Small Spatial Resolution Research Articles

Flat lens–based subwavelength focusing and scanning enabled by Fourier translation

Abstract We demonstrate a technique for flexibly controlling subwavelength focusing and scanning, by using the Fourier translation property of a topology-preserved flat lens. The Fourier transform property of the flat lens enables converting an initial phase shift of light into a spatial displacement of its focus. The flat lens used in the technique exhibits a numerical aperture of 0.7, leading to focusing the incident light to a subwavelength scale. Based on the technique, we realize flexible control of the focal positions with arbitrary incident light, including higher-order structured light. Particularly, the presented platform can generate multifocal spots carrying optical angular momentum, with each focal spot independently controlled by the incident phase shift. This technique results in a scanning area of 10 μm × 10 μm, allowing to realize optical scanning imaging with spatial resolution up to 700 nm. This idea is able to achieve even smaller spatial resolution when using higher-numerical-aperture flat lens and can be extended to integrated scenarios with smaller dimension. The presented technique benefits potential applications such as in scanning imaging, optical manipulation, and laser lithography.

A Miniaturized Ultrawideband V-Shaped Tip E-Probe for Near-Field Measurements.

A sensitive, miniaturized, ultrawideband probe is proposed for near-field measurements. The proposed probe is based on a new V-shaped tip design and a slope structure resulting in better field distribution and impedance matching with a span bandwidth from 10 kHz up to 52 GHz, which is compatible with ultrawideband applications. The proposed E-probe fabrication process utilizes a four-layer printed circuit board (PCB) using Rogers RO4003 (tm) and RO4450 high-performance dielectrics, with εr = 3.55 and 3.3, respectively. The probe length is 40 mm with a minimum width of 4 mm, which is suitable for narrow, complex, and integrated PCBs. The passive E-probe sensitivity is -106.29 dBm and -87.48 dBm at 2 GHz and 40 GHz, respectively. It has a very small spatial resolution of 0.5 mm at 20, 25, 30, and 35 GHz. The probe is small and cheap and can diagnose electromagnetic interference (EMI) in electronic systems such as telemetry, UAVs, and avionics.

Ensuring spatial scalability with temporal-wise spatial attentive pooling for temporal action detection

Recent temporal action detection models have focused on end-to-end trainable approaches to utilize the representational power of backbone networks. Despite the advantages of end-to-end trainable methods, these models still employ a small spatial resolution (e.g., 96 × 96) due to the inefficient trade-off between computational cost and spatial resolution. In this study, we argue that a simple pooling method (e.g., adaptive average pooling) acts as a bottleneck at the spatial aggregation part, restricting representational power. To address this issue, we propose a temporal-wise spatial attentive pooling (TSAP), which alleviates the bottleneck between the backbone and the detection head using a temporal-wise attention mechanism. Our approach mitigates the inefficient trade-off between spatial resolution and computational cost, thereby enhancing spatial scalability in temporal action detection. Moreover, TSAP is adaptable to previous end-to-end approaches by simply replacing the spatial pooling part. Our experiments demonstrated the essential role of spatial aggregation, and consistent improvements are observed by incorporating TSAP into previous end-to-end methods.

Validation of Microionization Chambers in Small-field Dosimetry.

This study aims to validate the Razor Nano Chamber (RNC) and Razor Chamber (RC) dosimetric characteristics in a small field. The dosimetric parameters of the two chambers were compared. The chamber characteristics of leakage (pre and post), stability, energy dependency, dose linearity, dose rate effect, stem effect, angular dependency, ion recombination effect, and polarity effect were studied. Relative dose measurements of the percentage depth dose, profile measurement, and output factor (OF) measurements were performed for small fields. All measurements were performed in a Theratron 780E telecobalt unit and an Elekta Versa HD™ Linear Accelerator. The measured pre- and postirradiation leakage and energy dependency were within 0.5% of the acceptable limit. In the stability check, the standard error and standard error of mean were 0.047% and 0.068%, respectively, for both RNC and RC. The deviation in the angular responses of the RNC and RC ion chambers was negligible. In the ion recombination measurements, both the RNC and RC were overestimated for the 10 flattening filter-free photon beam. In small fields, there were no significant differences in the kpol values for either chamber. The RNC showed good agreement in the relative dose measurements compared to the RC. RNC and RC showed good agreement in small-field dosimetry. The RNC angular dependency and OF showed a superior response compared to the RC because of the small volume spherical shape and high spatial resolution, which gives a reduced penumbra and no volume averaging effect.

