High-resolution Sensor Research Articles

Multilayer Fluorine-Free MoBTx MBene with Hydrophilic Structural-Modulating for the Fabrication of a Low-Resistance and High-Resolution Humidity Sensor.

2D transition metal borides (MBenes) with abundant surface terminals hold great promise in molecular sensing applications. However, MBenes from etching with fluorine-containing reagents present inert -fluorine groups on the surface, which hinders their sensing capability. Herein, the multilayer fluorine-free MoBTx MBene (where Tx represents O, OH, and Cl) with hydrophilic structure is prepared by a hydrothermal-assisted hydrochloric acid etching strategy based on guidance from the first-principle calculations. Significantly, the fluorine-free MoBTx-based humidity sensor is fabricated and demonstrates low resistance and excellent humidity performance, achieving a response of 90% to 98%RH and a high resolution of 1%RH at room temperature. By combining the experimental results with the first-principles calculations, the interactions between MoBTx and H2O, including the adsorption and intercalation of H2O, are understood first in depth. Finally, the portable humidity early warning system for real-time monitoring and early warning of infant enuresis and back sweating illustrates its potential for humidity sensing applications. This work not only provides guidance for preparation of fluorine-free MBenes, but also contributes to advancing their exploration in sensing applications.

GW-MUSIC focusing algorithm with high accuracy for composite damage diagnosis

The Guided Wave (GW)-based Multiple Signal Classification (MUSIC) method for damage imaging in composite structures offers high-resolution and flexible sensor array placement, demonstrating considerable potential for precise damage localization. However, the application to real aircraft composite structures, characterized by complex structural features, presents substantial challenges. These include complex boundary reflections, pronounced anisotropy, and diminished scattered signals from damage sites. This paper presents a novel single-peak anisotropy-compensated MUSIC method for damage imaging of complex composites. This method, designed for improved localization accuracy, combines a self-excited calibration strategy to mitigate anisotropy with single wave peak focusing to amplify signals and reduce boundary reflections. Experimental validation on a complex composite wing structure demonstrated significant enhancements in localization precision, affirming the method's efficacy.

Land Surface Longwave Radiation Retrieval from ASTER Clear-Sky Observations

Surface longwave radiation (SLR) plays a pivotal role in the Earth’s energy balance, influencing a range of environmental processes and climate dynamics. As the demand for high spatial resolution remote sensing products grows, there is an increasing need for accurate SLR retrieval with enhanced spatial detail. This study focuses on the development and validation of models to estimate SLR using measurements from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Given the limitations posed by fewer spectral bands and data products in ASTER compared to moderate-resolution sensors, the proposed approach combines an atmospheric radiative transfer model MODerate resolution atmospheric TRANsmission (MODTRAN) with the Light Gradient Boosting Machine algorithm to estimate SLR. The MODTRAN simulations were performed to construct a representative training dataset based on comprehensive global atmospheric profiles and surface emissivity spectra data. Global sensitivity analyses reveal that key inputs influencing the accuracy of SLR retrievals should reflect surface thermal radiative signals and near-surface atmospheric conditions. Validated against ground-based measurements, surface upward longwave radiation (SULR) and surface downward longwave radiation (SDLR) using ASTER thermal infrared bands and surface elevation estimations resulted in root mean square errors of 17.76 W/m2 and 25.36 W/m2, with biases of 3.42 W/m2 and 3.92 W/m2, respectively. Retrievals show systematic biases related to extreme temperature and moisture conditions, e.g., causing overestimation of SULR in hot humid conditions and underestimation of SDLR in arid conditions. While challenges persist, particularly in addressing atmospheric variables and cloud masking, this work lays a foundation for accurate SLR retrieval from high spatial resolution sensors like ASTER. The potential applications extend to upcoming satellite missions, such as the Landsat Next, and contribute to advancing high-resolution remote sensing capabilities for an improved understanding of Earth’s energy dynamics.

High-dimensional quantum correlation measurements with an adaptively gated hybrid single-photon camera

Efficient measurement of high-dimensional quantum correlations, especially spatial ones, is essential for quantum technologies. We propose and demonstrate an adaptively gated hybrid intensified camera (HIC) that combines the information from a high spatial resolution sensor and a high temporal resolution detector, offering precise control over the number of photons detected within each frame. The HIC facilitates spatially resolved single-photon counting measurements. We study the measurement of momentum correlations of photon pairs generated in type-I spontaneous parametric downconversion with the HIC and demonstrate the possibility of time-tagging the registered photons. With a spatial resolution of multi-megapixels and nanosecond temporal resolution, this system allows for the realization of previously infeasible quantum optics experiments.

