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A venting system in accordance with the present disclosure includes a ventilation fan and one or more sensors coupled to the ventilation fan. The ventilation fan is positioned to vent air from an indoor environment. The sensors are positioned to monitor conditions in the indoor environment and selectively operate the ventilation fan to condition air in the indoor environment.
In data-driven automated fault detection and diagnostics (AFDD) modeling for building energy systems, feature engineering is a critical process of extracting information from high-dimensional and noisy sensor measurement and turning it into informative and representative inputs or features for data-driven modeling. However, few studies specifically discuss the feature engineering, especially the interactions between feature extraction and feature selection in whole-building AFDD. We developed a systematic feature extraction and selection framework for whole-building AFDD. In this framework, features are aggressively extracted from raw sensor data using statistical feature extraction techniques with various window sizes and statistics. With many features extracted, a hybrid feature selection algorithm that combines the filter and wrapper method then selects the best feature set. The framework considers diversity in the duration of fault behavior among fault types in whole-building AFDD, thus achieving high model generalization. We implemented our developed framework in a virtual testbed calibrated with measured data from Oak Ridge National Laboratory's Flexible Research Platform designed to mimic the operation of a typical small commercial building. The AFDD model is trained by the simulation data generated from the virtual testbed. The results show that (1) the developed framework improves the generalization of the AFDD model by 10.7% compared with literature-reported feature extraction and selection methods and (2) features with diverse window sizes and statistics are selected, providing insight into physical systems beyond the current understanding of buildings and faults and improving the detection and diagnostics of multiple fault types.
Detection of radiological iodine gas after nuclear accidents or in nuclear fuel reprocessing is necessary for the safety of human life and the environment. The development of sensors for the detection of iodine benefits from the incorporation of nanoporous materials with high selectivity for I 2 from common competing gases in air. Silver mordenite zeolite (Ag-MOR) is widely-used material for capture of gaseous iodine (I 2 ). In this paper, thin film zeolite coatings were applied to Pt interdigitated electrodes (IEDs) to fabricate iodine gas sensors with direct electrical readout responses. Correlations between occluded ion, exposure to iodine gas, resultant AgI nanoparticle polymorphs and location in zeolite with resultant impedance spectroscopy (IS) properties are described. Furthermore, IS is leveraged to elucidate the changes in charge conduction pathways as determined by the cation-zeolite film incorporated in the sensor. Silver mordenite reveals a significant change in impedance upon exposure to gaseous I 2 at 70 °C, and the magnitude and direction of the response is dependent on whether the Ag + -mordenite is reduced (Ag 0 ) before I 2 exposure. An equivalent circuit model is developed to describe the movement of charge along the surface and through the pores of the mordenite grains. Relative changes in the impedance of these conduction pathways are related to the chemical changes from Ag + or Ag 0 to resultant AgI polymorph phase. Lastly, together, these results inform design of a compact Ag-mordenite sensor for direct electrical detection of gaseous I 2 .
Molybdenum disulphide (MoS{sub 2}) is one of the most attractive two dimensional materials other than graphene, and the exceptional properties make it a promising candidate for bio/chemical sensing. Nevertheless, intrinsic properties and sensing performances of MoS{sub 2} are easily masked by the presence of the Schottky barrier (SB) at source/drain electrodes, and its impact on MoS{sub 2} sensors remains unclear. Here, we systematically investigated the influence of the SB on MoS{sub 2} sensors, revealing the sensing mechanism of intrinsic MoS{sub 2}. By utilizing a small work function metal, Ti, to reduce the SB, excellent electrical properties of this 2D material were yielded with 2–3 times enhanced sensitivity. We experimentally demonstrated that the sensitivity of MoS{sub 2} is superior to that of graphene. Intrinsic MoS{sub 2} was able to realize rapid detection of arsenite down to 0.1 ppb without the influence of large SB, which is two-fold lower than the World Health Organization (WHO) tolerance level and better than the detection limit of recently reported arsenite sensors. Additionally, accurately discriminating target molecules is a great challenge for sensors based on 2D materials. This work demonstrates MoS{sub 2} sensors encapsulated with ionophore film which only allows certain types of molecules to selectively permeate through it. As a result, multiplex ion detection with superb selectivity was realized. Our results show prominent advantages of intrinsic MoS{sub 2} as a sensing material.
