prediction Using Prophet Forecasting Method by Exploiting a High-Resolution Hydrogeological Monitoring System

Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level prediction starting from measured meteorological data (i.e., precipitation and temperature) and observed groundwater levels, by exploiting data-driven approaches. Barely a few research combine the meteorological variables and groundwater level data with unsaturated zone monitored variables (i.e., soil water content, soil temperature, and bulk electric conductivity), and—in most of these—the vadose zone is monitored only at a single depth. Our approach exploits a high spatial-temporal resolution hydrogeological monitoring system developed in the Conero Mt. Regional Park (central Italy) to predict groundwater level trends of a shallow aquifer exploited for drinking purposes. The field equipment consists of a thermo-pluviometric station, three volumetric water content, electric conductivity, and soil temperature probes in the vadose zone at 0.6 m, 0.9 m, and 1.7 m, respectively, and a piezometer instrumented with a permanent water-level probe. The monitored period started in January 2022, and the variables were recorded every fifteen minutes for more than one hydrologic year, except the groundwater level which was recorded on a daily scale. The developed model consists of three “virtual boxes” (i.e., atmosphere, unsaturated zone, and saturated zone) for which the hydrological variables characterizing each box were integrated into a time series forecasting model based on Prophet developed in the Python environment. Each measured parameter was tested for its influence on groundwater level prediction. The model was fine-tuned to an acceptable prediction (roughly 20% ahead of the monitored period). The quantitative analysis reveals that optimal results are achieved by expoiting the hydrological variables collected in the vadose zone at a depth of 1.7 m below ground level, with a Mean Absolute Error (MAE) of 0.189, a Mean Absolute Percentage Error (MAPE) of 0.062, a Root Mean Square Error (RMSE) of 0.244, and a Correlation coefficient of 0.923. This study stresses the importance of calibrating groundwater level prediction methods by exploring the hydrologic variables of the vadose zone in conjunction with those of the saturated zone and meteorological data, thus emphasizing the role of hydrologic time series forecasting as a challenging but vital aspect of optimizing groundwater management.

Electrochemical Nucleation and Growth: A Multimicroscopy Approach at the Same Scale

Electrodeposition is a critical electrochemical reaction with broad applications in technology. This demands a comprehensive understanding of the electrochemical nucleation and growth process (EN&G), and the different factors driving the formation of a new phase on a given substrate. Our understanding of the role of the substrate and the real nature of active sites still remains elusive due to the challenge of probing specific sites and bringing a statistical interpretation of the nucleation process. Moreover, conventional macroscopic approaches often record electrochemical magnitudes over large areas (cm2) which are then correlated with surface characterizations obtained from a vastly different scale (~μm2), correlations, which are not necessarily representative of the macroscopic surface. In order to establish meaningful and unambiguous correlations, it becomes necessary to employ techniques capable of surveying the surface with a high spatial-temporal resolution, such as scanning probe techniques, capable of recovering information in a scale closer to the growth of the new phase, i.e., local scale1. To address these challenges, we employed Scanning Electrochemical Cell Microscopy (SECCM), to study the EN&G of Cu on a glassy carbon substrate at a scale closer to the initial stages of the electrodeposition2. SECCM can be combined with other imaging methods such as scanning electron microscopy (SEM) to recover information from the deposited nanostructures at the same scale as the electrochemical response3. This so-called multimicroscopy approach allows us establishing direct correlations between the EN&G and the morphological, structural, and compositional features of the substrate and deposit. As a result, new insights that are not available with conventional macro-electrochemical analysis can be gained, such as the time-, potential-, and site-dependent nature of the EN&G process with a statistical perspective. This approach provides a new avenue for developing novel analysis strategies to understand the real nature of active sites for nucleation.References M. Bernal, D. Torres, S. S. Parapari, M. Čeh, K. Ž. Rožman, S. Šturm, and J. Ustarroz, Electrochim. Acta, 445, 142023 (2023).D. Torres, M. Bernal, A. Demaude, S. Hussain, L. Bar, P. Losada-Pérez, F. Reniers, and J. Ustarroz, J. Electrochem. Soc., 169, 102513 (2022).D. Torres, M. Bernal Lopez, and J. Ustarroz, Electrochemical nucleation and growth: A multimicroscopy Approach at the same scale, In preparation. (2023). Figure 1