Temperature-controlled Se-S isotope fractionation during seawater mixing and sulfide precipitation in black smoker chimneys

Submarine hydrothermal venting leads to the formation of black smoker chimneys with complex internal zoning. These emerge through mineral precipitation under changing fluid conditions caused by variable degrees of fluid-seawater mixing. This study explores the potential of selenium (Se) isotopes as a novel tool to understand the enrichment processes and identify the sources of chalcophile elements in seafloor hydrothermal systems. In-situ trace element analysis of hydrothermal sulfides combined with high-precision Se and S isotope analysis of micro-domains through the wall of black smoker chimneys from the Nifonea vent field (New Hebrides back-arc, SW Pacific) revealed variations in sulfide chemistry at small spatial resolution. The δ82/76Se values (deviation of 82Se/76Se relative to NIST SRM 3149) of the sulfide micro-domains range from +0.6 to −3.7 ‰ ± 0.2 ‰ (2 s.d.) with a general decrease from the inner chalcopyrite lining towards the outer chimney wall. The highest δ82/76Se values overlap with the mean value of fresh basaltic glass from Nifonea caldera (+0.6 ± 0.4 ‰, n = 4), indicating that Se is primarily leached from the surrounding back-arc basalts. The trace element contents of sulfides revealed systematic variations between vent sites, linked to differences in fluid vapor content induced by sub-seafloor boiling, which did not cause any Se isotope fractionation. Instead, decreasing Se/Tl ratios of pyrite together with decreasing δ82/76Se values of sulfide micro-domains and calculated δ34SH2S fluid values towards the outer chimney wall are indicative of temperature-dependent Se and S isotope fractionation at 370 to <200 °C during fluid-seawater mixing within the chimney wall. This study presents Se isotopes as a new robust instrument to unravel the sources of chalcophile elements and related fluid processes in seawater-diluted hydrothermal systems, enhancing our understanding of their geochemical complexities.

Micro X-ray fluorescence device based on monocapillary ellipsoidal lens for thin film thickness measurement

X-ray fluorescence (XRF) is a crucial non-destructive technique for determining the thickness of thin films or coating materials to ensure their optimal performance. In order to further expand the application of XRF in thin film thickness analysis, we have developed an inner-wall coated elliptical monocapillary X-ray lens (EMXRL), which has a larger acceptance angle and higher utilization efficiency for light sources compared to traditional inner-wall uncoated EMXRL. In addition, we have built a laboratory micro-X-ray fluorescence (Micro-XRF) thickness measurement device based on coated EMXRL. Compared to common XRF devices, it has a smaller X-ray spot size and higher intensity gain, resulting in smaller spatial resolution and higher detection efficiency in XRF thickness measurement applications. We established a standard curve in the range from 0.85μm to 56.5μm for measuring the thickness of Cu coating on silicon using this Micro-XRF device. The experimental results show that the micro-area thickness measurement device, based on the standard curve method, can measure the thickness of Cu coatings from 0.85μm to about 30.00μm with a relative deviation of about 3%. Within the thickness range from about 30.0μm to 56.5μm, the relative deviation of the measured thickness increased and was larger than 5%. This larger relative deviation mainly resulted from the greater absorption from the thicker coating. Furthermore, We used the device to perform mapping scans on scratched areas of Cu-plated circuit board to verify its usefulness. The results show that the device has the advantages of simplicity and speed, and has potential applications in fields such as semiconductors.

Diffusion Tensor Imaging of Peripheral Nerves: Current Status and New Developments.

Diffusion tensor imaging (DTI) is an emerging technique for peripheral nerve imaging that can provide information about the microstructural organization and connectivity of these nerves and complement the information gained from anatomical magnetic resonance imaging (MRI) sequences. With DTI it is possible to reconstruct nerve pathways and visualize the three-dimensional trajectory of nerve fibers, as in nerve tractography. More importantly, DTI allows for quantitative evaluation of peripheral nerves by the calculation of several important parameters that offer insight into the functional status of a nerve. Thus DTI has a high potential to add value to the work-up of peripheral nerve pathologies, although it is more technically demanding. Peripheral nerves pose specific challenges to DTI due to their small diameter and DTI's spatial resolution, contrast, location, and inherent field inhomogeneities when imaging certain anatomical locations. Numerous efforts are underway to resolve these technical challenges and thus enable wider acceptance of DTI in peripheral nerve MRI.

