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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Modular Accelerator Robotics Framework Implementation For Accelerator Inspection

Accelerator tunnels, such as the ones at Fermilab, remain highly radioactive after beam shutoff due to induced radiation from the beam. This residual radiation creates a hazardous environment for manual inspection and repair of beamline components. To minimize worker radiation dose and reduce beam downtime, the AD Robotics Initiative previously built a fleet of low-cost custom mobile robots. However, the custom Python sockets server-client architecture lacked standardization, causing development delays and complicating the integration of new sensors and actuators. Here, we developed a modular system using ROS2 and Docker to standardize the teleoperation and control interfaces. This system was validated by implementing a teleoperation controller with real-time, low-latency, and high-definition video feedback. The aim of this framework is for a new feature or even a robot to be integrated into the system simply by documenting the hardware configuration. Current integration of LiDAR, Odometry, and Depth Cameras provides the foundation for Simultaneous Localization and Mapping (SLAM) tasks. Finally, future work involves integration into the accelerator control system and the attachment of a 6 degree-of-freedom robotic arm for telemanipulation.

Rayyan Khana, M. [Unlisted, US, IL] (ORCID:0009000↗
DOE OSTI2026

CMS Endcap Timing Layer: System Validation and Assembly

The High-Luminosity Large Hadron Collider (HL-LHC) will operate at unprecedented luminosities, resulting in up to 200 simultaneous interactions per bunch crossing. To mitigate the resulting pileup challenges, the CMS experiment is implementing the MIP Timing Detector (MTD), designed to provide precise timing information with a resolution of 30 – 40 picoseconds per track. The MTD consists of the Barrel Timing Layer (BTL) and the Endcap Timing Layer (ETL), each optimized for different regions of the detector. The ETL, comprising two double-sided disks, utilizes Low-Gain Avalanche Diode (LGAD) sensors coupled with the Endcap Timing Readout Chip (ETROC) to achieve high-precision timing measurements in the forward region. Significant progress has been made toward the realization of the ETL through extensive system-level validation of the ETROC readout chain and the development of scalable module assembly procedures. This presentation will provide a comprehensive overview of the ETL and highlight recent advances in system validation and module assembly, emphasizing their roles in ensuring the detector readiness for HL-LHC operation.

Lee, Dongyub [Kyungpook Natl. U.] (ORCID:000000034↗
DOE OSTI2025

A Novel, Low-Cost, Portable Device for Counterfeit and Noncompliant Refrigerant Detection

The increasing prevalence of counterfeit refrigerants presents significant risks to Heating, Ventilation, Air Conditioning, and Refrigeration (HVACR) systems, including compromised equipment performance, safety hazards, and environmental non-compliance. This report details the development of a novel, cost-effective, and portable detection device designed to accurately identify counterfeit refrigerants. The device utilizes a controlled gas sampling and analysis system within a sealed chamber, ensuring precise measurements while maintaining safety through a purging mechanism. The system features a high-sensitivity sensor integrated with an onboard control module that analyzes gas composition in real-time, providing users with clear visual indicators for refrigerant authenticity.Laboratory validation demonstrated the device’s high accuracy (exceeding 95%) in detecting unauthorized refrigerant blends. Key advantages include affordability, ease of use, rapid response time, and compatibility with a wide range of refrigerants. This solution supports compliance with regulatory frameworks such as the American Innovation and Manufacturing (AIM) Act, enhances safety in HVACR operations, and mitigates the risks associated with counterfeit refrigerants. Future developments will focus on expanding refrigerant detection capabilities, integrating machine learning for enhanced accuracy, implementing cost-reduction strategies to improve accessibility and market adoption, and optimizing system packaging for enhanced field portability.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗
DOE OSTI2025

A Novel, Low-Cost, Portable Device for Counterfeit and Noncompliant Refrigerant Detection

