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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2025

Applying deep learning methods to develop new models of molecular charge transfer, nonadiabatic dynamics, and nonlinear spectroscopy in the condensed phase

Photon- and field-induced charge transfer has central importance in the generation and storage of electricity, the novel properties of materials, photo-induced catalysis, and electro-optic activity (e.g., photovoltaic cells, fuel cells, and organic chromophores for use in optical fibers and light-emission diodes). These non-equilibrium electronic and chemical transformations are probed by ultrafast, nonlinear spectroscopies. Accurate simulations play a crucial role in our ability to understand, optimize, and control these transformations. This project applies modern deep learning and machine learning (ML) methods to dramatically improve models of electronic dynamics, electronic-nuclear dynamics, and spectroscopic measurements for improved simulations of chemistry in complex environments, far from equilibrium phenomena, and processes in extreme environments, such as materials exposed to strong or resonant fields. This project develops accurate neural net models that go beyond predictive capability to also provide new insight into the fundamental physics underlying electron and nuclear dynamics. To achieve its objectives, this project explores and develops customized versions of high-capacity deep learning algorithms/models. These techniques are developed with an emphasis on fundamental chemical insight, not just predictive accuracy, to assist the development of the next generation of quantum simulation methods.

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

Reducing NOx Emissions in Ammonia Combustors

Ammonia continues to attract growing interest as a carbonneutral replacement fuel, motivating numerous research efforts toward understanding fundamental ammonia combustion characteristics. A major challenge for the use of ammonia is the development of combustor technologies for mitigating potentially high NOx emissions from the fuel-bound nitrogen chemical pathways to acceptable levels. Our work focuses on a staged RQL combustor architecture for minimizing the NOx emission levels through burning fuel-rich in the primary stage to formcombustion products containing significant levels of hydrogen in addition to nitrogen and water with minimal NOx formation. The subsequent quench and burnout stages of the combustor must then quickly burn residual hydrogen with flame-temperatures moderated by nitrogen and water forming in the first stage. Chemical Reactor Network modeling was used to understand and identify optimal stoichiometry and residence times in each stage for minimizing NOx emissions and to quantify pressure and temperature effects. Reducing the overall NOx emissions requires relatively long residence times in the primary stage to achieve near equilibrium NO levels due to kinetically controlling processes. For conditions relevant to gas turbines (e.g., 30 atm), our work indicates that NOx emissions below 20 ppm are theoretically achievable in a staged RQL combustor architecture. However, these emission predictions significantly depend on the accuracies of currently available chemical kinetic mechanisms which have not been extensively validated under elevated pressure and temperature conditions relevant to gas turbines.

10 SYNTHETIC FUELS↗
DOE OSTI2024

Development of prechamber enabled mixing-controlled combustion strategy for ultra-low methane emissions from lean burn natural gas engines

This numerical study explores the optimization of Prechamber Enabled Mixing-Controlled Combustion (PC-MCC) using natural gas in heavy-duty engines, aiming to enhance combustion efficiency to minimize methane slip and NOx emissions. The approach involves a prechamber ignition system, distinct from conventional spark ignition (SI) systems, to initiate combustion of direct injected natural gas. By leveraging the robust ignition characteristics of the prechamber, the PC-MCC method demonstrates significant potential in achieving efficient combustion akin to diesel engines but with lower greenhouse gas emissions. The research evaluates the effects of various geometric and operational parameters on the combustion process and emissions, including prechamber volume, nozzle diameter, direct injector (DI) geometry, and engine operating strategies. Computational Fluid Dynamics (CFD) simulations are utilized, focusing on a heavy-duty, single-cylinder engine modeled after the Caterpillar C9.3B engine. Key findings indicate that a prechamber volume of 3 cc, coupled with a nozzle diameter of 2.75 mm for two prechamber holes, strikes an optimal balance between combustion efficiency and emissions reduction. This configuration ensures robust combustion across a range of operating conditions while maintaining methane slip within targeted limits. Further investigation into DI geometry shows the significance of the injector umbrella angle and nozzle diameter in shaping the fuel-air mixing and combustion dynamics. An umbrella angle of 130° and a nozzle diameter of 300 microns are identified as optimal, promoting rapid and efficient combustion with minimized methane and NOx emissions. The study also investigates the impact of injection timing and pressure, highlighting their roles in controlling combustion timing and influencing emissions levels. Advanced injection timing is found to be crucial in achieving the desired low methane slip, whereas retarded injection timing assists to reduce NOx emissions while having a slight increase in methane emissions. Operating strategies incorporating various levels of Exhaust Gas Recirculation (EGR) are assessed for their effectiveness in further reducing emissions. The research demonstrates that a judicious combination of internal hot EGR and careful calibration of DI pressure and SOI timing can achieve significant reductions in NOx emissions while keeping methane slip under control. Specifically, an internal EGR level of 15%–25%, combined with DI pressures of 200–300 bar and injection timings at or after top dead center, is recommended. These findings contribute valuable insights into the development of advanced combustion techniques for natural gas engines, offering a viable pathway to reduce methane slip without compromising engine efficiency or performance. The PC-MCC system presents a promising solution for the future of heavy-duty natural gas engine technology to reduce methane emissions.