Tracking the Dynamics of Spartina alterniflora with WorldView-2/3 and Sentinel-1/2 Imagery in Zhangjiang Estuary, China

The invasion of Spartina alterniflora (S. alterniflora) has posed serious threats to the sustainability, quality and biodiversity of coastal wetlands. To safeguard coastal ecosystems, China has enacted large-scale S. alterniflora removal projects, which set the goal of effectively controlling S. alterniflora throughout China by 2025. The accurate monitoring of S. alterniflora with remote sensing is urgent and requisite for the scientific eradication, control and management of this invasive plant. In this study, we combined multi-temporal WorldView-2/3 (WV-2/3) and Sentinel-1/2 imagery to monitor the S. alterniflora dynamics before and after the S. alterniflora removal projects in Zhangjiang Estuary. We put forward a new method for S. alterniflora detection with eight-band WV-2/3 imagery. The proposed method first used NDVI to discriminate S. alterniflora from water, mud flats and mangroves based on Ostu thresholding and then used the red-edge, NIR1 and NIR2 bands and support vector machine (SVM) classifier to distinguish S. alterniflora from algae. Due to the contamination of frequent cloud cover and tidal inundation, the long revisit time of high-resolution satellite sensors and the short-term S. alterniflora removal projects, we combined Sentinel-1 SAR time series and Sentinel-2 optical imagery to monitor the S. alterniflora removal project status in 2023. The overall accuracies of the S. alterniflora detection results here are above 90%. Compared with the traditional SVM method, the proposed method achieved significantly higher identification accuracy. The S. alterniflora area was 115.19 hm2 in 2015, 152.40 hm2 in 2017 and 15.29 hm2 in 2023, respectively. The generated S. alterniflora maps clearly show the clonal growth of S. alterniflora in Zhangjiang Estuary from 2015 to 2017, and the large-scale S. alterniflora eradication project has achieved remarkable results with a removal rate of about 90% in the study area. With the continuous implementation of the “Special Action Plan for the Prevention and Control of Spartina alterniflora (2022–2025)” which aims to eliminate more than 90% of S. alterniflora in all provinces in China by 2025, the continual high-spatial resolution monitoring of S. alterniflora is crucial to control secondary invasion and restore coastal wetlands.

Coastal CUBEnet: an integrated observation and modeling system for sustainable Northern Gulf of Mexico coastal areas

The University of Southern Mississippi has developed the coastal CUBEnet environment. Coastal CUBEnet is a high-resolution, coastal ocean sensor, modeling, and data sharing web-based network that provides the environmental intelligence needed to support the complex modeling of the interlinked processes in the northern Gulf of Mexico. With near-real time data delivery via a common infrastructure, CUBEnet uses state of the art sensors to provide a set of networked measurements systems, visualization tools, and model developments to gain an understanding of the Gulf of Mexico’s marine environments. CUBEnet is also a mechanism for improved human engagement with Gulf of Mexico resources and provides stake holders with the data needed to make informed coastal, environmental, and economic decisions. The Coastal CUBEnet’s data environment utilizes both stationary and uncrewed mobile systems and high-resolution distributed sensors to create a networked platform across the northern coastal Gulf of Mexico. CUBEnet’s modeling environment has developed an implementation of The Coupled Ocean Atmosphere Wave Sediment Transport (COAWST) Model for the Mississippi Bight region that has been applied to investigate shore to shelf advective exchange processes, and their influence on coastal water quality conditions that support the region’s prolific marine ecosystem. CUBEnet’s modeling environment provides prototype modeling applications that are supported by real-time observations of key coastal environmental variables. CUBEnet’s Web accessible visualization tools provide parameter fields and vertical profiles from hydrodynamic models and field observations. Nowcasts and forecast results are available for the Eastern LA coastline, MS coastline, Mobile Bay, and the West coast of Florida.