Since the 1970s, Raytheon has developed, built, tested and integrated high performance cryocoolers. Our versatile designs for single and multi-stage cryocoolers provide reliable operation for temperatures from 10 to 200 Kelvin with power levels ranging from 50 W to nearly 600 W. These advanced cryocoolers incorporate clearance seals, flexure suspensions, hermetic housings and dynamic balancing to provide long service life and reliable operation in all relevant environments. Today, sensors face a multitude of cryocooler integration challenges such as exported disturbance, efficiency, scalability, maturity, and cost. As a result, cryocooler selection is application dependent, oftentimes requiring extensive trade studies to determine the most suitable architecture. To optimally meet the needs of next generation passive IR sensors, the Compact Inline Raytheon Stirling 1-Stage (CI-RS1), Compact Inline Raytheon Single Stage Pulse Tube (CI-RP1) and Compact Inline Raytheon Hybrid Stirling/Pulse Tube 2-Stage (CI-RSP2) cryocoolers are being developed to satisfy this suite of requirements. This lightweight, compact, efficient, low vibration cryocooler combines proven 1-stage (RS1 or RP1) and 2-stage (RSP2) cold-head architectures with an inventive set of warm-end mechanisms into a single cooler module, allowing the moving mechanisms for the compressor and the Stirling displacer to be consolidated onto a common axis and in a common working volume. The CI cryocooler is a significant departure from the current Stirling cryocoolers in which the compressor mechanisms are remote from the Stirling displacer mechanism. Placing all of the mechanisms in a single volume and on a single axis provides benefits in terms of package size (30% reduction), mass (30% reduction), thermodynamic efficiency (>20% improvement) and exported vibration performance (≤25 mN peak in all three orthogonal axes at frequencies from 1 to 500 Hz). The main benefit of axial symmetry is that proven balancing techniques and hardware can be utilized to null all motion along the common axis. Low vibration translates to better sensor performance resulting in simpler, more direct mechanical mounting configurations, eliminating the need for convoluted, expensive, massive, long lead damping hardware.
Hydrogen sensors are recognized as an important technology for facilitating the safe implementation of hydrogen as an alternative fuel, and there are numerous reports of a sensor alarm successfully preventing a potentially serious event. However, gaps in sensor metrological specifications, as well as in their performance for some applications, exist.The U.S. Department of Energy (DOE) Fuel Cell Technology Office published a short list of critical gaps in the 2007 and 2012 multiyear project plans; more detailed gap analyses were independently performed by the JRC and NREL. There have been, however, some significant advances in sensor technologies since these assessments, including the commercial availability of hydrogen sensors with fast response times (t90 less than 1 s, which had been an elusive DOE target since 2007), improved robustness to chemical poisons, improved selectivity, and improved lifetime and stability. These improvements, however, have not been universal and typically pertain to select platforms or models. Moreover, as hydrogen markets grow and new applications are being explored, more demands will be imposed on sensor performance. The hydrogen sensor laboratories at NREL and JRC are currently updating the hydrogen safety sensor gap analysis through direct interaction with international stakeholders in the hydrogen community, especially end-users. NREL and the JRC are currently organizing a series of workshops (in Europe and the U.S.) with sensor developers, end-users, and other stakeholders in 2017 to identify technology gaps and to develop a path forward to address them. One workshop is scheduled for May 10 in Brussels, Belgium at the Headquarters of the Fuel Cell and Hydrogen Joint Undertaking. A second workshop is planned at the National Renewable Energy Laboratory in Golden, CO, USA. This presentation will review improvements in sensor technologies in the past 5 to 10 years, identify gaps in sensor performance and use requirements, and identify potential research strategies to address the gaps. The presentation will also summarize the outcomes of the Hydrogen Sensors Workshops.