Large-scale public charging demand prediction with a scenario- and activity-based approach

Transportation system electrification is expected to bring millions of electric vehicles (EVs) on road within decades. Accurately predicting the charging demand is necessary to accommodate the surge in EV deployment. This paper presents a novel modeling framework to predict the public charging demand profile derived from people’s travel trajectories, with the consideration of the demand and supply stochasticity of transportation systems and the charging behavior heterogeneity of EV users. The vehicle charging decision-making process is explicitly modeled, and the charging need of each EV user is estimated associated with their travel trajectories. The methodology enables charging demand prediction with a high spatial–temporal resolution for transportation system electrification planning. A case study was conducted in Los Angeles County to predict the demand for public charging facilities in 2035 and perform corresponding spatial–temporal analysis of EV public charging under various scenarios of future electrification levels and network conditions.

A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning

While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolution. To overcome this limitation, we developed UMelt: a surface melt record for all Antarctic ice shelves with a high spatial (500 m) and high temporal (12 h) resolution for the period 2016–2021. Our approach is based on a deep learning model, specifically a U-Net, which was developed in Google Earth Engine. The U-Net combines microwave remote sensing observations from three sources: Sentinel-1, Special Sensor Microwave Imager/Sounder (SSMIS), and Advanced Scatterometer (ASCAT). The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017–2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital elevation model, and multi-year Sentinel-1 melt fraction as predictors. The trained U-Net performed well on the Shackelton Ice Shelf for test melt season 2016–2017 (accuracy: 91.3%; F1-score: 86.9%), and the Larsen C Ice Shelf, which was not considered during training (accuracy: 91.0%; F1-score: 89.3%). Using the trained U-Net model, we have successfully developed the UMelt record. UMelt allows Antarctic-wide surface melt to be detected at a small scale while preserving a high temporal resolution, which could lead to new insights into the response of ice shelves to a changing atmospheric forcing.

Backpack satellite transmitters reduce survival but not nesting propensity or success of greater sage-grouse.

Telemetry technology is ubiquitous for studying the behavior and demography of wildlife, including the use of traditional very high frequency (VHF) radio telemetry and more recent methods that record animal locations using global positioning systems (GPS). Satellite-based GPS telemetry allows researchers to collect high spatial-temporal resolution data remotely but may also come with additional costs. For example, recent studies from the southern Great Basin suggested GPS transmitters attached via backpacks may reduce the survival of greater sage-grouse (Centrocercus urophasianus) relative to VHF transmitters attached via collars that have been in use for decades. While some evidence suggests GPS backpacks reduce survival, no studies have examined the effects of GPS backpacks on breeding behavior and success. Therefore, we compared survival, breeding behavior, and nest success of sage-grouse hens marked with both VHF collars and GPS backpack transmitter over a 7-year period in central Idaho, USA. GPS backpacks reduced spring-summer survival of sage-grouse hens relative to hens with VHF collars, where daily mortality probability was 68%-82% higher from March 1 to August 1. Yet satellite GPS backpacks did not consistently affect nest success or the likelihood or timing of nest initiation relative to VHF collars. Daily nest survival varied annually and with timing of nest initiation and nest age, but marginal effects of transmitter type were statistically insignificant and interactions between transmitter type and study year produced no meaningful patterns. Our results corroborate recent studies for the effect of satellite GPS backpacks on sage-grouse survival, but also suggest that these transmitters do not appear to affect components of fecundity. Our results therefore add important context to recent debate surrounding the effects of GPS backpacks on sage-grouse, and the relative strengths and weaknesses of different transmitter types for understanding behavior and population dynamics.