Nsaif, Osama↗
DOE OSTI2024

Tuning austenite stability through prior microstructure control in a low-alloy Q&P steel

Quenching and partitioning (Q&P) processing is a widely accepted heat treatment methodology for creating high strength steels consisting of ferrite, martensite, and austenite, while maintaining relatively low manufacturing costs. Though the research on effects of prior microstructure is limited, an understanding of the heat treatment response of different starting microstructures is critical to processing and creating steels with complex microstructures that contain retained austenite and may afford opportunities to further optimize properties. This study investigates the influence of starting microstructure (ferrite/pearlite versus martensite) and prior levels of cold work (38 verses 58 %) on the microstructural development and mechanical properties of a 0.2 C-2.0 Mn-1.5 Si (wt.%) steel exposed to Q&P processing. Samples with a starting martensitic microstructure resulted in higher retained austenite fractions and a more homogeneous microstructure after Q&P processing compared to a starting microstructure of ferrite-pearlite. Starting martensitic microstructures also displayed higher work hardening rates and higher uniform elongations. Larger cold reductions saw accelerated dissolution kinetics and austenite formation during intercritical annealing, resulting in more similar final microstructures from the ferrite-pearlite and martensitic starting microstructures. Finally, the results presented here indicate that varying prior processing can be a route to manipulate and control austenite stability in a Q&P processed steel.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Process-driven roadmap for depositing super duplex stainless steel via wire Arc additive manufacturing

Here, this study systematically investigates the effects of shielding gas, bead spacing, weld mode, and travel speed on the phase balance, porosity, and hardness of wire arc additively manufactured (WAAM) super duplex stainless steel (ER2594). Robotic WAAM was employed to fabricate multilayer walls under systematically varied process conditions, followed by phase transformation simulations, X-ray computed tomography (XCT), electron backscatter diffraction (EBSD), and microhardness evaluation. Thermodynamic simulations predicted rapid cooling of AM process can suppress the potential formation of deleterious precipitates which was later validated via cross-sectional microstructure analyses of printed samples. XCT revealed porosity levels below 0.2% for all deposits, with reduced travel speed significantly lowering defect volume. Microstructural analyses revealed the evolution of various austenite precipitates, including grain boundary austenite (GBA), Widmanstätten austenite (WA), and intergranular austenite (IGA), sequentially upon cooling of the ferrite phase. Among all process parameters, weld transfer mode exhibited the strongest influence on phase balance; pulsed mode promoted higher ferrite retention (~ 36%) compared to RapidX mode. No consistent relationship between stabilized phase fraction and captured microhardness was observed. This work provides critical insights for optimizing WAAM parameters to control phase balance and mechanical performance, which is essential for producing high-integrity super duplex stainless-steel components for nuclear and marine applications.

Grain orientation↗
DOE OSTI2026

Paradigm for universal quantum information processing with integrated acousto-optic frequency beamsplitters

Frequency-bin encoding offers tremendous potential in quantum photonic information processing, in which a single waveguide can support hundreds of lightpaths in a naturally phase-stable fashion. This stability, however, comes at a cost: arbitrary unitary operations can be realized by cascaded electro-optic phase modulators and pulse shapers, but require nontrivial numerical optimization for design and have thus far been limited to discrete tabletop components. In this article, we propose, formalize, and computationally evaluate a new paradigm for universal frequency-bin quantum information processing using acousto-optic scattering processes between distinct transverse modes. We show that controllable phase matching in intermodal processes enables 2 × 2 frequency beamsplitters and transverse-mode-dependent phase shifters, which together comprise cascadable FRequency-transverse-mODe Operations (FRODOs) that can synthesize any unitary via analytical decomposition procedures. Modeling the performance of both random gates and discrete Fourier transforms, we demonstrate the feasibility of high-fidelity quantum operations with existing integrated photonics technology, highlighting prospects of parallelizable operations achieving 100% bandwidth utilization. Our approach is realizable with CMOS technology, opening the door to scalable on-chip quantum information processing in the frequency domain.