Hydrogen represents a major pathway to decarbonize and stabilize the national and international energy industry and select manufacturing markets. To facilitate the development of hydrogen markets, the US Department of Energy initiated H2@Scale to bring together stakeholders to advance affordable hydrogen production, transport, storage, and utilization to increase revenue opportunities in multiple energy sectors. One major impediment to hydrogen implementation is cost. To expedite the use of hydrogen in energy and other markets, the United States announced in 2021 the Hydrogen Shot, which seeks to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). As the cost of hydrogen drops, new applications will emerge that will require unique configurations of existing equipment and infrastructure, and eventually lead to advances in the generation and utilization of hydrogen. As the hydrogen economy expands, sensors and detection methods will need to adapt to changing infrastructure demands to address the primary targets of health & safety, emissions monitoring, and process control. The NREL Sensor Laboratory is playing a pivotal role in advancing the use of hydrogen sensors and detection methodologies in each of these categories to support DOE's mission for safe and efficient utilization in emerging markets. Health & safety monitors are required to ensure that operators and facilities can react to unintended hydrogen releases, either as GH2, LH2, or as a constituent of blends (e.g., natural gas or ammonia). Current detection methodologies focus on safety applications to detect near its lower flammable limit (4 vol %), and typically include point sensors in applications such as fixed or mobile detectors (e.g., personal gas monitors). Methodologies amenable for area detection include acoustic, emerging optical imaging methods, and flame detectors. Comparable detection strategies can be utilized for emissions monitoring and quantization, however few methods can simultaneously cover both low (emissions) and high (health & safety) levels. Deployment of emission level detectors will be required to 1) reduce product loss through small but potentially significant leaks from an environmental or cost perspective, 2) reduce downtime of high demand systems by early identification of eminent system failures (leaks through pump or compressor seals indicative of impending failure), and 3) address potential emission monitoring requirements that may be set by regulating bodies. The first two points should be adopted by industry to reduce the cost-of-goods-sold. The third main category for hydrogen detection relates to process control and may be advantageous for many existing applications. Two main applications are emerging. For example, the purity requirements for hydrogen that is dispensed from refueling systems for hydrogen fuel cell electric vehicles (FCEV) is rigorously regulated by the Standard SAE J2719, which prescribes maximum allowable levels of multiple impurities in the hydrogen fuel and must be verified by a regulatory body. Hydrogen contaminant detectors (HCD) integrated to the fueling station can assure this compliance. HCDs must be able operate in 100% H2 backgrounds and be able to distinguish between multiple contaminants at low ppm to low ppb levels. Secondly, as a strategy to decarbonize the natural gas grid, there are proposals to blend hydrogen with natural gas. This blending will affect transport applications (pipeline infrastructure), stationary combustion systems (turbines), and consumer and commercial appliances. In the short-term, hydrogen levels up to 20% are proposed. Variations in the hydrogen level can have dramatic impact on the combustion process and on the potential response of safety sensors. These mixtures may be regulated so that the concentration at a delivery point must be monitored with high precision. However, routine maintenance may introduce background gases such as ambient air (with water) or maintenance gases (introduced with welding processes or adhesive outgassing.) Therefore, the detection methodology must be robust enough to recover or respond to various contaminants. Several reviews can be found in literature addressing sensing and detection technologies, including their limitations and applications. However, for most applications, limitations can be alleviated by combining various detection techniques either through system integration or implementation of machine learning methods (artificial intelligence). In this presentation, we will discuss several applications, highlight their current approach for hydrogen detection, and suggest detection strategies to supplement their limitations.
We leverage the frontiers of the Internet of Things technology in a recently developed end-to-end wireless sensor network (WSN) system that samples, collects, stores, and displays mountain hydrology measurements in near real-time. At the core of the system lies an ultra-low power, radio channel-hoping, and self-organizing mesh that allows for remote autonomous sampling of snow. Such properties, combined with a rugged weather-sealed design of the devices and multi-level data replication, provides reliable real-time data at spatial and temporal scales previously impractical to achieve in mountain environments. The system was deployed at three 1 km 2 sites across the North Fork of the Feather River basin with a cluster of 12 sensor nodes for each location. Measurements show that existing operational autonomous systems are non-representative spatially, with biases that can reach up to 50%. A comparison between a wet and dry year showed that snow depths exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Temporally dense analysis using elastic net regression shows that dominant features at the sub-km 2 scale are site-dependent and differ from the watershed scale. Newly introduced explanatory variables, based on the nearest neighbor with a Landsat assimilated historical product, consistently explained up to 90% of the variance in the watershed-scale SWE for both years. At two WSN sites, lagged cross-correlation of snowmelt with stream flow measurements showed a significant improvement of up to 100% compared with existing systems, suggesting that WSNs can be instrumental in improving runoff forecasting and water management.