The increasing prevalence of counterfeit refrigerants presents significant risks to Heating, Ventilation, Air Conditioning, and Refrigeration (HVACR) systems, including compromised equipment performance, safety hazards, and environmental non-compliance. This report details the development of a novel, cost-effective, and portable detection device designed to accurately identify counterfeit refrigerants. The device utilizes a controlled gas sampling and analysis system within a sealed chamber, ensuring precise measurements while maintaining safety through a purging mechanism. The system features a high-sensitivity sensor integrated with an onboard control module that analyzes gas composition in real-time, providing users with clear visual indicators for refrigerant authenticity. Laboratory validation demonstrated the device’s high accuracy (exceeding 95%) in detecting unauthorized refrigerant blends. Key advantages include affordability, ease of use, rapid response time, and compatibility with a wide range of refrigerants. This solution supports compliance with regulatory frameworks such as the AIM Act, enhances safety in HVACR operations, and mitigates the risks associated with counterfeit refrigerants. Future developments will focus on expanding refrigerant detection capabilities, integrating machine learning for enhanced accuracy, implementing cost-reduction strategies to improve accessibility and market adoption, and optimizing system packaging for enhanced field portability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Ultrasonic Transducer Irradiation Test Results

Ultrasonic technologies offer the potential for high accuracy and resolution in-pile measurement of a range of parameters, including geometry changes, temperature, crack initiation and growth, gas pressure and composition, and microstructural changes. Many Department of Energy-Office of Nuclear Energy (DOE-NE) programs are exploring the use of ultrasonic technologies to provide enhanced sensors for in-pile instrumentation during irradiation testing. For example, the ability of small diameter ultrasonic thermometers (UTs) to provide a temperature profile in candidate metallic and oxide fuel would provide much needed data for validating new fuel performance models. These efforts are limited by the lack of identified ultrasonic transducer materials capable of long term performance under irradiation test conditions. To address this need, the Pennsylvania State University (PSU) was awarded an Advanced Test Reactor National Scientific User Facility (ATR NSUF) project to evaluate the performance of promising magnetostrictive and piezoelectric transducers in the Massachusetts Institute of Technology Research Reactor (MITR) up to a fast fluence of at least 1021 n/cm2 . A multi-National Laboratory collaboration funded by the Nuclear Energy Enabling Technologies Advanced Sensors and Instrumentation (NEET ASI) program also provided initial support for this effort. This irradiation, which started in February 2014, is an instrumented lead test and real-time transducer performance data are collected along with temperature and neutron and gamma flux data. The irradiation is ongoing and will continue to approximately mid-2015. To date, very encouraging results have been attained as several transducers continue to operate under irradiation.

Daw, Joshua↗
DOE OSTI2025

Quality-Controlled Meteorological Data from the Flood Control District of Maricopa County (FCDMC) Network, Phoenix, Arizona (1987-2024)

This dataset contains 15- or 30-minute interval meteorological data from the Flood Control District of Maricopa County (FCDMC), Arizona, USA, covering eight key variables across multiple sensor stations between 1987 and 2024. Each variable is stored as a separate CSV file, containing time-series data that have undergone rigorous quality control (QC) procedures and, where appropriate, short-gap interpolation for consistency. The quality control (QC) pipeline consisted of four sequential tests: (1) a range test to ensure all values fall within physically realistic limits, (2) a step test to identify abrupt and implausible changes between consecutive records, (3) a proximity test that validates flagged values from step test using data from nearby stations and exceedance probability thresholds, and (4) a persistence test to detect and remove periods of unrealistically constant readings. These thresholds were calibrated to Arizona’s environmental conditions and sensor specifications. After QC, short gaps (≤2 hours) were linearly interpolated to ensure consistent temporal resolution, except for wind variables. Due to a major upgrade in FCDMC’s data transmission system, only ALERT-2 protocol data (2016–2024) for wind variables are included; earlier ALERT-1 data were excluded because of irregular sampling and high missing rates. This dataset supports regional climate and infrastructure resilience studies by providing standardized, high-resolution meteorological data for the greater Phoenix metropolitan area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The ePIC dual-radiator RICH detector

The dual radiator Ring Imaging Cherenkov (dRICH) detector is required to provide continuous hadron identification from ≈3 GeV/c up to ≈50 GeV/c, and to supplement electron and positron identification from a few hundred MeV/c up to about 15 GeV/c, in the forward (ion-side) end-cap of the ePIC experiment. Such an extended momentum range imposes the use of two radiators, gas and aerogel. The common imaging system, that ensures compactness and cost-effectiveness, is based on SiPM sensors to work in a high non-uniform magnetic field. During the R&D phase, the dual radiator principle and the single component performance have been validated. A status overview of the project is presented. The design and technological choices are discussed together with the results obtained from laboratory characterization of the component demonstrators and beam tests of the evolving prototypes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗
DOE OSTI2026