Small Unmanned Aerial Vehicle LiDAR-based High Spatial Resolution Topographic Dataset in Russell Glacier, Greenland

Greenland contains a large continental glacier. The influence of glacier melting has been expanding due to global warming. Although regional monitoring based on satellite data is being conducted, the demand for local/specific variation observation has increased as rising climate temperature patterns in the polar region. In this study, a precise topographic dataset was created for Greenland’s Russell glacier using a small unmanned aerial vehicle (sUAV) onboarded LiDAR sensor. A precise digital surface model (DSM) was constructed based on LiDAR data obtained at an altitude of about 100 to 200 m, and DSM resampled to a 2 m sample distance was produced to confirm its applicability by comparing before-and-after variations. This study provides DSM data applied with a pre/post-processing used for the comparison analysis.

Validation of Island 3D-mapping Based on UAV Spatial Point Cloud Optimization: a Case Study in Dongluo Island of China

The unmanned aerial vehicle (UAV) remote sensing is of small volume, low cost, fine timeliness, and high spatial resolution, and has the special advantage on island surveying. Focus on the inaccurate elevation of non-ground point cloud without lidar device, this study explored a methodology for island three-dimensional (3D) mapping and modelling based on spatial point clouds optimization with a K-Nearest Neighbors Adaptive Inverse Distance Weighted (K-AIDW) interpolation algorithm. By classifying the UAV point clouds into ground, vegatetation, and structure, the K-AIDW algorithm was applied to optimize the elevations of non-ground point clouds (vegetation and structure) to recalculate Z values. The aerophotogrammetry result was generated based on the optimized spatial point clouds. Finally, the 3D model of Dongluo Island was reconstructed and rendered in Metashape. The accuracy evaluation result shows that the max-errors of ground control points (–0.0154 in X, 0.0305 in Y, and 0.0133 in Z) and the checkpoints (–0.091 in X, –0.176 in Y, and 0.338 in Z) can meet the error-tolerance requirements of the corresponding terrain on the 1:500 scale set by the national standard of GB/T 23236-2009 in China. It is found that the K-AIDW algorithm displayed the best Z accuracy (root-mean-square error of 0.2538) compared with IDW (0.3668) and no-optimized (1.6012), proving it is an effective methodology for improving 3D-modelling accuracy of island.

Using Facebook advertising data to describe the socio-economic situation of Syrian refugees in Lebanon.

While the fighting in the Syrian civil war has mostly stopped, an estimated 5.6 million Syrians remain living in neighboring countries. Of these, an estimated 1.5 million are sheltering in Lebanon. Ongoing efforts by organizations such as UNHCR to support the refugee population are often ineffective in reaching those most in need. According to UNHCR's 2019 Vulnerability Assessment of Syrian Refugees Report (VASyR), only 44% of the Syrian refugee families eligible for multipurpose cash assistance were provided with help, as the others were not captured in the data. In this project, we are investigating the use of non-traditional data, derived from Facebook advertising data, for population level vulnerability assessment. In a nutshell, Facebook provides advertisers with an estimate of how many of its users match certain targeting criteria, e.g., how many Facebook users currently living in Beirut are "living abroad," aged 18-34, speak Arabic, and primarily use an iOS device. We evaluate the use of such audience estimates to describe the spatial variation in the socioeconomic situation of Syrian refugees across Lebanon. Using data from VASyR as ground truth, we find that iOS device usage explains 90% of the out-of-sample variance in poverty across the Lebanese governorates. However, evaluating predictions at a smaller spatial resolution also indicate limits related to sparsity, as Facebook, for privacy reasons, does not provide audience estimates for fewer than 1,000 users. Furthermore, comparing the population distribution by age and gender of Facebook users with that of the Syrian refugees from VASyR suggests an under-representation of Syrian women on the social media platform. This work adds to growing body of literature demonstrating the value of anonymous and aggregate Facebook advertising data for analysing large-scale humanitarian crises and migration events.