Wireless sensors without external power supply gained considerable attention due to convenience both in installation and operation. Optical arc detecting sensor equipping with self sustaining power supply using energy harvesting method was investigated. Continuous energy harvesting method was attempted using thermoelectric generator to supply standby power in micro ampere scale and operating power in mA scale. Peltier module with heat-sink was used for high efficiency electricity generator. Optical arc detecting sensor with hybrid filter showed insensitivity to fluorescent and incandescent lamps under simulated distribution panel condition. Signal processing using integrating function showed selective arc discharge detection capability to different arc energy levels, with a resolution below 17 J energy difference, unaffected by bursting arc waveform. The sensor showed possibility for application to arc discharge detecting sensor in power distribution panel. Also experiment with proposed continuous energy harvesting method using thermoelectric power showed possibility as a self sustainable power source of remote sensor.
As part of the Advanced Test Reactor (ATR) National Scientific User Facility (NSUF) program, a joint Idaho State University (ISU) / French Alternative Energies and Atomic Energy Commission (CEA) / Idaho National Laboratory (INL) project was initiated in FY-10 to investigate the feasibility of using neutron sensors to provide online measurements of the neutron flux and fission reaction rate in the ATR Critical Facility (ATRC). A second objective was to provide initial neutron spectrum and flux distribution information for physics modeling and code validation using neutron activation based techniques in ATRC as well as ATR during depressurized operations. Detailed activation spectrometry measurements were made in the flux traps and in selected fuel elements, along with standard fission rate distribution measurements at selected core locations. These measurements provide additional calibration data for the real-time sensors of interest as well as provide benchmark neutronics data that will be useful for the ATR Life Extension Program (LEP) Computational Methods and V&V Upgrade project. As part of this effort, techniques developed by Prof. George Imel will be applied by Idaho State University (ISU) for assessing the performance of various flux detectors to develop detailed procedures for initial and follow-on calibrations of these sensors. In addition to comparing data obtained from each type of detector, calculations will be performed to assess the performance of and reduce uncertainties in flux detection sensors and compare data obtained from these sensors with existing integral methods employed at the ATRC. The neutron detectors required for this project were provided to team participants at no cost. Activation detectors (foils and wires) from an existing, well-characterized INL inventory were employed. Furthermore, as part of an on-going ATR NSUF international cooperation, the CEA sent INL three miniature fission chambers (one for detecting fast flux and two for detecting thermal flux) with associated electronics for assessment. In addition, Prof. Imel, ISU, has access to an inventory of Self-Powered Neutron Detectors (SPNDs) with a range of response times as well as Back-to-Back (BTB) fission chambers from prior research he conducted at the Transient REActor Test Facility (TREAT) facility and Neutron RADiography (NRAD) reactors. Finally, SPNDs from the National Atomic Energy Commission of Argentina (CNEA) were provided in connection with the INL effort to upgrade ATR computational methods and V&V protocols that are underway as part of the ATR LEP. Work during fiscal year 2010 (FY10) focussed on design and construction of Experiment Guide Tubes (EGTs) for positioning the flux detectors in the ATRC N-16 locations as well as obtaining ATRC staff concurrence for the detector evaluations. Initial evaluations with CEA researchers were also started in FY10 but were cut short due to reactor reliability issues. Reactor availability issues caused experimental work to be delayed during FY11/12. In FY13, work resumed; and evaluations were completed. The objective of this "Quick Look" report is to summarize experimental activities performed from April 4, 2013 through May 16, 2013.
