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At least 1,279 records · Page 71

Classification Analytics of Pu-239 and U-235 Source Signatures Using Gamma Spectral Regions

Machine learning detection methods using gamma signatures from spectral measurements of low-intensity Pu-239 and U-235 sources are studied. NaI detectors located at different distances fromthe source have been used to collect the training and independent testing data sets. The source is introduced via a shielded conduit into the facility where it is surrounded by 21 NaI detectors deployed over 6 x 6 meters area in the formation of two concentric circles and a spiral. The counts in gamma spectral regions associated with these two sources are estimated at 1 second intervals for each NaI detector, and are used as classifier features for detecting the source presence. Eight different classifiers with five basic properties — namely, smooth, non-smooth, statistical, structural, and hyper-parameter tuning — are trained and tested using the background and source measurements collected over multiple experimental runs. While the overall classifier performance improved as detectors closer to the source are used, some identically produced detectors under-performed but differently between two sources. Some classifiers achieved lower training error but their testing error based on independent measurements is higher for both sources. Overall, these results indicate significant over-fitting by these methods, and illustrate the complexity of training and selecting the machine learning methods to solve these detection problems.

Rao, Nageswara↗

Luminescent Enhancement for Combined Solar and Commercial Agriculture. Final Report

This proposal will develop a new racking / mounting system combined with a new agriculture-tuned luminescent solar panel for low cost high tunnel implementation to produce both crops and electricity for small local farms. Crop trials performed under this specialized panel/racking system can be extended to open field agriculture with standard utility scale racking system in phase II. The US agricultural industry uses about 300 billion KW-hr of energy / year. Of this energy consumption, roughly 20% goes towards food production and 20% goes towards food processing. This proposal will allow farm land to simultaneously generate electricity, food AND provide jobs. Employment is one of the largest hurdles for the introduction of solar into rural areas. The ability to provide agricultural jobs in addition to solar energy from a plot of land will pave the way for greater adoption of photovoltaics in rural areas. A cost effective and highly durable hybrid power generating high tunnel greenhouse will be developed that generates electricity at less than $2.50/watt INSTALLED cost and can be used for crop production of high values crops. The high tunnel will use a new design of Soliculture greenhouse integrated luminescent solar panels to selectively absorb the green portion of the solar spectrum that is not used by plants and convert the light to red light with higher photosynthetic efficiency and to enhance power output of solar cells. Green light is selectively downshifted to red light using luminescent materials that match the photosythetically active region for plants and the efficiency maximum of Si photovoltaic cells. The color tuning associated with converting green light to red light has been shown to facilitate fruit production in commercial greenhouses growing cucumbers, lettuce and herbs.

14 SOLAR ENERGY↗

Lithiation-Induced Defect Engineering to Promote Oxygen Evolution Reaction

Exploring efficient electrocatalysts for oxygen evolution reaction (OER) is an urgent need to advance the development of sustainable energy conversion. Though defect engineering is considered an effective strategy to regulate catalyst activity for enhanced OER performance, the controllable synthesis of defective oxides electrocatalysts remains challenging. Here, oxygen defects are introduced into NiCo 2 O 4 nanorods by an electrochemical lithiation strategy. By tuning in situ lithiation potentials, the concentration of oxygen defects and the corresponding catalytic activity can be feasibly regulated. In addition, the relationship between the changes in the defect density and electronic structure and the lithiation cut-off voltages is revealed. The results show that NiCo 2 O 4 nanorods undertook intercalation and two-step conversion reaction, in which the lithiation-induced conversion reaction gives rise to a CoO@NiO-based structure with higher defect density and lower oxidation states. As a result, the defective CoO@NiO-based catalyst exhibits exceptional OER activity with an overpotential of 270 mV at 10 mA cm -2 , which is about 74 mV below the pristine nanomaterials. In conclusion, this research proposes a novel strategy to explore high-performance catalysts with structural stability and defect control.