Beyond the surface: A comparative study of intraoral scanners in subgingival configuration scanning

ObjectivesThis study conducted a comprehensive comparative analysis of three intraoral scanners (CEREC Primescan, TRIOS, CEREC Omnicam) and a lab scanner (inEosX5) assessing their precision in simulating subgingival tooth preparations. MethodsUtilizing a dental simulation mannequin with a 3D-printed resin structure, 100 structures with depths ranging from 0.5 to 4.0 mm were created within a square mimicking a rectangular tank surface. Four scanner groups (A-D) and five subgroups were established. Two digitization methods, a customized parallelometer and an intraoral simulation, were applied, ensuring a standardized scanning sequence. Trueness was evaluated by comparing CAD-calculated surface areas with actual dimensions, and qualitative trueness analysis was conducted using MeshLab. Surface areas were computed using the formula SA = 2lw + 2lh + 2wh. Statistical analyses, including Pearson’s correlation coefficient, Kolmogorov–Smirnoff and Levene’s tests, three-way ANOVA, and paired sample t-tests, elucidated relationships and differences (a=0.05). ResultsA robust correlation (r = 0.850, p < 0.001) between intraoral scanner choice and scanned area depth was found. Inverse correlations were noted for experimental methods. Three-way ANOVA demonstrated significant scanner-depth interaction (F(12,760) = 760.801, p < 0.001). SignificanceEmphasizing high-resolution sensors and advanced technologies, the study underscores the optimal choice for subgingival digitization, acknowledging variations among scanners.

Detection and Quantification of Arnica montana L. Inflorescences in Grassland Ecosystems Using Convolutional Neural Networks and Drone-Based Remote Sensing

Arnica montana L. is a medicinal plant with significant conservation importance. It is crucial to monitor this species, ensuring its sustainable harvesting and management. The aim of this study is to develop a practical system that can effectively detect A. montana inflorescences utilizing unmanned aerial vehicles (UAVs) with RGB sensors (red–green–blue, visible light) to improve the monitoring of A. montana habitats during the harvest season. From a methodological point of view, a model was developed based on a convolutional neural network (CNN) ResNet101 architecture. The trained model offers quantitative and qualitative assessments of A. montana inflorescences detected in semi-natural grasslands using low-resolution imagery, with a correctable error rate. The developed prototype is applicable in monitoring a larger area in a short time by flying at a higher altitude, implicitly capturing lower-resolution images. Despite the challenges posed by shadow effects, fluctuating ground sampling distance (GSD), and overlapping vegetation, this approach revealed encouraging outcomes, particularly when the GSD value was less than 0.45 cm. This research highlights the importance of low-resolution image clarity, on the training data by the phenophase, and of the need for training across different photoperiods to enhance model flexibility. This innovative approach provides guidelines for mission planning in support of reaching sustainable management goals. The robustness of the model can be attributed to the fact that it has been trained with real-world imagery of semi-natural grassland, making it practical for fieldwork with accessible portable devices. This study confirms the potential of ResNet CNN models to transfer learning to new plant communities, contributing to the broader effort of using high-resolution RGB sensors, UAVs, and machine-learning technologies for sustainable management and biodiversity conservation.

P‐81: Multimodal Intelligent Display Backplane Blocks

The display of the future is multimodal, integrating optical sensors, RFID antennas, ultrasound transducers, etc. The sensors produce a large amount of redundant data, which should be preprocessed on the substrate, ideally on the backpanel. Control gates and select and data drivers need to be more configurable and partitionable to create operational flexibility. This paper presents various circuit blocks for processing and control, including operational amplifiers, high‐resolution analog‐to‐digital converters, a microprocessor, DC‐DC converters, and high resolution sensor pixel array architectures. Moreover, the performance of the basic building blocks is compared in LTPS and IGZO technology.

Freeform metasurface color router for deep submicron pixel image sensors.

Advances in imaging technologies have led to a high demand for ultracompact, high-resolution image sensors. However, color filter-based image sensors, now miniaturized to deep submicron pixel sizes, face challenges such as low signal-to-noise ratio due to fewer photons per pixel and inherent efficiency limitations from color filter arrays. Here, we demonstrate a freeform metasurface color router that achieves ultracompact pixel sizes while overcoming the efficiency limitations of conventional architectures by splitting and focusing visible light instead of filtering. This development is enabled by a fully differentiable topology optimization framework to maximize the use of the design space while ensuring fabrication feasibility and robustness to fabrication errors. The metasurface can distribute an average of 85% of incident visible light according to the Bayer pattern with a pixel size of 0.6 μm. The device and design methodology enable the compact, high-sensitivity, and high-resolution image sensors for various modern technologies and pave the way for the advanced photonic device design.