Lukens, Joseph M. [Purdue Univ., West Lafayette, I↗
DOE OSTI2026

HYBRID COMPOSITES VIA CO-EXTRUSION ADDITIVE MANUFACTURING-COMPRESSION MOLDING FOR PERFORMANCE OPTIMIZATION

The growing demand for hybrid polymer composites with multifunctional properties has led to the development of various hybridization techniques, such as multi-material compounding and controlled laminate stacking sequence. In this study, a novel hybrid manufacturing approach was used by integrating a multiplexing extrusion system (MExS) based on additive manufacturing with subsequent compression molding process. This technique enabled the co-extrusion of different materials during the additive manufacturing process to fabricate composites with tailored performance. The developed hybrid composite featured a skin layer of glass fiberreinforced polycarbonate (PC/GF) encapsulating a carbon fiber-reinforced acrylonitrile butadiene styrene (CF/ABS) core. The structure was engineered to promote improved thermal and impact resistance at the surface, supported by a stiff core for enhanced overall mechanical integrity. Mechanical, thermal and morphological properties of the hybrid composites were investigated to understand trade-offs in performance compared to a single-material system. The results demonstrate that this approach enables the production of multifunctional composites suitable for applications such as automotive body panels and protective housings, where a balance of weight, mechanical strength, and thermal performance is essential.

Wasti, Sanjita [ORNL]↗
DOE OSTI2026

Process design for recovering rare-earth elements from mine tailings with low rare-earth concentrations via sequential leaching and solvent extraction

Rare earth elements (REEs) are essential for advanced technologies and yet face significant supply chain risks due to their concentrated global production and limited domestic availability. Addressing this challenge requires efficient processes capable of upgrading low-grade secondary resources such as mine tailings. In this study, we developed a novel separation flowsheet that integrates sequential leaching and 2-stage solvent extraction (SX) processes to recover high-purity heavy REEs (HREEs) and light REEs (LREEs) from a simulated mine-tailing concentrate containing 2.4 wt% total REEs (TREEs; 0.6 wt% LREEs and 1.8 wt% HREEs). Sequential leaching with controlled pH adjustment selectively precipitated REEs while retaining the large amount of impurities in the solution, producing an REE-enriched leachate by following leaching processes with roughly twice the REE concentration and half the impurity concentration compared to that of single-step leaching. The optimized SX flowsheet employed Cyanex 572 to extract HREEs and Fe over LREEs, followed by Fe removal using tributyl phosphate (TBP), while the raffinate stream was processed by SX with di(2-ethylhexyl)phosphoric acid (D2EHPA) to recover LREEs under optimized conditions balancing both extraction efficiency and purity. Although increased extractant availability in the organic phase improved LREE recovery, it also increased co-extraction of Ca, underscoring trade-offs in process optimization. Both HREE- and LREE-rich solutions were subsequently precipitated into solid products via oxalate precipitation, resulting in high-purity REE solids containing ∼92.0 wt% HREEs (∼95.7 wt% TREEs) and ∼92.8 wt% LREEs (∼94.0 wt% TREEs). In conclusion, this proof-of-concept study using simulated mine tailings demonstrates a promising approach for upgrading low-grade REE resources, while highlighting the need for future validation with real materials.