Validation of FY-4A/AGRI LST and High Temporal–Spatial Resolution Analysis of Surface Heat Resources in Hunan Province, Central China

Land surface temperature (LST) is a crucial parameter in climate and ecology, exerting significant influence on meteorological conditions, ecosystems, and human life. LST data sources are diverse, with remote sensing being the prevailing means of acquisition. FY-4A/AGRI offers high-quality LST products for East Asia. We conducted a comprehensive evaluation and refined analysis of surface heat resources in Hunan Province, central China, over a two-year period using the 4 km/1 h resolution product in this study. The results demonstrate that the FY-4A LST product effectively captures surface temperature (R = 0.893), albeit with a relatively high error level (Bias = −6.295 °C; RMSE = 8.58 °C), particularly in capturing high LST values. The performance of this product is superior in the eastern flat terrain area of Hunan Province compared to its performance in the western mountainous region due to environmental conditions causing systematic errors that contribute to instability in detection deviation for this product. Surface heat resources are more abundant in eastern Hunan Province than in mountainous areas located west and southwardly, and the detailed distribution of them at finer scales is mainly influenced by terrain and climate conditions. There is no obvious seasonal difference in the distribution of heat resources except in winter, and rapid urbanization within Chang–Zhu–Tan urban agglomeration over two years has significantly altered the spatial distribution pattern of surface heat resources across Hunan Province. These findings provide a quantitative baseline for assessing FY-4A satellite’s detection capability while serving as a reference for further application of its LST products and establishing foundations for divisional classification and utilization strategies pertaining to surface heat resources within Hunan Province.

Gold Nanoclusters for Tumor Diagnosis and Treatment

Nanomedicine shows remarkable potential to improve the efficacy of diagnosis and treatment of tumors, utilizing nanotechnologies and nanomaterials. Gold nanoclusters (AuNCs) have emerged as a highly promising option due to their exceptional optical and enzyme‐mimicking catalytic activities as well as good biocompatibility. The renal clearable clusters can enrich in the tumors upon their enhanced permeability and retention properties, which benefits the tumor‐related applications. The fluorescence of AuNCs in the second near‐infrared region possesses extraordinary penetration depth and high temporal–spatial resolution, enabling excellent in vivo imaging and real‐time monitoring of pathological process. AuNC‐based nanoplatforms represent a paradigm of integrated, efficient, intelligent, and safe treatment strategy, extending personalized tumor therapy. Meanwhile, the optical and biocatalytic properties can be modulated by adopting the atom/ligand engineering, which further enhances the efficacy of AuNCs. Herein, the advances of AuNCs in the field of diagnosis and treatment of tumors are summarized and the directions to be improved are proposed to promote the clinical translation of the AuNCs.

A spatio-temporal fusion strategy for improving the estimation accuracy of the aboveground biomass in grassland based on GF-1 and MODIS

The accurate estimation of the aboveground biomass (AGB) is crucial for the sustainable utilization and management of grassland resources. Spatio-temporal inconsistencies between field samples and satellite images are major source of error in the estimation of grassland AGB. To solve this problem, this study selected the Three Rivers Headwater Region as the study area and proposed a selection strategy for base-period images of spatio-temporal fusion that was suitable for use at a large spatial scale in which cloud-free images are difficult to obtain. The spatial and temporal adaptive reflectance fusion model (STARFM) based on the selection strategy was used to generate a synthetic Normalized Difference Vegetation Index (NDVI) dataset with high spatial-temporal resolution by using the maximum value composite of GF-1 NDVI and MODIS NDVI to enhance the spatial-temporal quality of the images for field-scale application. Three estimation models for grassland AGB were then constructed by the random forest algorithm using synthetic NDVI, MODIS NDVI, GF-1 NDVI respectively, together with ancillary data. Following this, the estimation model with the highest accuracy was used to generate a 16-m eight-day time-series AGB in the growing season. The results showed: (1) The synthetic NDVI was correlated closely with the observed GF-1 NDVI, with an average R of 0.825 and a RMSE of 0.087. The temporal trend of the synthetic NDVI for each grassland type was highly consistent with that of the MODIS NDVI in the growing season with a correlation higher than 0.9. (2) The synthetic NDVI reduced the spatial difference between field samples and images to 16-fold, and the temporal difference was controlled to within four days under ideal conditions. (3) The synthetic NDVI improved the estimation accuracy of grassland AGB by about 15.9% and 19.7% (R2), and 13.7% and 17.5% (RMSE) relative to MODIS NDVI and GF-1 NDVI, respectively. (4) The time-series AGB revealed accurately the spatial distribution of and seasonal temporal variations in the grassland biomass. The results of this study may serve as scientific guidance for timely monitoring of grassland conditions and precise management of grassland resources in China.