36 MATERIALS SCIENCE↗

Harnessing large language models’ zero-shot and few-shot learning capabilities for regulatory research

Abstract Large language models (LLMs) are sophisticated AI-driven models trained on vast sources of natural language data. They are adept at generating responses that closely mimic human conversational patterns. One of the most notable examples is OpenAI's ChatGPT, which has been extensively used across diverse sectors. Despite their flexibility, a significant challenge arises as most users must transmit their data to the servers of companies operating these models. Utilizing ChatGPT or similar models online may inadvertently expose sensitive information to the risk of data breaches. Therefore, implementing LLMs that are open source and smaller in scale within a secure local network becomes a crucial step for organizations where ensuring data privacy and protection has the highest priority, such as regulatory agencies. As a feasibility evaluation, we implemented a series of open-source LLMs within a regulatory agency’s local network and assessed their performance on specific tasks involving extracting relevant clinical pharmacology information from regulatory drug labels. Our research shows that some models work well in the context of few- or zero-shot learning, achieving performance comparable, or even better than, neural network models that needed thousands of training samples. One of the models was selected to address a real-world issue of finding intrinsic factors that affect drugs' clinical exposure without any training or fine-tuning. In a dataset of over 700 000 sentences, the model showed a 78.5% accuracy rate. Our work pointed to the possibility of implementing open-source LLMs within a secure local network and using these models to perform various natural language processing tasks when large numbers of training examples are unavailable.

Biochemistry & Molecular Biology↗

Boosting the Oxygen Evolution Reaction by Tuning the Interfacial Iron Adsorption on Layered Double Hydroxide

Understanding the interaction between ions in the electrolyte and electrode materials plays an important role in optimizing the water electrolysis performance for hydrogen production. Herein, the synergistic effect of iron (Fe) in the electrolyte and interlayer anions within the layered structure on the oxygen evolution reaction (OER) has been investigated by combining material synthesis with controlled structure, multiple characterization techniques, and first-principles calculations. Nickel aluminum layered double hydroxides (NiAl-LDHs) with different interlayer anions (CO 3 2– , Cl – , and Br – ) show similar oxygen evolution activity in the absence of Fe species in the electrolyte. The addition of Fe into the electrolyte results in improved performance for all of the NiAl-LDHs, following the rank LDH-Br > LDH-Cl > LDH-CO 3 , under all of the conditions with varied concentration of Fe. X-ray absorption spectroscopy and identical location electron microscopy analyses show that the LDH structure remains unchanged after the OER activity test, while in situ stationary probe rotating disk electrode inductively coupled plasma mass spectrometry (SPRDE-ICP-MS) measurements show partial dissolution of the intercalating halide ions during cycling, with less dissolution for Br-intercalated materials. Insights from theoretical calculations demonstrate the thermodynamic preference of Br – to remain intercalated in the presence of Fe, while the stronger adsorption of Fe(OH) 3 species on the LDH-Br sample promotes the OER activity. In conclusion, these results provide mechanistic insights into the rational design of active layered materials with an enhanced OER performance for efficient water electrolysis.

58 GEOSCIENCES↗

Extreme Tele-Echocardiography: Methodology for Remote Guidance of In-flight Echocardiography Aboard the International Space Station

Echocardiography is ideally suited for cardiovascular imaging in remote environments, but the expertise to perform it is often lacking. In 2001, an ATL HDI5000 was delivered to the International Space Station (ISS). The instrument is currently being used in a study to investigate the impact of long-term microgravity on cardiovascular function. The purpose of this report is to describe the methodology for remote guidance of echocardiography in space. Methods: In the year before launch of an ISS mission, potential astronaut echocardiographic operators participate in 5 sessions to train for echo acquisitions that occur roughly monthly during the mission, including one exercise echocardiogram. The focus of training is familiarity with the study protocol and remote guidance procedures. On-orbit, real-time guidance of in-flight acquisitions is provided by a sonographer in the Telescience Center of Mission Control. Physician investigators with remote access are able to relay comments on image optimization to the sonographer. Live video feed is relayed from the ISS to the ground via the Tracking and Data Relay Satellite System with a 2 second transmission delay. The expert sonographer uses these images along with two-way audio to provide instructions and feedback. Images are stored in non-compressed DICOM format for asynchronous relay to the ground for subsequent off-line analysis. Results: Since June, 2009, a total of 19 resting echocardiograms and 4 exercise studies have been performed in-flight. Average acquisition time has been 45 minutes, reflecting 26,000 km of ISS travel per study. Image quality has been adequate in all studies, but remote guidance has proven imperative for fine-tuning imaging and prioritizing views when communication outages limit the study duration. Typical resting studies have included 12 video loops and 21 still-frame images requiring 750 MB of storage. Conclusions: Despite limited crew training, remote guidance allows research-quality echocardiography to be performed by non-experts aboard the ISS. Analysis is underway and additional subjects are being recruited to define the impact of microgravity on cardiac structure and systolic and diastolic function.