Comparison of Magneto-Optical Faraday Effect Method and Magnetic Camera for Imaging the 2-D Static Leakage Magnetic Flux Density

Magnetic flux leaks around the defect on the magnetized ferromagnetic specimen. The magnetic flux leakage testing (MFLT) use the magnetic sensor to measure the leaked magnetic flux density (LMFD). The high sensitivity of sensor need to increase the probability of detection (POD). Also the small size and high spatial resolution of sensor need to increase POD and to sizing defect. Uniform and small lift-off are another necessary to increase POD and sizing ability. Also, the real time imaging of magnetic flux density can increase the inspection time. So the period of planned preventive maintenance could reduce. Two dimensional semiconductor-based magnetic sensor array with high spatial resolution satisfies these requirements. Projection of magnetic flux density on a sensor itself would be another method. We compare the magnetic camera and the magneto-optical Faraday effect (MOE) for MFLT. Magnetic camera uses the two dimensional Hall sensor arrays to imaging the LMFD. And the MOE project LMFD. The plane of polarized light rotates inside of magneto-optical film (MOF). The Faraday rotation angle proportional to direction and intensity of magnetic flux density. Sensitivity, spatial resolution, magnetic hysteresis, repeatability, full-scaled operational range compared. Also shape of target specimen compares too.

Advances in orderly assembly of fluorescent small molecular probes and related bioimaging applications

<p indent="0mm">Fluorescence imaging technology based on small molecule probes has the advantages of high sensitivity, simple operation, fast response speed, small sample damage and high spatial and temporal resolution, and is a powerful tool for obtaining biochemical information at the cellular and living level. Among them, the orderly assembled fluorescent small molecule probes have better imaging performance than traditional small-molecule fluorescent probes (such as strong anti-diffusion ability, high imaging contrast, and good photostability due to their unique assembly methods and recognition modes). This article summarizes the applications of several common organic dye aggregates and corresponding small molecule fluorescent probes in the field of bioimaging. Subsequently, the design and optimization strategies of organic dye assemblies represented by HPQ and its derivatives with hydrogen-bond-driven ordered assembly and their applications in the biomedical field are highlighted. Finally, the problems existing in the design and application of ordered assembled fluorescent small molecule probes are discussed, which provides ideas for the design of new ordered assembled fluorescent small molecule probes.

Grazing Treatment Influences Recovery of Mesic Grassland from Seasonal Drought: An Assessment Using Unmanned Aerial Vehicle−Enabled Remote Sensing

Drought is frequent on grasslands, often creating patches with reduced coverage of green vegetation that influence the trajectory of vegetation recovery following drought. Limiting the extent and sizes of low-cover patches thus should be a target of adaptive management on grazed grasslands, but quantifying patch sizes requires measurements at small spatial resolution. We used airborne (unmanned aerial vehicle) estimates of green plant cover at 8-cm spatial resolution to quantify the aerial extent and patch sizes of mesic grassland in central Texas, United States in which green plant cover was eliminated during a growing-season (seasonal) drought in each of two grazing treatments (rotational, continuous). Remote measurements of the normalized difference vegetation index (NDVI), indicative of plant biomass, were then used to compare postdrought recovery of vegetation in patches that lacked versus retained green plant cover during drought (not green vs. green). Below-average precipitation from April through July 2018 (41% of the mean) eliminated green plant cover from 5%−12% of grassland area. Fractional coverage of the not-green area was greater by a factor of 2 under continuous grazing. Formerly not-green patches exhibited full recovery in seasonal mean values of NDVI during the year following drought in both grazing treatments. NDVI recovery was facilitated by a marked increase in abundance of early-season annual species in pasture dominated by summer-maturing perennial grasses. Spring NDVI of formerly not-green patches was greatest under continuous grazing during 2 post-drought yr, implying that annual species limited colonization of perennial vegetation more under continuous than rotational grazing. Our results imply that managers can facilitate drought recovery of perennial vegetation in mesic grassland by 1) adaptively managing grazing to reduce patch sizes of grassland that suffer complete loss of green plant cover and 2) employing rotational grazing to eliminate grazing pressure for several consecutive months annually following drought.

Effect of pulse group velocity on charge loading in laser wakefield acceleration

Total charge and energy evaluations for the electron beams generated in the laser wakefield acceleration (LWFA) is the primary step in the determination of the required laser and target parameters. Particle-in-cell (PIC) simulation is an efficient numerical tool that can provide such evaluations unless the effect of numerical dispersion is not diminished. The numerical dispersion, which is specific for the PIC modeling, affects not only the dephasing lengths in LWFA but also the total amount of the self-injected electrons. A numerical error of the order of 10−4−10−3 in the calculation of the speed of the driving laser pulse results in a significant error in the total injected charge, energy gain and other critical parameters of the accelerated electron bunches. In the standard numerical approach, the numerical correction in the speed of the laser pulse either requires infinitely small spatial grid resolution (which needs large computation platform) or force to compromise with the numerical accuracy. Here, we present a simple and easy to implement numerical scheme to suppress the numerical dispersion of the electromagnetic pulse in PIC simulations even with a modest spatial resolution, and without any special treatments to the core structure of the numerical algorithm. Evaluated charges of the self-injected electron bunches become essentially lower owing to the better calculations of the wake phase velocity.