Multispectral Vegetation Indices and Machine Learning Approaches for Durum Wheat (Triticum durum Desf.) Yield Prediction across Different Varieties

Durum wheat (Triticum durum Desf.) is one of the most widely cultivated cereal species in the Mediterranean basin, supporting pasta, bread and other typical food productions. Considering its importance for the nutrition of a large population and production of high economic value, its supply is of strategic significance. Therefore, an early and accurate crop yield estimation may be fundamental to planning the purchase, storage, and sale of this commodity on a large scale. Multispectral (MS) remote sensing (RS) of crops using unpiloted aerial vehicles (UAVs) is a powerful tool to assess crop status and productivity with a high spatial–temporal resolution and accuracy level. The object of this study was to monitor the behaviour of thirty different durum wheat varieties commonly cultivated in Italy, taking into account their spectral response to different vegetation indices (VIs) and assessing the reliability of this information to estimate their yields by Pearson’s correlation and different machine learning (ML) approaches. VIs allowed us to separate the tested wheat varieties into different groups, especially when surveyed in April. Pearson’s correlations between VIs and grain yield were good (R2 > 0.7) for a third of the varieties tested; the VIs that best correlated with grain yield were CVI, GNDVI, MTVI, MTVI2, NDRE, and SR RE. Implementing ML approaches with VIs data highlighted higher performance than Pearson’s correlations, with the best results observed by random forest (RF) and support vector machine (SVM) models.

Evaluation of GPM IMERG and error sources for tropical cyclone precipitation over eastern China

Long-term precipitation data with high temporal-spatial resolution and high precision is crucial for the monitoring of tropical cyclone (TC) precipitation and the understanding of its response to global climate change. The performance of the Final run of Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) V06B product to characterize the TC precipitation is evaluated during the TC season (May to September) from 2014 to 2019 against a dense network of gauges over eastern China, considering the impact of land cover types (LCTs), different techniques (morphing) and instruments, i.e., passive microwave (PMW) and infrared (IR) sensors. Overall, IMERG captures well the spatial pattern and temporal variations of mean TC rainfall rate (RR) over eastern China but shows underestimations of mean TC RR compared to gauge observations, with the mean error (ME) being −0.78 mm/h, which are mainly contributed by the underestimations of heavy TC RR (ME: −12.1 mm/h). Moreover, the IMERG performance shows dependence on the LCTs and IMERG components (i.e., morph, IR + morph, and PMW sensors). The overestimation/underestimation of light/heavy TC precipitation events is most obvious over water/evergreen broadleaf forests, with the bias being 1.2/0.5. Differently, IMERG even slightly underestimates light TC precipitation events over grasslands (bias = 0.88). Among four IMERG components, Microwave Humidity Sounder (MHS) performs the best in characterizing all TC precipitation events, followed by morph, Special Sensor Microwave Imager/Sounder (SSMIS), and finally IR + morph, indicating that IR algorithms might need to be further improved. The mean TC RR is slightly overestimated by MHS over most regions of eastern China with positive ME exceeding 2.8 mm/h, while is largely underestimated by IR + morph among four components with most MEs lower than −1.2 mm/h. This study may provide an observational reference for the continued development and future improvement of the IMERG product.