This paper presents a technique to improve the Cross Correlation Function (CCF) method for indirect flow measurement. Unlike other methods, the CCF method does not need a specific instrument or tool for flow measurements. Instead, the delay time between two sensors can be derived by applying the CCF. A water flow loop was constructed to investigate the CCF method for flow measurement. Because the inherent process temperature variations were small when compared to significant amounts of measurement and process noise, inaccurate flow measurements were obtained. The performance of the CCF method could be improved by increasing the system signal-to-noise ratio, which is the process noise divided by the measurement noise. For this reason, a periodic injection of different temperature water was used to increase the process temperature variation. The results were favorable. However, several design variables needed to be selected. To assist, a CFD simulation was utilized to optimize the distance between sensors and injection point, the rate of injection flow, and other related variables. The results show that CCF optimization of these variables produces better flow measurement performance over a range of flow velocities. (authors)
Fast, robust, and cost-effective means of detecting spectroscopically inactive metal species are necessary for field detection and applications within a variety of areas including industry and the nuclear safeguards fields. A sensor based on spectroelectrochemistry is an excellent candidate to meet these needs as it provides improved selectivity for specifically quantifying metal ions by simultaneously monitoring at least two physio-chemical properties. Ruthenium was chosen as a model system for this study due to its spectroscopic and electrochemical characteristics as well as its relevance within the fuel cycle and industrial fields. Aqueous Ru displays multiple redox couples in which all available oxidation states have poor sensitivity for detection by visible absorption spectroscopy because of the low molar absorptivities. Ru can, however, form complexes with sensitizing ligands such as 2,2'-bipyridine, where the resulting [Ru(ligand)3]2+ complex displays a red luminescence with a high quantum yield of emission. This significantly improves detection limits for Ru and allows for the spectroelectrochemical detection of the otherwise hard-to-detect metal ion. This work explores the in-situ generation of Ru(bpy)3 complexes in simulated field samples and their subsequent spectroelectrochemical sensing using our sensor methodology.
In this Letter, a high-accuracy, two-dimensional displacement sensor is proposed, designed, and demonstrated based on the concept of an extrinsic Fabry–Perot Interferometer. The sensor is composed of two bundled single-mode optic fibers in parallel and two plasmonic metasurface resonators inscribed on a gold substrate via a focused ion beam. The fiber end surface and the metasurface are in parallel with a small cavity between. The cavity change or Z -component displacement is determined from the pattern of interference fringes. The X -component displacement, perpendicular to the Z component, is identified from wavelength-selective metasurface resonators, which possess unique resonant wavelengths due to different nanostructure designs. The sensor was calibrated with six displacements applied through a three-axis precision linear stage. Test results indicated that the proposed interferometer can measure displacements with a maximum error of 5.4 µm or 2.2%.
Cytoplasmic calcium (Ca 2+ ) activates the bestrophin anion channel, allowing chloride ions to flow down their electrochemical gradient. Mutations in bestrophin 1 (BEST1) cause macular degenerative disorders. Previously, we determined an X-ray structure of chicken BEST1 that revealed the architecture of the channel. Here, we present electrophysiological studies of purified wild-type and mutant BEST1 channels and an X-ray structure of a Ca 2+ -independent mutant. From these experiments, we identify regions of BEST1 responsible for Ca 2+ activation and ion selectivity. A “Ca 2+ clasp” within the channel’s intracellular region acts as a sensor of cytoplasmic Ca 2+ . Alanine substitutions within a hydrophobic “neck” of the pore, which widen it, cause the channel to be constitutively active, irrespective of Ca 2+ . We conclude that the primary function of the neck is as a “gate” that controls chloride permeation in a Ca 2+ -dependent manner. In contrast to what others have proposed, we find that the neck is not a major contributor to the channel’s ion selectivity. We find that mutation of a cytosolic “aperture” of the pore does not perturb the Ca 2+ dependence of the channel or its preference for anions over cations, but its mutation dramatically alters relative permeabilities among anions. The data suggest that the aperture functions as a size-selective filter that permits the passage of small entities such as partially dehydrated chloride ions while excluding larger molecules such as amino acids. Furthermore, unlike ion channels that have a single “selectivity filter,” in bestrophin, distinct regions of the pore govern anion-vs.-cation selectivity and the relative permeabilities among anions.