Single-Phase to Split-Phase Inverters with Advanced Grid Support Functions for Grid-Interactive Applications

This work presents a cost-effective single-phase to split-phase inverter with a reduced switch count, achieving grid interactive performance while maintaining operational efficiency. The proposed system integrates an Andronov-Hopf oscillator based secondary controller, which inherently embeds a nonlinear resistive droop architecture, ensuring rapid dynamic response. A Lyapunov energy function-based primary control enhances transient stability and regulation, while an internal model-based point of common coupling voltage estimation enables cost optimization without additional sensors. Equipped with advanced grid support functionalities, the inverter facilitates seamless distribution system operation with enhanced robustness. The effectiveness of the proposed architecture and control strategy is validated through MATLAB/Simulink and PLECS simulations, demonstrating its feasibility for high-performance grid-supportive applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Seismoacoustic Monitoring of Pyroprocessing Equipment

With the projected increase in world nuclear capacity comes the hurdle of spent nuclear fuel. Pyroprocessing is one method to process irradiated fuel by using high temperatures and electrochemical steps to separate radioactive components. High temperatures, high radiation levels, and equipment confined to a heavily shielded hot cell are a few of the challenges introduced regarding safeguards for pyroprocessing. The first stage of the process utilizes an element chopper to cut irradiated fuel into smaller pieces, offering the potential to identify equipment operation through seismoacoustic monitoring. Eleven seismic and infrasound sensors were deployed at various locations and distances near a pyroprocessing hot cell to evaluate signals emitted from the chopper. Signals were processed using short-time Fourier transforms and manually scanned to locate chopping events. Operator logs were then collected for a ground truth comparison. Results indicate that both infrasound and seismic signals offer the ability to accurately measure this stage of pyroprocessing, though seismic signals are more prominent. Sensor locations are also evaluated to determine where the signal is detectable. This type of monitoring offers a method to aid in safeguards and proliferation detection by validating a pyroprocessing facility’s schedule and identifying activity that does not align with records.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

An engineered lactate oxidase based electrochemical sensor for continuous detection of biomarker lactic acid in human sweat and serum

Lactate levels in humans reveal intensity and duration of exertion and provide a critical readout for the severity of life-threatening illnesses such as pediatric sepsis. Using the lactate oxidase enzyme (Lox) from Aerococcus viridians, we demonstrated its functionality for lactate electrochemical sensing in physiological fluids in a lab setting. The structure and dynamics of LOx were validated by crystallography, X-ray scattering, and hydroxyl radical protein footprinting. This provided a validated protein template for understanding and designing an enzyme-based electrochemical sensing elements. Using this template, LOx enzyme variants were generated and compared. Comparison of the variants demonstrates that one exhibits effective lactate sensing at significantly reduced operating voltages. Additionally, we demonstrate that the four hexahistidine-tags on each enzyme tetramer are sufficient for immobilization to create a durable, functional sensor, with no need for a covalent attachment, enabling self-immobilization and eliminating the need for additional immobilization steps. The functionality of the LOx enzyme variants was verified at physiological lactate concentrations in both human serum (0–4 mM) and artificial sweat (0–100 mM) using 3-electrode setups for analysis of the three variants in parallel. Accuracy of measurement in both artificial sweat and human serum were high. Employing a microfluidic flow cell, we successfully monitored varying lactate levels in physiological fluids continuously over a 2h period. Overall, this optimized LOx enzyme, which self-immobilizes onto gold sensing electrodes, facilitates efficient and reliable lactate detection and continuous monitoring at reduced operating voltages suitable for further development towards commercial use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Employing MACS/ViBRANT as a Surrogate MARVEL Reactor for Startup Reactivity Tuning and Supervisory Control Processes