Transcranial ultrasound stimulation at the peak-phase of theta-cycles in the hippocampus improve memory performance

The present study aimed to investigate the effectiveness of closed-loop transcranial ultrasound stimulation (closed-loop TUS) as a non-invasive, high temporal-spatial resolution method for modulating brain function to enhance memory. For this purpose, we applied closed-loop TUS to the CA1 region of the rat hippocampus for 7 consecutive days at different phases of theta cycles. Following the intervention, we evaluated memory performance through behavioral testing and recorded the neural activity. Our results indicated that closed-loop TUS applied at the peak phase of theta cycles significantly improves the memory performance in rats, as evidenced by behavioral testing. Furthermore, we observed that closed-loop TUS modifies the power and cross-frequency coupling strength of local field potentials (LFPs) during memory task, as well as modulates neuronal activity patterns and synaptic transmission, depending on phase of stimulation relative to theta rhythm. We demonstrated that closed-loop TUS can modulate neural activity and memory performance in a phase-dependent manner. Specifically, we observed that effectiveness of closed-loop TUS in regulating neural activity and memory is dependent on the timing of stimulation in relation to different theta phase. The findings implied that closed-loop TUS may have the capability to alter neural activity and memory performance in a phase-sensitive manner, and suggested that the efficacy of closed-loop TUS in modifying neural activity and memory was contingent on timing of stimulation with respect to the theta rhythm. Moreover, the improvement in memory performance after closed-loop TUS was found to be persistent.

Validation and Spatiotemporal Analysis of Surface Net Radiation from CRA/Land and ERA5-Land over the Tibetan Plateau

High spatial–temporal resolution surface net radiation (RN) data are of great significance to the study of climate, ecology, hydrology and cryosphere changes on the Tibetan Plateau (TP), but the verification of the surface net radiation products on the plateau is not sufficient. In this study, the China Meteorological Administration Global Land Surface Reanalysis Products (CRA/Land) and ECMWF Land Surface Reanalysis version 5 (ERA5-Land) RN data were validated using ground measurements at daily and monthly time scales, and the spatiotemporal patterns were also analyzed. The results indicate the following: (1) CRA/Land overestimated while ERA5-Land underestimated RN, but CRA/Land RN outperformed ERA5-Land in observations at the daily and monthly scale. (2) The CRA/Land RN data had a larger error in the central part and a smaller error in the northeast of the TP, while ERA5-Land showed the opposite. (3) The spatial patterns of RN revealed by CRA/Land and ERA5-Land data showed differences in most regions. The CRA/Land data showed that the RN of the TP had a downward trend during 2000 and 2020 with a slope of −0.112 W·m−2/a, while the ERA5-Land data indicated an upward trend with a change rate of 0.016 W·m−2/a. (4) Downwelling shortwave radiation (DSR), upwelling shortwave radiation (USR), downwelling longwave radiation (DLR) and upwelling longwave radiation (ULR) are the four components of RN, and the evaluation results indicate that the DSR, DLR and ULR recorded via CRA/Land and ERA5-Land are consistent with the observed data, but the consistency between the USR recorded via CRA/Land and ERA5-Land and the observed data is poor. (5) The inconsistency of the USR data is the main reason for the large differences in the spatiotemporal distribution of CRA/Land and ERA5-Land RN data across the TP.