Mine tailings↗
DOE OSTI2025

Performance Evaluation of LPBF Manufactured 316H Components

This work represents the continuation of a benchmark study that includes modeling, fabrication and characterization as demonstration to support industry’s adoption of advanced manufacturing processes in a variety of structures. This comprehensive study investigated the feasibility of using additive manufacturing (AM) technologies, specifically Laser Powder Direct Energy Deposition (LP-DED) and Laser Powder Bed Fusion (LPBF), to produce complex nuclear microreactor components using 316H stainless steel. The research focused on manufacturing an expanded elbow pipe component with transitioning sections, which are traditionally difficult and costly to produce through conventional manufacturing methods. The overall study’s primary objectives are therefore demonstrating AM viability for nuclear applications, optimizing process parameters, developing comprehensive material characterization protocols, validating computational modeling approaches, and establishing manufacturing guidelines for complex geometries. Although the initial work included the phased approach of cubical, upscaled cylindrical components, it is to enable to obtain more knowledge for the printing of the expanded elbow structure. The project achieved significant progress in process development by successfully optimizing LP-DED parameters to achieve 99.16-99.97% relative density in 316H stainless steel components. Through systematic evaluation of sixteen cube samples with varied laser powers (400-700W) and scan speeds (600-900 mm/min), optimal processing windows were identified at 500-550W with 600-700 mm/min or 650-700W with 650-900 mm/min scan speeds. The DED manufactured 316H demonstrated mechanical properties comparable or superior to wrought materials, with Young's modulus ranging from 153-208 GPa and controlled microstructural characteristics including greater than 95% face-centered cubic (FCC) phases and engineered cellular structures with sizes between 3.23-6.17 µm.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Additive manufacturing for electrocaloric terpolymer thin films

Current heating, venting, and air conditioning (HVAC) systems have drawbacks of high energy consumption, large CO 2 emissions, and low efficiency. Electrocaloric (EC) cycles present an eco-friendly alternative by converting thermal energy to electrical energy. Defect-free thin films with uniform thickness are required to achieve optimal EC performance. A scalable thin film fabrication process is essential for integrating EC cycles into HVAC systems. This study introduces EC thin films prepared by electrospray (ES) processing, a manufacturing method that deposits EC polymer layer by layer using high voltage. The resulting films show superior thickness control, smoother surfaces, and improved thermal and electrical properties compared with solution casting. In addition, post-annealing at 120°C enhances the thermal and EC performance, with films achieving a temperature change (ΔT) of 3.6°C at 100 MV/m when tested near room temperature. With the potential for future scalability, the ES method offers a promising approach for fabricating EC thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Controlling Preferred Grain Orientation in Vapor-Deposited Metal-Halide Perovskite Thin Films

Metal-halide perovskites are promising semiconductor materials for light-absorber layers in solar cells due to their optimized band gap, defect tolerance, and relative ease of synthesis. In this work, we demonstrate the viability of vapor transport deposition (VTD), a solvent-free processing method, to realize polycrystalline films of both lead iodide (PbI 2 ) and methylammonium lead iodide (MAPbI 3 ) perovskites showing preferential crystallographic orientation. Notable is the difference in perovskite film texturing observed for sequentially and co-deposited VTD films of MAPbI 3 , as well as the lack of strong preferential orientation in solution-processed MAPbI 3 films. While sequential deposition of the perovskite precursors PbI 2 and methylammonium iodide (MAI) leads to a MAPbI 3 film with a limited preferential grain orientation, co-deposited MAPbI 3 films mirror the orientation of excess PbI 2 present in the film. The latter is confirmed by the similar dependence of preferential orientation for PbI 2 and co-deposited MAPbI 3 on underlayer roughness, as well as the emergence of preferential orientation in MAPbI 3 only when excess PbI 2 is present. Both MAPbI 3 and PbI 2 films show a strong preferred lattice orientation on smooth substrates, while on rougher substrates, this orientation is disrupted. Furthermore, these results are interpreted as excess PbI 2 frustrating the diffusion of MAI in the film, which would otherwise disrupt the preferred orientation, pointing to a notable difference in lattice orientation arising from processing technique and conditions.

Deposition↗
DOE OSTI2026

Octave-spanning terahertz quarter-wave plates based on over-coupled Fabry–Pérot resonances in reflective metal–dielectric–metal metasurfaces

Compact devices for broadband polarization control in the terahertz (THz) regime remain challenging to realize due to the intrinsic phase dispersion associated with birefringent materials and resonant structures. Here, we demonstrate achromatic THz quarter-wave plates with near-unity efficiency and operating under a wide range of incidence angles, based on over-coupled metal–dielectric–metal reflective metasurfaces. The devices operate as single-port anisotropic Fabry–Pérot cavities, in which the phase dispersion of the over-coupled resonances is engineered to maintain an approximately constant relative phase delay between orthogonal field components over a broad frequency range. Four complementary metasurface designs operating at an incidence angle of 45° collectively cover the 0.25–3 THz frequency range accessible to a typical THz time-domain spectroscopy system. Each device exhibits an approximately octave-wide operational bandwidth, with axial ratios below 3 dB and polarization-conversion efficiencies exceeding 80% across most of the operating band. Systematic structural optimization suppresses coupling to diffraction and guided-wave modes, thereby further extending the usable bandwidth while preserving the required phase relationship between orthogonal polarizations. The demonstrated metasurfaces are compatible with wafer-scale fabrication processes, and experimental results show excellent agreement with simulations. These findings establish over-coupled reflective metasurfaces as a robust, scalable, and versatile platform for broadband THz polarization control.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Integration of LIBS with Machine Learning for Real-Time Monitoring of Feedstock in H 2 Gasification Applications

