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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A Coil Detection System for Dynamic Wireless Charging of Electric Vehicle

Application of wireless power transfer while EV is in motion can significantly reduce the battery storage capacity. A major challenge in implementation of dynamic wireless power transfer (DWPT) is automatic detection of EV to avoid loss in efficiency and alleviate any safety concerns. This paper proposes a novel coil detection method for segmented DWPT. Detection of the EV ahead of its arrival will initiate energizing of the transmitter buried inside the road to enable just-in-time transfer of power. At low speeds, communication can be a reliable method to power up the transmitter coil. However, at high speeds on highways, communication latency time for the detection of an EV is long and hence impractical. This paper proposes a low cost and low power EV detection system based on a novel orthogonal coil arrangement to detect EVs traveling at high speeds. Here, the proposed detection system was tested on a laboratory scale prototype for verification purpose. For high speed verification, simulation in PLECS was conducted to test the functionality of the proposed system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Application of automated iterative target detection for standoff hyperspectral imaging

The utility of hyperspectral imaging (HSI) has been well established for a wide array of applications but has generated a need for automated screening of high volumes of large HSI cubes. We report two important automated algorithms for more efficient standoff processing: atmospheric correction and target detection. The atmospheric correction method is based on a fast asymmetric least squares approach that is applied on a pixel-by-pixel basis. Here, the correction can be applied to entire images without manually identifying regions of interest and utilizes only in-scene information, no ancillary modeling of the atmosphere is required. An iterative target detection approach is also introduced which demonstrates faster speeds relative to moving window approaches. The target detection algorithm classifies each pixel as true target detections, near target detections, clutter, and no-calls. The algorithms were tested on forty images of twenty-two solid mineral targets placed at a 14-meter standoff distance allowing general observations on expected detection performance for a variety of minerals. In addition to identifying anomalous pixels, the inclusion of “no-calls” reduced the number of false detections significantly.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms

Graph representations are pervasive in scientific and social computing. They serve as vital tools to model the interplay among different interacting entities. In this paper, we visit the problem of community detection, which is one of the most widely used graph operations toward scientific discovery. Community detection refers to the process of identifying tightly-knit subgroups of vertices in a large graph. These sub-groups (or communities) represent vertices that are tied together through common structure or function. Identification of communities could help in understanding the modular organization of complex networks. However, owing to large data sizes and high computational costs, performing community detection at scale has become increasingly challenging. Here, we present a detailed review and analysis of some of the leading computational methods and implementations developed for executing community detection on modern day multicore and manycore architectures. Our goals are to: a) define the problem of community detection and highlight its scientific significance; b) relate to challenges in parallelizing the operation on modern day architectures; c) provide a detailed report and taxonomical classification of the approaches that have been designed for various architectures; and d) finally, provide insights into the strengths and suitability of different architectures for community detection, and a preview into the future trends of the area. It is our hope that this detailed treatment of community detection on parallel architectures can serve as an exemplar study for extending the application of modern day multicore and manycore architectures to other complex graph applications.

community detection, multithreaded platforms↗
DOE OSTI2016

Fiber optic quench detection via optimized Rayleigh Scattering in high-field YBCO accelerator magnets