Multi-source remote sensing data and image fusion technology reveal significant spatiotemporal heterogeneity of inundation dynamics in a typical large floodplain lake system

Study regionThe Poyang Lake, which is located on the south bank of the middle-lower Yangtze River basin. The lake is the largest freshwater lake in China, and also a typical floodplain lake in the world. Study focusThe spatiotemporal heterogeneity of inundation dynamics of large floodplain lake system has not been paid enough attention. Based on the reconstructed high spatial and temporal resolution inundation dataset using the image fusion model and multi-source remote sensing data, this study systematically analyzed the spatiotemporal heterogeneity of inundation dynamics in the Poyang Lake- floodplain system. New hydrological insights for the regionIt is found that within the same floodplain lake, the inundated area and inundation frequency in different regions of the lake (the main lake region and the adjacent floodplain region) can have asynchronous intra-annual fluctuation and opposite inter-annual change trend. This is highly related to the hydrological complexity of the lake: the relative impacts of catchment inflow and the Yangtze River varies in different regions across the lake. The stage-area relationship at the central station along the flow direction of the lake has the highest linear correlation, which might provide more accurate estimates of lake surface/volume. In addition, this study highlights the importance of reconstructed high spatial-temporal resolution of remote sensing data for the accurate assessment of inundation dynamics in floodplain lakes. All the results enrich the understanding of complex hydrological regime of large floodplain lakes and are valuable for the practice of water resources management and ecological conservation in such lakes.

Surface Solar Radiation Compositions Observed from Himawari-8/9 and Fengyun-4 Series

Abstract Surface downward solar radiation compositions (SSRC), including photosynthetically active radiation (PAR), ultraviolet-A (UVA), ultraviolet-B (UVB), and shortwave radiation (SWR), with high spatial–temporal resolutions and precision are essential for applications including solar power, vegetation photosynthesis, and environmental health. In this study, an optimal algorithm was developed to calculate SSRC, including their direct and diffuse components. Key features of the algorithm include combining the radiative transfer model with machine learning techniques, including full consideration of the effects of aerosol types, cloud phases, and gas components. A near-real-time monitoring system was developed based on this algorithm, with SSRC products generated from Himawari-8/9 and Fengyun-4 series data. Validation with ground-based data shows that the accuracy of the SWR and PAR compositions (daily mean RMSEs of 19.7 and 9.2 W m−2, respectively) are significantly better than those of state-of-the-art products from CERES, ERA5, and GLASS. The accuracy of UVA and UVB measurements is comparable with CERES. Characteristics of aerosols, clouds, gases, and their impacts on SSRC are investigated before, during, and post COVID-19; in particular, significant SSRC variations due to the reduction of aerosols and increase of ozone are identified in the Chinese central and eastern areas during that period. The spatial–temporal resolution of data products [up to 0.05° (10 min)−1 for the full-disk region] is one of the most important advantages. Data for the East Asia–Pacific region during 2016–20 is available from the CARE home page (www.slrss.cn/care/sp/pc/).

In Situ Photoacoustic Visualization of Pneumonia Induced by MRSA and Specific Identifying Tumor-Homing Bacteria.

Optical imaging holds great promise for monitoring bacterial infectious processes and drug resistance with high temporal-spatial resolution. Currently, the diagnosis of deep-seated bacterial infections in vivo with fluorescence imaging, including near-infrared (NIR) fluorescence imaging technology, remains a significant challenge due to its limited tissue penetration depth. In this study, we developed a highly specific targeting probe, Cy7-Neo-NO2, by conjugating a bacterial 16S rRNA-targeted moiety, neomycin, with a bacterial nitroreductase (NTR)-activated NIR photoacoustic (PA) scaffold using our previously developed caged photoinduced electron transfer (a-PeT) approach. This conjugation effectively resolved probe aggregation issues in physiological conditions and substantially enhanced its reactivity toward bacterial NTR. Notably, Cy7-Neo-NO2 enabled the first in situ photoacoustic imaging of pneumonia induced by methicillin-resistant Staphylococcus aureus (MRSA), as well as the detection of bacteria within tumors. Furthermore, upon NIR irradiation, Cy7-Neo-NO2 successfully inhibited MRSA growth through a synergistic effect combining photothermal therapy and photodynamic therapy. Our results provided an effective tool for obtaining exceptional PA agents for accurate diagnosis, therapeutic evaluation of deep-seated bacterial infections in vivo, and intratumoral bacteria-specific recognition.