This project, funded by the U.S. Department of Energy (DOE) – Office of Fossil Energy under Award Number DE-FE0032177, aimed to assess the feasibility of an integrated Laser-Induced Breakdown Spectroscopy (LIBS) system with advanced machine learning (ML) models for real-time characterization and potential control of hydrogen gasifiers running on waste materials as feedstocks. This was a multidisciplinary effort that encompassed the acquisition and standardized analysis of individual and blended feedstocks—comprising biomass, coal waste, and plastic waste, followed by the development of a dynamic LIBS bench system for material sample analysis and development of predictive ML models. Comprehensive laboratory testing enabled the creation of a robust elemental dataset that served as the foundation for ML model training. Techniques such as Random Forest, Gradient Boosting, Support Vector Regression, and Neural Networks were employed to predict key feedstock properties, including higher heating value (HHV), moisture content, thermal conductivity, and ash composition with high accuracy. The results were validated against experimental data and demonstrated strong potential for real-time application in gasifier control systems. The project concluded with a study on the integration of the LIBS+ML approach for gasifier control and a techno-economic analysis of the implementation of the approach into hydrogen (H 2 ) gasification systems. Dissemination of results was carried out at a DOE meeting. This work establishes a scalable framework for automated, in-line feedstock quality assessment, offering significant implications for process optimization and emissions reduction in hydrogen production.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Optimization of boron carbide density using direct current sintering for use as sputtering sources at the National Ignition Facility

Several experimental trials were conducted to determine optimal sintering parameters and compositions for purchased boron carbide powder using spark plasma sintering/direct current sintering. Sputtering targets used to create ablators for the National Ignition Facility have been unable to survive post processing procedures suggesting that in-house fabrication may yield better results due to extended control.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Process innovations to enable viable enzymatic poly(ethylene terephthalate) recycling

Enzymatic depolymerization of poly(ethylene terephthalate) (PET) has received considerable attention for closed-loop polyester recycling. However, current approaches for enzymatic PET recycling face challenges to achieve commercial viability with lower environmental impacts compared with virgin polyester manufacturing. Here, in this study, we present multiple process innovations for enzymatic PET recycling that enable economic and environmental feasibility. We show that substrate amorphization through extrusion and quenching is energy-efficient and enables near-quantitative enzymatic conversion in 50 h. Using ammonium hydroxide for pH control and thermolysis of the isolated diammonium terephthalate salt reduces the acid and base consumption by >99%, lowering annual operating expenses by 74%. Fed-batch processing increased ethylene glycol concentration, leading to a 65% reduction in energy consumption for ethylene glycol recovery. These improvements were modeled in an optimal process, with recycled PET estimated to be US$\$$1.51 kg -1 relative to US domestic virgin PET at US$\$$1.87 kg -1 and eliminating key life cycle obstacles to scale this technology.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Two-step spin-coating of vacancy-ordered double perovskites enables growth of thin films for electronic devices

Vacancy-ordered double perovskites (VODPs), such as Cs 2 TeX 6 (X = Cl, Br, I), are lead-free alternatives to conventional metal-halide perovskites (MHPs). One limitation of VODPs is the lack of processes to form thin films relevant for physical characterization and electronic devices. A two-step spin-coating method was developed for synthesizing high-quality films of Cs 2 TeBr 6 . Independently depositing CsBr and TeBr 4 enables high precursor concentrations and control over crystallization kinetics. By optimizing the spin-coating parameters, conversion of precursors to phase pure films was observed using structural and surface characterization methods. The growth of mixed-halide systems was investigated using alternative salts including CsCl and CsI. The formation of halide alloys was found to depend on the existence of routes to byproducts. Lastly, single carrier diodes of Cs2TeBr6 were designed following valence band characterization with photoelectron spectroscopy. Temperature-dependent space-charge-limited current measurements revealed that transport occurs by hopping and the hole mobility is 3.2 × 10 −5 cm 2 V −1 s −1 near room temperature. As a result, the insights from the 2-step procedure provide a pathway towards making semiconducting devices from VODPs.

Kuklinski, Owen [University of California, Santa B↗