Safety and Security-Specific Application of Multiple Drone Sensors at Movement Areas of an Aerodrome

Nowadays, the public service practice applicability of drones and remote sensing sensors is being explored in almost all industrial and military areas. In the present research, in collaboration with different universities, we investigate the applicability of drones in airport procedures, assessing the various potential applications. By exploiting the data from remote sensing sensors, we aim to develop methodologies that can assist airport operations, including managing the risk of wildlife threats to runway safety, infrastructure maintenance, and foreign object debris (FOD) detection. Drones equipped with remote sensing sensors provide valuable insight into surface diagnostics, helping to assess aprons, taxiways, and runways. In addition, drones can enhance airport security with effective surveillance and threat detection capabilities, as well as provide data to support existing air traffic control models and systems. In this paper, we aim to present our experience with the potential airport applications of UAV high-resolution RGB, thermal, and LiDAR sensors. Through interdisciplinary collaboration and innovative methodologies, our research aims to revolutionize airport operations, safety, and security protocols, outlining a path toward a safer, more efficient airport ecosystem.

IMPROVEMENT OF CAPILLAROSCOPY METHOD FOR STUDYING HUMAN MICROCIRCULATION

The human circulatory system has historically captivated researchers in the field of medicine. Modern medicine, however, has moved beyond identifying a single crucial element, such as the heart or venous system, for maintaining blood flow. Instead, it emphasizes a more analytical approach, focusing on the interconnected functioning of all components within the system. The aim of this work is to analyze the existing methods of studying blood microcirculation and to improve the technology of capillaroscopy, technical means of raster microphotography to determine the physiological state and disorders of capillary circulation. To achieve this objective, we address the following tasks: analyzing existing non-invasive methods for studying the human vascular system; enhancing the optical capillaroscopy method through the utilization of modern high-resolution digital cameras, computer, and multimedia equipment, along with appropriate software for analyzing electronic images; developing a technological scheme and equipment design for digital microphotography of capillaries in the periungual region of the upper extremities; providing software solutions for registering and analyzing digital microphotographic images obtained through capillaroscopy; conducting experimental studies to explore the structure and properties of capillaries utilizing the developed technologies and equipment. Magnetic resonance imaging (MRI) enables the comprehensive evaluation of both anatomical and functional aspects of blood flow. Magnetic resonance angiography (MRA) capitalizes on the distinction between the signal emitted by moving tissue (blood) and that of surrounding stationary tissue, facilitating the acquisition of vascular images without the need for radiopaque contrast agents. Ultrasonography integrates Doppler and conventional ultrasound techniques, providing physicians with insights into blood vessel structure and blood flow dynamics. Traditional ultrasound employs sound waves that are imperceptible to the human ear and bounce off blood vessels, while Doppler ultrasound measures the velocity of sound wave reflection from moving elements. Ophthalmoscopy constitutes a fundamental component of standard ophthalmological examinations, serving as a pivotal tool for diagnosing eye conditions and evaluating the condition of blood vessels. Additionally, ophthalmoscopy aids in diagnosing autoimmune disorders. Capillaroscopy enables comprehensive assessments of both systemic and regional microcirculation disorders, facilitating the characterization of tissue metabolism. Dysfunction in capillary function contributes to circulatory impairments, leading to blood stasis, metabolic irregularities, and compromised immunity, thereby exacerbating existing conditions and predisposing individuals to new diseases. Optical computerized capillaroscopy offers a non-invasive means of visualizing, examining, and archiving capillary images, enabling clinicians to make informed assessments regarding blood microcirculation. Experimental validation of the optimized hardware and software capillaroscopy system was conducted using nail bed capillaries of a patient with type II diabetes mellitus undergoing insulin therapy for 10 years. This study underscores the importance of refining and enhancing optical capillaroscopy methodologies by leveraging high-resolution camera sensors and modern computational tools for image processing.