Advanced nuclear reactors are a key part of the future of nuclear energy both in the United States and globally. They offer unique benefits for various energy-demanding applications, including use in remote locations, compact size, modular manufacturing, remote monitoring, low and/or variable power rating operation, and reliance on novel technologies to enhance operational safety. To achieve economic feasibility, advanced reactors must significantly reduce their workforces in comparison with the current fleet. Achieving this reduction will occur through reducing staff workloads using technology to achieve autonomous or semi-autonomous operations, demonstrated by comprehensive testing and validation activities. These operations will require both software and hardware platforms during the design and testing phases. While simulations are useful during the design phase, their performance can significantly deviate during actual deployment on hardware. This report presents the outcomes of a collaborative technical initiative between the U.S. Department of Energy (DOE) Microreactor Program (MRP) and Advanced Sensors and Instrumentation (ASI) Program. The collaboration utilized the Microreactor Automated Control System (MACS) hardware platform to bridge the gap between theoretical reactor design and actual startup and control operations. Two key use cases were investigated: facilitating the startup testing period and demonstrating supervisory control. The first use case details the key Microreactor Applications Research Validation and Evaluation (MARVEL) reactor startup physics testing activities conducted using the MACS platform. These activities included drum worth measurements, shutdown margin assessment, temperature feedback analysis, and scram time evaluation, as well as unique testing that would apply to the MARVEL reactor to demonstrate the testing methodologies in a low-risk environment. The MACS platform, serving as a surrogate representation of the MARVEL reactor, proved instrumental in performing these tests. The exercise revealed aspects that led to optimized processes, refined hardware design, and enhanced base software capabilities. By maturing methods and technologies in this manner, the initiative promises to reduce wasted time in the actual on-site reactor deployment effort, thereby saving significant time and resources. The second use case focuses on the development and implementation of supervisory control methods aimed at managing core tilt, which can result from asymmetrical operations or manufacturing imperfections in fuel rods or reactivity control devices. A key objective was to assess and compare the use of artificial intelligence (AI) for supervisory control. The effort aimed to define the role of supervisory control to enhance performance without risking control instability. This effort explored three distinct approaches: rules-based (RB) methods, optimization techniques, and reinforcement learning (RL) algorithms. Each approach was evaluated for its ease of implementation, its usability, and its effectiveness in responding to asymmetries in neutron flux. Comparative analysis of these approaches provided valuable insights into their applicability and effectiveness, offering a robust framework for advanced reactor operations. Together, these two use cases highlight the potential of hardware test beds to help streamline the design, operation, and control of advanced nuclear reactors. This collaborative effort underscores the importance of continued innovation and experimentation in achieving the next generation of safe, reliable, and economically viable nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Supervised Learning-Based Spatial Position Estimation with Vertical Displacement for Hovering UAV Wireless Power Transfer

This study presents a supervised learning-based spatial position estimation approach for wireless power transfer (WPT) systems supporting hovering unmanned aerial vehicle (UAV) charging. Unlike stationary charging scenarios, hovering UAVs introduce continuous lateral misalignment and vertical displacement, leading to variations in magnetic coupling and reduced power transfer efficiency. To address this challenge, the proposed method estimates the relative spatial position of the receiver coil using only electrical measurements obtained at the secondary side. A supervised learning model is trained to map output voltage and current features to spatial coordinates, enabling position awareness without requiring external sensors, vision systems, or communication links. The sensing functionality is inherently integrated into the WPT system, allowing simultaneous power transfer and localization through the same magnetic interface. Experimental validation is conducted on a laboratory-scale prototype under varying lateral offsets and air-gap conditions. In addition, spline-based interpolation is employed to increase spatial data density for training. The results demonstrate that the proposed framework can capture spatial variations associated with both lateral and vertical displacement, providing reliable position estimation under hovering conditions. This work establishes a hardware-efficient, sensorless solution for UAV wireless charging and serves as a baseline for advanced data-driven position estimation methods in dynamic WPT systems.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2025

ThunderDOEME: A Seismoacoustic Deployment at Langmuir Observatory

This report documents a two-month deployment of seismoacoustic instrumentation at the Langmuir Observatory, located outside Socorro, New Mexico. A total of eleven Fairfield Nodal Seismometers and fifteen GEM infrasound loggers were deployed from June 2 to August 5, 2025. The nodal seismometers recorded data for an average of 34 days, while the GEMs had variable operational durations throughout the deployment period. The primary objective was to capture thunder signals using seismoacoustic instrumentation, contributing to a better understanding of the acoustic and seismic phenomena associated with thunder. Coinciding with this deployment, optical and electric field sensors were present, providing information regarding the timing and location of lightning strikes in the region. Additionally, triggered lightning strikes were conducted, serving as a ground truth for validating the captured thunder signals. Preliminary analysis of the data reveals clear thunder signals in both the seismic and infrasound recordings, with peak frequencies observed across a range of 4 to 40 Hz, depending on the event.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Evaluating the feasibility of using downwind methods to quantify point source oil and gas emissions using continuously monitoring fence-line sensors