Blood Circulation Assessment by Steadily Fluorescent Near-Infrared-II Aggregation-Induced Emission Nano Contrast Agents.

The dysfunction of the blood circulation system typically induces acute or chronic ischemia in limbs and vital organs, with high disability and mortality. While conventional tomographic imaging modalities have shown good performance in the diagnosis of circulatory diseases, multiple limitations remain for real-time and precise hemodynamic evaluation. Recently, fluorescence imaging in the second region of the near-infrared (NIR-II, 1000-1700 nm) has garnered great attention in monitoring and tracing various biological processes in vivo due to its advantages of high spatial-temporal resolution and real-time feature. Herein, we employed NIR-II imaging to carry out a blood circulation assessment by aggregation-induced emission fluorescent aggregates (AIE nano contrast agent, AIE NPs). Thanks to the longer excited wavelength, enhanced absorptivity, higher brightness in the NIR-II region, and broader optimal imaging window of the AIE NPs, we have realized a multidirectional assessment for blood circulation in mice with a single NIR-II imaging modality. Thus, our work provides a fluorescence contrast agent platform for accurate hemodynamic assessment.

Integrating micro-air quality sensors and satellite-based measurements to establish a high spatial-temporal resolution PM2.5 model in Taiwan

Integrating micro-air quality sensors and satellite-based measurements to establish a high spatial-temporal resolution PM2.5 model in Taiwan

3D printing of plasmonic nanofocusing tip enabling high resolution, high throughput and high contrast optical near-field imaging

Scanning near-field optical microscopy (SNOM) offers a means to reach a fine spatial resolution down to ~ 10 nm, but unfortunately suffers from low transmission efficiency of optical signal. Here we present design and 3D printing of a fiber-bound polymer-core/gold-shell spiral-grating conical tip that allows for coupling the inner incident optical signal to the outer surface plasmon polariton with high efficiency, which then adiabatically transport, squeeze, and interfere constructively at the tip apex to form a plasmonic superfocusing spot with tiny size and high brightness. Numerical simulations and optical measurements show that this specially designed and fabricated tip has 10% transmission efficiency, ~ 5 nm spatial resolution, 20 dB signal-to-noise ratio, and 7000 pixels per second fast scanning speed. This high-resolution, high throughput, and high contrast SNOM would open up a new frontier of high spatial-temporal resolution detecting, imaging, and monitoring of single-molecule physical, chemical, and biological systems, and deepen our understanding of their basic science in the single-molecule level.

Development of The Navigator: A Lagrangian sensing system to characterize surface freshwater ecosystems

Most freshwater aquatic studies rely on Eulerian monitoring, i.e., water quality and quantity are monitored using grab samples or semi-continuous sensors deployed at fixed cross-sections. While Eulerian monitoring is practical, it provides a limited understanding of spatial and temporal heterogeneity. We designed and built The Navigator, a Lagrangian (i.e., along a flow path) monitoring system that offers cost-effective solutions for in-situ, real-time data collection in surface freshwater ecosystems. The Navigator features a suite of technologies, including an autonomous surface vehicle with GPS and LTE connectivity, water quality sensors, a depth sonar, a camera, and a webpage dashboard to visualize real-time data. With these technologies, The Navigator provides insight into where, how, and why water quality and quantity change over time and space as it moves with the current or follows user-specified pathways. We tested The Navigator monitoring water quality parameters at high spatial-temporal resolution in multiple surface water bodies in New Mexico (USA) to: (1) identify water quality changes associated with land use changes along a 7th-order reach in the Rio Grande, (2) identify the fate of wildfire disturbances ∼175 km downstream of a burned watershed affected by the largest wildfire ever recorded in the state, (3) monitor the water quality of a recreational fishing pond in the City of Albuquerque. Our three successful tests confirm that The Navigator is an affordable (USD 5,101 in 2023) monitoring system that can be used to address questions involving mass and energy balances in surface waters.