Yttrium barium copper oxide (YBCO) coated conductors are known for their ability to operate in the superconducting state at relatively high temperatures, even above the boiling point of liquid nitrogen (77 K). When these same conductors are operated at lower temperatures, they are able to operate in much higher magnetic fields than traditional superconductors like NiTi or Nb 3 Sn. Thus, YBCO superconducting magnets are one of the primary options for generating the high magnetic fields needed for future high energy physics devices. Due to slow quench propagation, quench detection remains one of the primary limitations to YBCO magnets. Fiber optic sensing, based upon Rayleigh scattering, has the potential for spatial resolution approaching the wavelength of light, or very fast temporal resolution at low spatial resolution, and a continuum of combinations in between. This project has studied, theoretically and experimentally, YBCO magnets and Rayleigh scattering quench detection systems to demonstrate feasibility of the systems for YBCO quench protection systems. Under this grant an experimentally validated 3D quench propagation model was used to accurately define the acceptable range of spatial and temporal resolutions for effective quench detection in YBCO magnets and to evaluate present-day and potentially improved YBCO conductors. The data volume and speed requirements for quench detection via Rayleigh scattering required the development of a high performance fiber optic based quench detection/data acquisition system and its integration with an existing voltage tap/thermo-couple based system. In this project, optical fibers are tightly co-wound into YBCO magnet coils, with the fiber on top of the conductor as turn-to-turn insulation. Local changes in the temperature or strain of the conductor are sensed by the optical fiber, which is in close thermal and mechanical contact with the conductor. Intrinsic imperfections in the fiber reflect Rayleigh backscattered laser signals that are shifted by the changes in the fiber that are induced by a local change in the YBCO temperature or strain. One goal of this project was to show that modern technology can be used to interrogate the signals from a (very expensive) YBCO magnet to detect an impending quench in time to protect it from self-destruction. The results show that Rayleigh-backscattering interrogated optical fibers (RIOF) have significant advantages over traditional techniques, including very high spatial resolution and the ability to detect a hot-spot well before the peak local temperature becomes so high that the conductor can be damaged. RIOF quench detection is intrinsically faster than voltage taps, and this intrinsic advantage is greater as the coil size and/or current margin increases. We describe the development and testing program performed under the grant.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Collaborative Research and Development Program on Explosive Detection Technology

In September 2013, at the 6th Permanent Coordinating Group Meeting between the U.S Department of Energy (DOE) and the French Institut de Radioprotection et de Sureté Nucléaire (IRSN), France expressed an interest in bilateral cooperation with the United States because its newly revised regulations that require enhanced explosives detection capabilities at nuclear and radiological facilities. In the ensuing years, PNNL (DOE/NNSA) and IRSN sought to identify an area of collaboration within explosives detection that would leverage the specific technical strengths of each organization. Based upon awareness of each other’s technical acumen gleaned from the scientific literature on explosives detection, it was clear that specific organizations within each nation could provide the needed expertise to enable enhancement of explosives detection through a collaborative development effort. The French lnstitut Saint-Louis was determined to be an optimal partner for IRSN to develop a collaboration with DOE/NNSA using PNNL’s detection team in this effort. Thus, the dialog was started between the technical experts at each organization to define where complementary expertise in explosives detection could be best leveraged. The technical plans and objectives of this project were sound with promising results. In the end, the joint action sheet was not implemented. The challenge with executing the project was in the complexity of getting a signed agreement between DOE, IRSN and ISL. Most of the obstacles surrounded the ability to protect intellectual property and obtain an agreement which included all of the parties. At a high level, this report documents the interactions and attempt to develop a cooperative framework for explosives detection development from FY 2014 through FY 2020.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Barriers to Broader Utilization of Fault Detection Technologies for Improving Residential HVAC Equipment Efficiency

Faults in residential heating, ventilating, and air conditioning (HVAC) equipment may occur due to poor installation practices or develop over time, and these faults can negatively impact system efficiency, thermal comfort, and equipment lifespan. Automated fault detection and diagnostic (AFDD) technologies identify energy wasting HVAC faults, such as low indoor airflow and improper refrigerant charge, and guide technicians in improving system efficiency. For residential HVAC, AFDD consists of a range of fault detecting and diagnostic capabilities, sensor configurations, and target applications. AFDD technology can either be permanently installed by the original equipment manufacturer (OEM) using embedded sensors or as an add-on product either during or after installation. Additionally, several advanced installation tools and refrigerant gauge sets include AFDD features for temporary use during equipment installation and tune-ups. Some technologies can detect a fault but have limited diagnostic capabilities. For example, a single-point measurement from the home's thermostat or energy monitor can provide certain fault detection capability by analyzing the equipment runtime or energy consumption. These technologies, though limited at determining the cause of a given fault, may have significant energy savings potential due to their low cost and prevalence in the residential HVAC market. Despite the potential benefits, fault detection technologies face many technical and market barriers preventing broad adoption. Beyond the cost barriers due to the added sensor requirements and technology development, fault detection technologies face many implementation and adoption barriers such as installer training, customer awareness, standardized communication protocols, and methods of test for evaluating accuracy. The purpose of this whitepaper is to characterize market and technical barriers impeding broader utilization of fault detection technology for residential HVAC energy efficiency applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Evaluating the Effectiveness of a Detection and Deterrent System in Reducing Golden Eagle Fatalities at Operational Wind Facilities