Probabilistic Machine Learning with Low-Cost Sensor Networks for Occupational Exposure Assessment and Industrial Hygiene Decision Making.

Occupational exposure assessments are dominated by small sample sizes and low spatial and temporal resolution with a focus on conducting Occupational Safety and Health Administration regulatory compliance sampling. However, this style of exposure assessment is likely to underestimate true exposures and their variability in sampled areas, and entirely fail to characterize exposures in unsampled areas. The American Industrial Hygiene Association (AIHA) has developed a more realistic system of exposure ratings based on estimating the 95th percentiles of the exposures that can be used to better represent exposure uncertainty and exposure variability for decision-making; however, the ratings can still fail to capture realistic exposure with small sample sizes. Therefore, low-cost sensor networks consisting of numerous lower-quality sensors have been used to measure occupational exposures at a high spatiotemporal scale. However, the sensors must be calibrated in the laboratory or field to a reference standard. Using data from carbon monoxide (CO) sensors deployed in a heavy equipment manufacturing facility for eight months from August 2017 to March 2018, we demonstrate that machine learning with probabilistic gradient boosted decision trees (GBDT) can model raw sensor readings to reference data highly accurately, entirely removing the need for laboratory calibration. Further, we indicate how the machine learning models can produce probabilistic hazard maps of the manufacturing floor, creating a visual tool for assessing facility-wide exposures. Additionally, the ability to have a fully modeled prediction distribution for each measurement enables the use of the AIHA exposure ratings, which provide an enhanced industrial decision-making framework as opposed to simply determining if a small number of measurements were above or below a pertinent occupational exposure limit. Lastly, we show how a probabilistic modeling exposure assessment with high spatiotemporal resolution data can prevent exposure misclassifications associated with traditional models that rely exclusively on mean or point predictions.

Inter-annual variation in winter distribution affects individual seabird contamination with mercury

Migratory seabirds are exposed to various pollutants throughout their annual cycle. Among them, mercury (Hg) is of particular concern given its large impact on animal health. Recent studies suggest that winter is a critical period for seabirds when contamination by Hg can be higher than at other times of year. However, individuals within and between species can have different migration strategies and winter distributions that could affect their exposure. Here, we combined multi-year individual tracking data and Hg measurements from 6 Arctic seabird species. We investigated whether inter-annual variations in individual winter contamination with Hg was related to seabird fidelity to a wintering site over years. First, our results show that Hg concentrations above the toxicity threshold (i.e. 5 µg g-1dry weight in feathers) were observed in variable proportions according to species (from 2% of northern fulmars to 37% of Brünnich’s guillemots). Second, individuals with high fidelity to a wintering ground had more similar Hg concentrations among years compared to individuals with low fidelity, suggesting an effect of their migratory strategy on Hg contamination. Further, we found that the directional change in wintering areas among years influenced seabird Hg contamination, highlighting an additional effect of seabirds’ winter distribution. More specifically, individuals migrating to the northwest direction of a previous wintering ground tended to be more contaminated compared to those moving to eastern directions. These results confirm spatial differences in Hg concentration throughout the North Atlantic-Arctic and an east-west gradient increase in Hg concentrations. Verifying this trend will require more large-scale ecotoxicological studies at smaller spatial resolution.

LayerCAM: Exploring Hierarchical Class Activation Maps for Localization.

The class activation maps are generated from the final convolutional layer of CNN. They can highlight discriminative object regions for the class of interest. These discovered object regions have been widely used for weakly-supervised tasks. However, due to the small spatial resolution of the final convolutional layer, such class activation maps often locate coarse regions of the target objects, limiting the performance of weakly-supervised tasks that need pixel-accurate object locations. Thus, we aim to generate more fine-grained object localization information from the class activation maps to locate the target objects more accurately. In this paper, by rethinking the relationships between the feature maps and their corresponding gradients, we propose a simple yet effective method, called LayerCAM. It can produce reliable class activation maps for different layers of CNN. This property enables us to collect object localization information from coarse (rough spatial localization) to fine (precise fine-grained details) levels. We further integrate them into a high-quality class activation map, where the object-related pixels can be better highlighted. To evaluate the quality of the class activation maps produced by LayerCAM, we apply them to weakly-supervised object localization and semantic segmentation. Experiments demonstrate that the class activation maps generated by our method are more effective and reliable than those by the existing attention methods. The code will be made publicly available.