Remote sensing approaches to identify trees to species-level in the urban forest: A review

Most urban tree inventories depend on resource-intensive, field-based assessments, which are unevenly distributed in space and time. Recently, these inventories have been conducted using field inventories combined with airborne multispectral, hyperspectral, LiDAR, and spaceborne multispectral remote sensing. Significant advances have been made in urban tree GIS databases and remote sensing methods, which include delineating individual tree crowns, extracting tree species metrics, and employing classification techniques. Generally, remote sensing methods distinguish individual urban trees using either pixel-based or object-based methods, while image classification procedures are typically divided into parametric (e.g., regression-based classification, Bayesian, and principal component analysis) and non-parametric approaches such as machine learning (e.g., random forests support vector machines) and deep learning (e.g., convolutional neural networks). Our synthesis of the current state of science suggests sensors with the highest spatial (m), spectral (bands), and temporal (repeat time) resolutions result in the most accurate tree species identification. Combining airborne LiDAR/hyperspectral or airborne LiDAR/spaceborne high-resolution multispectral sensors yields the highest accuracy for the most diverse urban forests. An object-based non-parametric approach, like a fully convolutional neural network, scores higher in accuracy assessments than pixel-based parametric approaches. Future studies can leverage global/regional GIS field inventory databases to expand the scope of studies within and across multiple cities, utilizing LiDAR and spaceborne sensors.

Design and Analysis of a Sub-THz Resonator-Based High-Resolution Permittivity Sensor

Design and Analysis of a Sub-THz Resonator-Based High-Resolution Permittivity Sensor

Efficient Target Classification Based on Vehicle Volume Estimation in High-Resolution Radar Systems

In this paper, we propose a method for efficient target classification based on the spatial features of the point cloud generated by using a high-resolution radar sensor. The frequency-modulated continuous wave radar sensor can estimate the distance and velocity of a target. In addition, the azimuth and elevation angle of the target can be estimated by using a multiple-input and multiple-output antenna system. Using the estimated distance, velocity, and angle, the 3D point cloud of target can be generated. From the generated point cloud, we extract the point cloud for each individual target using the density-based spatial clustering of application with noise method and a camera mounted on the radar sensor. Then, we define the convex hull boundaries that enclose these point clouds in both 3D and 2D spaces obtained by orthogonally projecting onto the xy, yz, and zx planes. Using the vertices of convex hull, we calculate the volume of the targets and the areas in 2D spaces. Several feature points, including the calculated spatial information, are numerized and configured into feature vectors. We design an uncomplicated deep neural network classifier based on minimal input information to achieve fast and efficient classification performance. As a result, the proposed method achieved an average accuracy of 97.1%, and the time required for training was reduced compared to the method using only point cloud data and the convolutional neural network-based method.

Assessing experimental silvicultural treatments enhancing structural complexity in a central European forest – BEAST time‐series analysis based on Sentinel‐1 and Sentinel‐2

AbstractAssessing the dynamics of forest structure complexity is a critical task in times of global warming, biodiversity loss and increasing disturbances in order to ensure the resilience of forests. Recent studies on forest biodiversity and forest structure emphasize the essential functions of deadwood accumulation and diversification of light conditions for the enhancement of structural complexity. The implementation of an experimental patch‐network in managed broad‐leaved forests within Germany enables the standardized analysis of various aggregated and distributed treatments characterized through diverse deadwood and light structures. To monitor the dynamics of enhanced forest structure complexity as seasonal and trend components, dense time‐series from high spatial resolution imagery of Sentinel‐1 (Synthetic‐Aperture Radar, SAR) and Sentinel‐2 (multispectral) are analyzed in time‐series decomposition models (BEAST, Bayesian Estimator of Abrupt change, Seasonal change and Trend). Based on several spatial statistics and a comprehensive catalog on spectral indices, metrics from Sentinel‐1 (n = 84) and Sentinel‐2 (n = 903) are calculated at patch‐level. Metrics best identifying the treatment implementation event are assessed by change point dates and probability scores. Heterogeneity metrics of Sentinel‐1 VH and Sentinel‐2 NMDI (Normalized Multi‐band Drought Index) capture the treatment implementation event most accurately, with clear advantages for the identification of aggregated treatments. In addition, aggregated structures of downed or no deadwood can be characterized, as well as more complex standing structures, such as snags or habitat trees. To conclude, dense time‐series of complementary high spatial resolution sensors have the potential to assess various aggregated forest structure complexities, thus supporting the continuous monitoring of forest habitats and functioning over time.

Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters

The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (Rrs) for some part of the spectrum, with the most common failures (&gt;20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and Rrs uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited.

Ferroelectric Properties of Polymer-Semiconductor Hybrid Material or Composite under Optical Excitation.

Polymer-semiconductor hybrid materials or composites have been investigated with respect to their microstructure, optical, photoconductive, and ferroelectric properties. For this purpose, either CdSe quantum dots or (Cd:Zn)S microparticles were dispersed in poly(vinylidenefluoride-trifluoroethylene) solution and hot pressed to films. In both material systems, the electrical conductivity and the polarization behavior could be controlled by the intensity of the optical excitation. The simultaneous high optical transparency of the CdSe quantum-dot-based hybrid materials makes them particularly interesting for applications in the field of flexible, high-resolution sensors.

Canopy cover and microtopography control precipitation-enhanced thaw of ecosystem-protected permafrost

Northern high-latitudes are projected to get warmer and wetter, which will affect rates of permafrost thaw and mechanisms by which thaw occurs. To better understand the impact of rain, as well as other factors such as snow depth, canopy cover, and microtopography, we instrumented a degrading permafrost plateau in south-central Alaska with high-resolution soil temperature sensors. The site contains ecosystem-protected permafrost, which persists in unfavorable climates due to favorable ecologic conditions. Our study (2020–2022) captured three of the snowiest years and three of the four wettest years since the site was first studied in 2015. Average thaw rates along an across-site transect increased nine-fold from 6 ± 5 cm yr−1 (2015–2020) to 56 ± 12 cm yr−1 (2020–2022). This thaw was not uniform. Hummock locations, residing on topographic high points with relatively dense canopy, experienced only 8 ± 9 cm yr−1 of thaw, on average. Hollows, topographic low points with low canopy cover, and transition locations, which had canopy cover and elevation between hummocks and hollows, thawed 44 ± 6 cm yr−1 and 39 ± 13 cm yr−1, respectively. Mechanisms of thaw differed between these locations. Hollows had high warm-season soil moisture, which increased thermal conductivity, and deep cold-season snow coverage, which insulated soil. Transition locations thawed primarily due to thermal energy transported through subsurface taliks during individual rain events. Most increases in depth to permafrost occurred below the ∼45 cm thickness seasonally frozen layer, and therefore, expanded existing site taliks. Results highlight the importance of canopy cover and microtopography in controlling soil thermal inputs, the ability of subsurface runoff from individual rain events to trigger warming and thaw, and the acceleration of thaw caused by consecutive wet and snowy years. As northern high-latitudes become warmer and wetter, and weather events become more extreme, the importance of these controls on soil warming and thaw is likely to increase.

Ultradense Electrochemical Chips with Arrays of Nanostructured Microelectrodes to Enable Sensitive Diffusion-Limited Bioassays.

Nanostructured microelectrodes (NMEs) are an attractive alternative to yield sensitive bioassays in unprocessed samples. However, although valuable for different applications, nanoporous NMEs usually cannot boost the sensitivity of diffusion-limited analyses because of the enlarged Debye length within the nanopores, which reduces their accessibility. To circumvent this limitation, nanopore-free gold NMEs were electrodeposited from 45 μm SU-8 apertures, featuring nanoridged microspikes on a recessed surface of gold thin film while carrying interconnected crown-like and spiky structures along the edge of a SU-8 passivation layer. These structures were grown onto ultradense, vertical array chips that offer a promising strategy for translating reproducible, high-resolution, and cost-effective sensors into real-world applications. The NMEs yielded reproducible analyses, while machine learning allowed us to predict the analytical responses from NME electrodeposition data. By taking advantage of the high surface area and accessible structure of the NMEs, these structures provided a sensitivity for [Fe(CN)6]3-/4- that was 5.5× higher than that of bare WEs while also delivering a moderate antibiofouling property in undiluted human plasma. As a proof of concept, these electrodes were applied toward the fast (22 min) and simple determination of Staphylococcus aureus by monitoring the oxidation of [Fe(CN)6]4-, which acted as a cellular respiration rate redox reporter. The sensors also showed a wide dynamic range, spanning 5 orders of magnitude, and a calculated limit of detection of 0.2 CFU mL-1.