In this paper, an Artificial Intelligence-based (AI) system is proposed for an 11-level cascaded H-bridge multilevel inverter (MLI) with the aims of harmonic suppression and reliability enhancement. The system consists of three seamlessly integrated Neural Networks (NNs). First, a multilayer perceptron is used to generalize the optimal switching angles for selective harmonic elimination under non-equal DC voltages. Next, an autoencoder NN estimates the voltage sensor readings to address potential drifting. Finally, a perceptron NN detects inverter faults based solely on the output voltage of the MLI. Simulation scenarios were evaluated, and the results show that the proposed system provides a comprehensive solution for the robust operation of the MLI. The proposed solution is capable of minimizing the targeted harmonics orders with minimal impact on the fundamental voltage, even when the voltage sensor drifts. Furthermore, the inverter under fault conditions was successfully identified.
Embodiments of the subject invention relate to a gas sensor and method for sensing one or more gases. An embodiment incorporates an array of sensing electrodes maintained at similar or different temperatures, such that the sensitivity and species selectivity of the device can be fine tuned between different pairs of sensing electrodes. A specific embodiment pertains to a gas sensor array for monitoring combustion exhausts and/or chemical reaction byproducts. An embodiment of the subject device related to this invention operates at high temperatures and can withstand harsh chemical environments. Embodiments of the device are made on a single substrate. The devices can also be made on individual substrates and monitored individually as if they were part of an array on a single substrate. The device can incorporate sensing electrodes in the same environment, which allows the electrodes to be coplanar and, thus, keep manufacturing costs low. Embodiments of the device can provide improvements to sensitivity, selectivity, and signal interference via surface temperature control.
Ribozymes use divalent cations for structural stabilization, as catalytic cofactors, or both. Because of the prominent role of Ca 2+ in intracellular signaling, engineered ribozymes with stringent Ca 2+ selectivity would be important in biotechnology. The wild-type glmS ribozyme (glmSWT) requires glucosamine-6-phosphate (GlcN6P) as a catalytic cofactor. Previously, a glmS ribozyme variant with three adenosine mutations (glm SAAA ) was identified, which dispenses with GlcN6P and instead uses, with little selectivity, divalent cations as cofactors for site-specific RNA cleavage. We now report a Ca 2+ -specific ribozyme (glmSCa) evolved from glm SAAA that is >10,000 times more active in Ca 2+ than Mg 2+ , is inactive in even 100 mM Mg2+, and is not responsive to GlcN6P. This stringent selectivity, reminiscent of the protein nuclease from Staphylococcus, allows rapid and selective ribozyme inactivation using a Ca 2+ chelator such as EGTA. Because glmSCa functions in physiologically relevant Ca 2+ concentrations, it can form the basis for intracellular sensors that couple Ca 2+ levels to RNA cleavage. Biochemical analysis of glmSCa reveals that it has co-opted for selective Ca 2+ binding a nonspecific cation-binding site responsible for structural stabilization in glmSWT and glmSAAA. Fine-tuning of the selectivity of the cation site allows repurposing of this preexisting molecular feature.
Since the industrial revolution, detection and monitoring of toxic matter, chemical wastes, and air pollutants has become an important environmental issue. Thus, it leads to the development of chemical sensors for various environmental applications. The recent disastrous oil spills over the near-surface of ocean due to the offshore drilling emphasize the use of chemical sensors for prevention and monitoring of the processes that might lead to these mishaps.1, 2 Chemical sensors operated on a simple principle that the sensing platform undergoes a detectable change when exposed to the target substance to be sensed. Among all the types of chemical sensors, solid state gas sensors have attracted a great deal of attention due to their advantages such as high sensitivity, greater selectivity, portability, high stability and low cost.3, 4 Especially, semiconducting metal oxides such as SnO2, TiO2, and WO3 have been widely used as the active sensing platforms in solid state gas sensors.5 For the enhanced properties of solid state gas sensors, finding new sensing materials or development of existing materials will be needed. Thus, nanostructured materials such as nanotubes,6-8 nanowires,9-11 nanorods,12-15 nanobelts,16, 17 and nano-scale thin films18-23 have been synthesized and studied for chemical sensing applications.