Construction of micro-nano electrochemical sensing interfaces for real-time monitoring of single cells

<p indent="0mm">Cell is the basic structural and biological unit of organisms. Conventional methods for detecting population cells often conceal individual differences between cells. Therefore, efficient single-cell analysis strategies are urgently needed for in-depth studies of cell life activities, revealing the mechanism of disease developments, and promoting individualized diagnosis and treatment. Micro-nano electrochemical sensors play an important role in real-time dynamic monitoring of single cells, due to its small size, high temporal and spatial resolution. At present, micro-nano electrochemical sensors have made important progress in the electrode preparation, high-performance sensing interface construction, theoretical analysis and breakthroughs have been made in single-cell real-time monitoring and related mechanism research. However, complex internal environment, low concentration of active molecules, and dynamic changes in time and space put forward higher requirements on the sensitivity and selectivity of micro-nano electrochemical sensors for single cell detection. Our research group has been engaged in the construction of micro-nano electrochemical sensing interface and its application in real-time dynamic monitoring of single cells and subcellular structures. This feature article mainly introduces our research progress in this field in the past <sc>10 years,</sc> and discusses challenges and opportunities in the future.

100\u2009pT/cm single-point MEMS magnetic gradiometer from a commercial accelerometer

Magnetic sensing is present in our everyday interactions with consumer electronics and demonstrates the potential for the measurement of extremely weak biomagnetic fields, such as those of the heart and brain. In this work, we leverage the many benefits of microelectromechanical system (MEMS) devices to fabricate a small, low-power, and inexpensive sensor whose resolution is in the range of biomagnetic fields. At present, biomagnetic fields are measured only by expensive mechanisms such as optical pumping and superconducting quantum interference devices (SQUIDs), suggesting a large opportunity for MEMS technology in this work. The prototype fabrication is achieved by assembling micro-objects, including a permanent micromagnet, onto a postrelease commercial MEMS accelerometer using a pick-and-place technique. With this system, we demonstrate a room-temperature MEMS magnetic gradiometer. In air, the sensor’s response is linear, with a resolution of 1.1 nT cm−1, spans over 3 decades of dynamic range to 4.6 µT cm−1, and is capable of off-resonance measurements at low frequencies. In a 1 mTorr vacuum with 20 dB magnetic shielding, the sensor achieves a 100 pT cm−1 resolution at resonance. This resolution represents a 30-fold improvement compared with that of MEMS magnetometer technology and a 1000-fold improvement compared with that of MEMS gradiometer technology. The sensor is capable of a small spatial resolution with a magnetic sensing element of 0.25 mm along its sensitive axis, a >4-fold improvement compared with that of MEMS gradiometer technology. The calculated noise floor of this platform is 110 fT cm−1 Hz−1/2, and thus, these devices hold promise for both magnetocardiography (MCG) and magnetoencephalography (MEG) applications.

Characterization of a microSilicon diode detector for small-field photon beam dosimetry.

This study characterized a new unshielded diode detector, the microSilicon (model 60023), for small-field photon beam dosimetry by evaluating the photon beams generated by a TrueBeam STx and a CyberKnife. Temperature dependence was evaluated by irradiating photons and increasing the water temperature from 11.5 to 31.3°C. For Diode E, microSilicon, microDiamond and EDGE detectors, dose linearity, dose rate dependence, energy dependence, percent-depth-dose (PDD), beam profiles and detector output factor (OFdet) were evaluated. The OFdet of the microSilicon detector was compared to the field output factors of the other detectors. The microSilicon exhibited small temperature dependence within 0.4%, although the Diode E showed a linear variation with a ratio of 0.26%/°C. The Diode E and EDGE detectors showed positive correlations between the detector reading and dose rate, whereas the microSilicon showed a stable response within 0.11%. The Diode E and microSilicon demonstrated negative correlations with the beam energy. The OFdet of microSilicon was the smallest among all the detectors. The maximum differences between the OFdet of microSilicon and the field output factors of microDiamond were 2.3 and 1.6% for 5 × 5 mm2 TrueBeam and 5 mm φ CyberKnife beams, respectively. The PDD data exhibited small variations in the dose fall-off region. The microSilicon and microDiamond detectors yielded similar penumbra widths, whereas the other detectors showed steeper penumbra profiles. The microSilicon demonstrated favorable characteristics including small temperature and dose rate dependence as well as the small spatial resolution and output factors suitable for small field dosimetry.