The Renewable Energy Wildlife Institute (REWI) was appointed as the prime awardee of DOE award number DE-EE0007883 to lead a team of scientists, wind developers, and technology manufacturers toward the overarching goal of evaluating the effectiveness of the current DTBird system in minimizing the risk of golden eagles (Aquila chrysaetos) and other large soaring raptors from approaching the rotor-swept zone (RSZ) of operating wind turbines. As part of this goal, the team set out to 1) quantify the expected reduction in collision risk for golden eagles from operation of the detection and deterrence modules in a manner that supports the approach used by the U.S. Fish and Wildlife Service (USFWS) to assess and credit facility operators for their efforts to minimize predicted collision fatalities and 2) provide information to help improve the technology to maximize its effectiveness. DTBird is an automated detection and audio deterrent system created by the Spanish company Liquen, designed to discourage birds from entering the RSZ of spinning wind turbines. The system uses cameras to automatically detect airborne targets of interest, records each such event in an online database, and triggers a warning signal (loud sound) if the tracked object has moved close to the turbine. If the object moves even closer to the RSZ, a more aggressive dissuasion signal is broadcast. To meet our objectives, the team conducted a two-year experiment at the Goodnoe Hills wind facility in Washington state, in which 14 turbines were outfitted with DTBird units. Daily, each DTBird-equipped turbine was randomly assigned to a control or treatment group. Treatment turbines operated with DTBird running as intended—broadcasting warning or deterrent signals when DTBird detected a target within range. On control turbines, no sound signals were broadcast if a moving target triggered the DTBird system. The team also flew unmanned aerial vehicles (UAVs) designed to coarsely mimic the general size, weight, and coloration of golden eagles in programmed flight transects across DTBird detection ranges to quantify DTBird’s ability to detect intended targets and to evaluate factors that influence the probability of detection and DTBird’s response distances. Additionally, the team evaluated the behavioral responses of in situ eagles exposed to spinning turbines alone (visual and sound influences) versus spinning turbines plus broadcasted DTBird audio deterrents, to estimate the effectiveness of deterrence by the DTBird system. The data and results from these investigations were combined with those from a pilot study conducted at the Manzana Wind Power Project in California to better evaluate DTBird’s effectiveness across different landscapes.

17 WIND ENERGY↗
DOE OSTI2024

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Assessment of Envelope- and Machine Learning-Based Electrical Fault Type Detection Algorithms for Electrical Distribution Grids

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Sensing Technology Survey for Obstacle Detection in Vegetation

This study reviews obstacle detection technologies in vegetation for autonomous vehicles or robots. Autonomous vehicles used in agriculture and as lawn mowers face many environmental obstacles that are difficult to recognize for the vehicle sensor. This review provides information on choosing appropriate sensors to detect obstacles through vegetation, based on experiments carried out in different agricultural fields. The experimental setup from the literature consists of sensors placed in front of obstacles, including a thermal camera; red, green, blue (RGB) camera; 360° camera; light detection and ranging (LiDAR); and radar. These sensors were used either in combination or single-handedly on agricultural vehicles to detect objects hidden inside the agricultural field. The thermal camera successfully detected hidden objects, such as barrels, human mannequins, and humans, as did LiDAR in one experiment. The RGB camera and stereo camera were less efficient at detecting hidden objects compared with protruding objects. Radar detects hidden objects easily but lacks resolution. Hyperspectral sensing systems can identify and classify objects, but they consume a lot of storage. To obtain clearer and more robust data of hidden objects in vegetation and extreme weather conditions, further experiments should be performed for various climatic conditions combining active and passive sensors.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

GenAI-Based Digital Twins Aided Data Augmentation Increases Accuracy in Real-Time Cokurtosis-Based Anomaly Detection of Wearable Data

Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, including heart rate and activity, we developed a framework for the early detection of infection in individuals. Despite the availability of data from recent pandemics, substantial gaps remain in data collection, hindering method development. To bridge this gap, we utilized Wasserstein Generative Adversarial Networks (WGANs) to generate realistic synthetic wearable data, augmenting our dataset for training. Subsequently, we use these augmented datasets to implement a cokurtosis-based technique for anomaly detection in multivariate time-series data. Our approach includes a comprehensive assessment of uncertainties in synthetic data compared to the actual data upon which it was modeled, as well as the uncertainty associated with fine-tuning anomaly detection thresholds in physiological measurements. Through our work, we present an enhanced method for early anomaly detection in multivariate datasets, with promising applications in healthcare and beyond. This framework could revolutionize early detection strategies and significantly impact public health response efforts in future pandemics.

Data-Driven Digital Twins↗
DOE OSTI2016

IMPLICATION OF THE NON-DETECTION OF GZK NEUTRINOS

The IceCube telescope has detected diffuse neutrino emission up to a deposited energy of 2.6 PeV. Neutrinos with higher energies are expected from the Greisen Ztsepin Kuzmin (GZK) effect, namely the interaction of ultrahigh-energy cosmic rays (UHECRs) with the cosmic microwave background (CMB) and the extragalactic background light (EBL), but have not yet been detected. Models for GZK neutrinos vary greatly due to different assumptions on the UHECR elemental composition, as well as on the cosmological evolution of their sources and of the EBL. We show that the high ratio of EeV to PeV neutrinos in essentially all GZK models excludes the currently detected PeV neutrinos from being due to the GZK effect, because many additional higher-energy neutrinos should have been detected but were not. The non-detection of GZK neutrinos, despite more than essentially 1800 observing days, already rules out at 95% confidence all of the models that predict rates of 0.6 neutrinos yr{sup −1} or more. The non-detection is further used here to quantify the confidence at which classes of GZK models can be ruled out, and to compute the additional IceCube observing time required in order to rule them out with 95% confidence, if no detection is made. Finally, the number of GZK neutrinos expected from various classes of models in the future neutrino telescopes ARA and KM3NeT is estimated.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Characterizing the youngest Herschel-detected protostars. II. Molecular outflows from the millimeter and the far-infrared

We present Combined Array for Research in Millimeter-wave Astronomy (CARMA) CO (J=1→0) observations and Herschel PACS spectroscopy, characterizing the outflow properties toward extremely young and deeply embedded protostars in the Orion molecular clouds. The sample comprises a subset of the Orion protostars known as the PACS Bright Red Sources (PBRS; Stutz et al.). We observed 14 PBRS with CARMA and 8 of these 14 with Herschel, acquiring full spectral scans from 55 to 200 μm. Outflows are detected in CO (J=1→0) from 8 of 14 PBRS, with two additional tentative detections; outflows are also detected from the outbursting protostar HOPS 223 (V2775 Ori) and the Class I protostar HOPS 68. The outflows have a range of morphologies; some are spatially compact, <10,000 au in extent, while others extend beyond the primary beam. The outflow velocities and morphologies are consistent with being dominated by intermediate inclination angles (80° ≥ i ≥ 20°). This confirms the interpretation of the very red 24–70 μm colors of the PBRS as a signpost of high envelope densities, with only one (possibly two) cases of the red colors resulting from edge-on inclinations. We detect high-J (J {sub up} > 13) CO lines and/or H{sub 2}O lines from 5 of 8 PBRS and only for those with detected CO outflows. The far-infrared CO rotation temperatures of the detected PBRS are marginally colder (∼230 K) than those observed for most protostars (∼300 K), and only one of these five PBRS has detected [O i] 63 μm emission. The high envelope densities could be obscuring some [O i] emission and cause a ∼20 K reduction to the CO rotation temperatures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