Martin, David↗

Realizing continuous cation order-to-disorder tuning in a class of high-energy spinel-type Li-ion cathodes

Conventional Li-ion cathode materials are dominated by well-ordered structures, in which Li and transition metals occupy distinct crystallographic sites. Here, we show in this paper that profoundly new degrees of freedom for the optimization of electrochemical properties may be accessed if controllable cation disorder is introduced. In a class of high-capacity spinel-type cathode materials, we identify cation to anion ratio in synthesis as a key parameter for tuning the structure continuously from a well-ordered spinel, through a partially ordered spinel, to rocksalt. We find that the varying degree of cation disorder modifies the voltage profile, rate capability, and charge-compensation mechanism in a rational and predictable way. Our results indicate that spinel-type order is most beneficial for achieving high-rate performance as long as the cooperative 8a to 16c phase transition is suppressed, while more rocksalt-like disorder facilitates O redox, which can increase capacity. Our findings reveal an important tuning handle for achieving high energy and power in the vast space of partially ordered cathode materials.

25 ENERGY STORAGE↗

Investigation of application of two-degree-of-freedom dry tuned-gimbal gyroscopes to strapdown navigation systems

The work is described which was accomplished during the investigation of the application of dry-tuned gimbal gyroscopes to strapdown navigation systems. A conventional strapdown configuration, employing analog electronics in conjunction with digital attitude and navigation computation, was examined using various levels of redundancy and both orthogonal and nonorthogonal sensor orientations. It is concluded that the cost and reliability performance constraints which had been established could not be met simultaneously with such a system. This conclusion led to the examination of an alternative system configuration which utilizes an essentially new strapdown system concept. This system employs all-digital signal processing in conjunction with the newly-developed large scale integration (LSI) electronic packaging techniques and a new two-degree-of-freedom dry tuned-gimbal instrument which is capable of providing both angular rate and acceleration information. Such a system is capable of exceeding the established performance goals.

Source record↗

Enhancing the Mechanical Properties of Glassy Nanocomposites by Tuning Polymer Molecular Weight

The addition of nanoparticles to a polymer matrix is a well-known process to improve the mechanical properties of polymers. Many studies of mechanical reinforcement in polymer nanocomposites (PNCs) focus on rubbery matrices; however, much less effort concentrates on the factors controlling the mechanical performance of the technologically important glassy PNCs. This paper presents a study of the effect of the polymer molecular weight (MW) on the overall mechanical properties of glassy PNCs with attractive interaction by using Brillouin light scattering. We found that the mechanical moduli (bulk and shear) have a nonmonotonic dependence on MW that cannot be predicted by simple rule of mixtures. The moduli increase with increasing MW up to 100 kg/mol followed by a drop at higher MW. We demonstrate that the change in the mechanical properties of PNCs can be associated with the properties of the interfacial polymer layer. The latter depend on the interfacial chain packing and stretching, as well as polymer bridging, which vary differently with the MW of the polymer. These competing contributions lead to the observed nonmonotonic variations of the glassy PNC moduli with MW. Our work provides a simple, cost-effective, and efficient way to control the mechanical properties of glassy PNCs by tuning the polymer chain length. Our finding can be beneficial for the rational design of PNCs with desired mechanical performance.

36 MATERIALS SCIENCE↗

Decoding Golden Eagle Movement Behavior from High-Resolution, Variable-Rate Telemetry Data Through Bayesian Filtering