The dependable reporting of methane (CH 4 ) emissions from point sources, such as fugitive leaks from oil and gas infrastructure, is important for profit maximization (retaining more hydrocarbons), evaluating climate impacts, assessing CH 4 fees for regulatory programs, and validating CH 4 intensity in differentiated gas programs. Currently, there are disagreements between emissions reported by different quantification techniques for the same sources. It has been suggested that downwind CH 4 quantification methods using CH 4 measurements on the fence line of production facilities could be used to generate emission estimates from oil and gas operations at the site level, but it is currently unclear how accurate the quantified emissions are. To investigate the accuracy of downwind methods, this study uses fence-line simulated data collected during controlled-release experiments as input for a non-standard closed-path eddy covariance (EC), the Gaussian plume inverse model (GPIM), and the backward Lagrangian stochastic (bLs) model in a range of atmospheric conditions. This study's EC attempt was unsuccessful due to data collection and instrumentation issues, resulting in invalid results characterized by underestimated emissions, large negative fluxes, and cospectra/ogives that deviated from their ideal shapes. Consequently, the EC results could not be compared with the GPIM and bLS model. The bLs model demonstrated the highest accuracy for single-release single-point emissions, though it exhibited greater uncertainty than GPIM under multi-release conditions. Across the GPIM and bLs model, the most reliable quantification was achieved with 15 min averaging and a narrow 5° wind sector range. Although EC was limited in this context, future studies should consider employing a standard EC system and further optimizing GPIM and bLs approaches – particularly for complex multi-source scenarios – to enhance quantification accuracy and reduce uncertainty.

03 NATURAL GAS↗
DOE OSTI2025

Dry electrodes with a printed cellulose–graphene ink for low-profile strain sensors in electromyography

Dihydrolevoglucosenone, commonly known as Cyrene, is a renewable and fully biodegradable cellulose-waste derived, environmentally friendly solvent, presenting a non-toxic alternative to N-methyl-2-pyrrolidone (NMP). Currently, solution-based processing of graphene and other similar van der Waals solids favor toxic solvents such as NMP, limiting their use for biosensing. However, with the use of Cyrene, bio-compatible printable devices are possible, and studies have already demonstrated its use in temperature and other biosensing methods through screen-printing. Screen-printing unfortunately often requires masks that constrain the minimum acquirable feature size to be above hundreds of centimeters and wastes material, adding to process complexity and cost. Conversely, inkjet-printing is an attractive alternative for the maskless patterning of hierarchically assembled structures, with micron length scales attainable. Graphene's high conductivity positions it ideally for long-wear sensors such as dry electrodes or respiration monitors. Here, we demonstrate the potential of Cyrene-based graphene inks through few-layer inkjet printing on flexible substrates for the first time, to produce non-toxic conductors toward a strain-mediated mechanism for biosensing, used to detect bodily motion for wearable electronics. The challenges overcome in this study include engineering ink chemistry and printing parameters such that Cyrene's relatively high viscosity compared to typical inkjet solvents, still allows for droplet ejection in a conventional material printer, yielding well-resolved clean line-edges in contrast to other solvents that exhibit diffuse line-edges possibly from stray droplets and ink-splashing. Temperature-dependent transport measurements on the inkjet-printed Cyrene-based graphene films showed the conductivity to be largely temperature-invariant but at lower temperatures below 100 K, conductivity decreased, likely as a result of increased inter-membrane separation arising from thermal contraction. Additionally, temperature-dependent Raman spectroscopy showed the red-shift in the G-band, 2D-band and D-band peaks, as temperature increased. As a result, by validating flexion motion detection of the proximal interphalangeal joint demonstrated in this study, our work is the first of its kind to successfully additively manufacture inkjet-printed Cyrene-based graphene strain sensors on flexible substrates for bio-sensing and wearables.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