14 SOLAR ENERGY↗
NASA NTRS2011

Daytime Water Detection Based on Sky Reflections

Robust water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation. This is particularly true in wide-open areas where water can collect in naturally occurring terrain depressions during periods of heavy precipitation and form large water bodies. One of the properties of water useful for detecting it is that its surface acts as a horizontal mirror at large incidence angles. Water bodies can be indirectly detected by detecting reflections of the sky below the horizon in color imagery. The Jet Propulsion Laboratory (JPL) has implemented a water detector based on sky reflections that geometrically locates the pixel in the sky that is reflecting on a candidate water pixel on the ground and predicts if the ground pixel is water based on color similarity and local terrain features. This software detects water bodies in wide-open areas on cross-country terrain at mid- to far-range using imagery acquired from a forward-looking stereo pair of color cameras mounted on a terrestrial UGV. In three test sequences approaching a pond under a clear, overcast, and cloudy sky, the true positive detection rate was 100% when the UGV was beyond 7 meters of the water's leading edge and the largest false positive detection rate was 0.58%. The sky reflection based water detector has been integrated on an experimental unmanned vehicle and field tested at Ft. Indiantown Gap, PA, USA.

sky reflections↗
DOE OSTI2015

Statistical Techniques For Real-time Anomaly Detection Using Spark Over Multi-source VMware Performance Data

Anomaly detection refers to the identi cation of an irregular or unusual pat- tern which deviates from what is standard, normal, or expected. Such deviated patterns typically correspond to samples of interest and are assigned different labels in different domains, such as outliers, anomalies, exceptions, or malware. Detecting anomalies in fast, voluminous streams of data is a formidable chal- lenge. This paper presents a novel, generic, real-time distributed anomaly detection framework for heterogeneous streaming data where anomalies appear as a group. We have developed a distributed statistical approach to build a model and later use it to detect anomaly. As a case study, we investigate group anomaly de- tection for a VMware-based cloud data center, which maintains a large number of virtual machines (VMs). We have built our framework using Apache Spark to get higher throughput and lower data processing time on streaming data. We have developed a window-based statistical anomaly detection technique to detect anomalies that appear sporadically. We then relaxed this constraint with higher accuracy by implementing a cluster-based technique to detect sporadic and continuous anomalies. We conclude that our cluster-based technique out- performs other statistical techniques with higher accuracy and lower processing time.

Apache Spark↗
DOE OSTI2025

Integrating Crack Detection and Pipe Shape Optimization for Enhanced Sewage System Durability

Crack detection in underground reinforced concrete pipes has been essential in determining the state of stormwater infrastructure. Detection models have been implemented for detecting cracks and other defects in pipes using CCTV footage for stormwater drainage systems. In addition, Finite element models have been used to determine optimum shapes and pipe thickness for different boundary conditions such as header pipes in power plants. The concept of shape optimization emerges as a crucial factor in power plant design and operation, with the potential to maximize performance while minimizing the use of materials. Shape optimization not only enhances efficiency but also contributes to reducing the environmental footprint. This paper discusses the integration of both topics by using the cracks detected in underground pipes as boundary conditions for shape optimization of the pipes. A machine learning model has been developed which uses limited data for training and outlines the location of detected cracks. A shape optimization methodology is proposed in which ANSYS modules are used to analyze fluid flow and then optimize the shape of the pipe. The crack detection model developed has been applied to a crack detected in lab setting and machine learning model used has an accuracy of 98% using a random forest algorithm.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Liquid Crystal‐Based Detection of Antigens with ELISA Sensitivity

Abstract A highly sensitive label‐free liquid crystal (LC)‐based technique is presented for detecting Immunoglobulin G (IgG) antigens used to uncover viral infections. The effectiveness, sensitivity, and selectivity of this detection method is demonstrated with goat IgG antigen at concentrations as low as 100 pg ml −1 , which is comparable to the sensitivity of the current enzyme‐linked immunosorbent assay (ELISA). The sensor is fabricated by decorating a transmission electron microscopy grid immobilized glass surfaces with antibodies; the target antigen is detected by a liquid crystal suspended onto the grid. This is different from previous methods where the antigen is detected either at the LC‐aqueous interface or in an LC sandwich cell with an antibody/antigen‐decorated substrate. This new approach has advantages such as easy sample preparation, higher sensitivity, and better storage capabilities. Binding the target antigen to the antibody results in a reorientation of the LC director that is detected optically. In addition to demonstrating the sensitivity, the physical principle of the detection is also discussed. This technique may apply to detect virtually any antigen of interest.

36 MATERIALS SCIENCE↗