The recent advances in animal tracking technology have enabled the collection of a vast amount of in situ data regarding the movement of wildlife at high spatiotemporal resolution. These data are usually available at variable time resolutions and contains noise (error) originating from GPS fixes. Decoding movement characteristics, particularly of flying animals, from telemetry data while handling these factors is a challenging yet important task for conservation purposes. Typically, this task is broken into two subtasks: resampling, and model calibration. The resampling subtask converts the variable rate positional data into a constant time interval data, while the model calibration subtask uses the resampled data to tune time-invariant parameters of the proposed models. For telemetry data at high temporal resolutions (order of 1 second), it is very challenging to decouple noise from actual movements using interpolation-based resampling techniques. Any errors introduced during resampling can significantly alter the the calibration and prediction attributes of the movement model. We address this problem through a unified Bayesian state-space framework that can handle both the resampling and calibration tasks in a single step. In addition, we use the speed and heading of the bird from telemetry data to regularize the position information of the bird. We use a Kalman filtering approach to include these nonlinearly related motion parameters within the state space framework. We cross-validated to quantify how this inclusion affects the model performance in estimating true bird movements. The relationship between the true state of the bird and environmental and topographical covariates is then represented parametrically. These parameters are then tuned using stochastic sampling strategies like Markov Chain Monte Carlo (MCMC). We use the telemetry data collected from golden eagles in the western USA to demonstrate the applicability of this approach to build a predictive, probabilistic movement model. Our preliminary results show that this approach provides improved predictive performance in terms of capturing higher-order motion parameters such as angular and horizontal accelerations, which may have simpler and more direct relationships with environmental covariates than corresponding speeds. In this talk, we will demonstrate how this state-space approach benefits the prediction capabilities of a movement model in simulating golden eagle paths through a wind power plant in Wyoming given certain atmospheric conditions. The model outcomes are aimed at informing mitigation strategies that can minimize the potential for collisions of golden eagles with wind turbines.

Bayesian methods↗

Maximizing Uptake of Hygroscopic Hydrogels Through Extreme Swelling-Induced Salt Loading

Hygroscopic hydrogels have emerged as scalable and low-cost materials with the potential of high-performance vapor sorption for atmospheric water harvesting, dehumidification, and passive cooling. Despite extensive research interest aimed at improving hygroscopic hydrogels, devices using these materials still exhibit insufficient performance, partly due to limited water uptake of the hydrogels. In this work, we study the swelling dynamics of hydrogels in aqueous lithium chloride solutions and use this to achieve extreme salt loading of hygroscopic hydrogels. By rationally tuning the salt concentration used for swelling we achieve successful synthesis of hygroscopic hydrogels with high water uptake of 1.76 g/g, 2.53 g/g, 3.80 g/g at relative humidites of 30%, 50%, and 70%, respectively, exceeding previous hydrogels by over 19%. These water uptakes bring the performance of hydrogels within ≈91% of the fundamental limit of commonly used hygroscopic salts, while still avoiding leakage problems common in salt solutions. Furthermore, we elucidate via models the maximum uptake achievable without leakage as a function of the operating relative humidity and hydrogel swelling ratio. The insights from this work can be used to design hygroscopic hydrogels with exceptional performance for a wide variety of sorption applications to tackle global challenges such as water scarcity and energy efficiency.

25 ENERGY STORAGE↗

Molecular spins for quantum computation

Spins in solids or in molecules possess discrete energy levels, and the associated quantum states can be tuned and coherently manipulated by means of external electromagnetic fields. Spins therefore provide one of the simplest platforms to encode a quantum bit (qubit), the elementary unit of future quantum computers. Notably, performing any useful computation demands much more than realizing a robust qubit—one also needs a large number of qubits and a reliable manner with which to integrate them into a complex circuitry that can store and process information and implement quantum algorithms. This ‘scalability’ is arguably one of the challenges for which a chemistry-based bottom-up approach is best-suited. Molecules, being much more versatile than atoms, and yet microscopic, are the quantum objects with the highest capacity to form non-trivial ordered states at the nanoscale and to be replicated in large numbers using chemical tools.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

IMHO Fine-Tuning Improves Claim Detection

Claims are the central component of an argument. Detecting claims across different domains or data sets can often be challenging due to their varying conceptualization. We propose to alleviate this problem by fine tuning a language model using a Reddit corpus of 5.5 million opinionated claims. These claims are self-labeled by their authors using the internet acronyms IMO/IMHO (in my (humble) opinion). Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. As these data sets include diverse domains such as social media and student essays this improvement demonstrates the robustness of fine-tuning on this novel corpus.

Chakrabarty, Tuhin↗

Dielectric Engineering of ZnO@ZIF‐8 for High‐Performance Triboelectric Nanogenerators and Self‐Powered Humidity Sensors

Porous metal oxide–metal-organic framework (MO x @MOF) hybrids offer synergistic effects that enhance surface charge density, electronic structure, and textural properties, making them ideal for self-powered sensing applications. Here, uniform, and pinhole-free ternary porous ZnO–PTFE@ZIF-8 hybrid films are developed on room-temperature co-sputtered ZnO–polytetrafluoroethylene (PTFE) composite films, where ZnO serves as a self-sacrificial precursor to precisely regulate ZIF-8 (Zn-MeIm 2 ) growth through solvothermal methods. The resulting TENG achieves a high output power density of 0.67 mW cm −2 , attributed to the synergistic effects of fluorine-rich PTFE and methyl-functionalized ZIF-8, which enhance surface charge density and dielectric response. By tuning PTFE content in the ZnO–PTFE composite, the dielectric constant and triboelectric output is optimized. The ZP60@ZIF-8-based device also demonstrates excellent humidity sensing performance, with a wide detection range (20–99% RH) and ultrahigh sensitivity (R V % of 19 900%, R I % of 19 325% at 99% RH). These results position ZP@ZIF-8-based TENGs as promising platforms for next-generation self-powered sensors and smart wearable electronics.

42 ENGINEERING↗

Self-Tuning of Design Variables for Generalized Predictive Control

Three techniques are introduced to determine the order and control weighting for the design of a generalized predictive controller. These techniques are based on the application of fuzzy logic, genetic algorithms, and simulated annealing to conduct an optimal search on specific performance indexes or objective functions. Fuzzy logic is found to be feasible for real-time and on-line implementation due to its smooth and quick convergence. On the other hand, genetic algorithms and simulated annealing are applicable for initial estimation of the model order and control weighting, and final fine-tuning within a small region of the solution space, Several numerical simulations for a multiple-input and multiple-output system are given to illustrate the techniques developed in this paper.

Lin, Chaung↗

Redox transistors for neuromorphic computing

Efficiency bottlenecks inherent to conventional computing in executing neural algorithms have spurred the development of novel devices capable of “in-memory” computing. Commonly known as “memristors,” a variety of device concepts including conducting bridge, vacancy filament, phase change, and other types have been proposed as promising elements in artificial neural networks for executing inference and learning algorithms. In this article, we review the recent advances in memristor technology for neuromorphic computing and discuss strategies for addressing the most significant performance challenges, including nonlinearity, high read/write currents, and endurance. As an alternative to two-terminal memristors, we introduce the three-terminal electrochemical memory based on the redox transistor (RT), which uses a gate to tune the redox state of the channel. Decoupling the “read” and “write” operations using a third terminal and storage of information as a charge-compensated redox reaction in the bulk of the transistor enables high-density information storage. These properties enable low-energy operation without compromising analog performance and nonvolatility. Finally, we discuss the RT operating mechanisms using organic and inorganic materials, approaches for array integration, and prospects for achieving the device density and switching speeds necessary to make electrochemical memory competitive with established digital technology.

97 MATHEMATICS AND COMPUTING↗

Quantifying Losses and Assessing the Photovoltage Limits in Metal–Insulator–Semiconductor Water Splitting Systems

Metal–insulator–semiconductor (MIS) photo-electrocatalysts offer a pathway to stable and efficient solar water splitting. Initially motivated as a strategy to protect the underlying semiconductor photoabsorber from harsh operating conditions, the thickness of the insulator layer in MIS systems has recently been shown to be a critical design parameter which can be tuned to optimize the photovoltage. Here we analyze the underlying mechanism by which the thickness of the insulator layer impacts the performance of MIS photo-electrocatalysts. A concrete example of an Ir/HfO 2 /n-Si MIS system is investigated for the oxygen evolution reaction. The results of combined experiments and modeling suggest that the insulator thickness affects the photovoltage i) favorably by controlling the flux of charge carriers from the semiconductor to the metal electrocatalyst and ii) adversely by introducing nonidealities such as surface defect states which limit the generated photovoltage. It is important to quantify these different mechanisms and suggest avenues for addressing these nonidealities to enable the rational design of MIS systems that can approach the fundamental photovoltage limits. The analysis described in this contribution as well as the strategy toward optimizing the photovoltage are generalizable to other MIS systems.

36 MATERIALS SCIENCE↗