Crystallography, Morphology, Electronic Structure, and Transport in Non-Fullerene/Non-Indacenodithienothiophene Polymer:Y6 Solar Cells
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To help SETO/DOE achieve its goals, the IBTS team proposed a project addressing system reliability by improving installation standards and quality management. The proposed approach was designed to help achieve measurable reductions in installation defect density and improvements in the performance of PV systems by optimizing design and installation of residential and commercial PV systems. This approach addressed the soft costs associated with installations and quality management. The project demonstrated improved system reliability and reduced PV system installation costs. The software developed improved operations, decreased risk, and increased the overall value of PV systems across their lifecycle. The project used several data collection methods, including extensive industry surveys, face-to-face high-level interviews at industry conferences, stakeholder teleconferences, and in-depth interviews conducted by IBTS staff. Results from the research found the industry needs a uniform assessment method for national providers to be more efficient; the software should support both code officials and installers; most industry stakeholders would find value in a centralized software system that allows them to collect, report, and review information on in-process and completed solar installations; and mobile solutions that bridge existing knowledge gaps with inspectors and integrate with existing methodologies (such as permitting software) are of great value. The software solution developed is web-based, allowing for national access, and is built on a Google Firebase platform that can handle significant users and data. It can be used onsite or remotely, allowing for code compliance to continue despite ongoing pandemic related delays or shutdowns for local economies. The information provided by the software tool allows users to uniformly assess a system for compliance and use that aggregated data to identify training topics or create internal process designed to improving issues and reducing occurrence. This solution has multiple benefits in managing quality at time of use and promoting an increase in future safety and quality through education. Perhaps most importantly, this software increases public safety by ensuring compliance of installed systems and allows for local AHJs to remotely engage specialized and qualified solar specific expertise for oversite of the installation in their jurisdictions. Data analysis provides the quality feedback loop identifying the root cause of failure and drives installation practices to improve through training and education, resulting in systems with higher performance, greater reliability, and reduced operations and maintenance costs. With the successful completion of this project, the industry can expect reduced soft costs and increased performance and safety and will ultimately benefit from longer performing systems that cost less to operate.
Legacy fixed route transit systems designed to serve commuters struggle to provide efficient and effective service for short neighborhood trips and for population groups unable to access and egress transit stops using active modes (e.g., elderly, disabled). Neighborhood on-demand transit (ODT) services using low-speed electric vehicles (LSEV) are an innovative technological solution that can help fill this gap in service (e.g., short, high-frequency trips) for diverse populations and trip types. This study evaluated user characteristics and travel behavior for a neighborhood ODT service (using LSEVs) in downtown St. Louis, Missouri using responses from a community survey (n=244), ridership data, and vehicle trajectory information. A comparative analysis between neighborhood ODT, fixed route transit, and transportation network companies (TNC) was also conducted from the perspectives of total travel time, cost, and greenhouse gas emissions. Ultimately, the goal of the analysis was to motivate and inform holistic public mobility systems where different services are optimized to meet specific community needs. Findings indicate that the neighborhood ODT was effective at reaching diverse populations (elderly (20%), lower income (27%), and households with limited access to private vehicles (34%)). ODT reduced total travel time by 32% compared to fixed route transit, produced 2.4 - 4.3 times less greenhouse gas emissions per passenger mile (compared to transit and TNCs), and was more affordable (free to users) than alternative options ($1 for transit, $10-12 for TNCs). Overall satisfaction rates were high, with 80% of respondents rating the service a 4 or 5 out of 5.
This project defined, developed, and tested the open standards-based requirements, engineering design and system integration of on and off-vehicle Vehicle-to-Grid technologies both on the grid and on the PEV, to assess their effectiveness in implementing the use cases that create value for the grid, which in turn, can be passed on to the ratepayers and the PEV owners. The project comprised of three key categories of work and four key sub-areas of work. The three key categories were: On-Vehicle V2G, Off-Vehicle V2G, and impact of V2G operation on the battery capacity degradation. The four key sub-areas of work under each of these categories were: defining requirements, implementing the technology, performing system integration testing, and estimating the value of each of the grid services under specific assumptions, on a per-vehicle and on an aggregated basis. The technical implementation demonstrated the validity of standards-based approach to grid integration that led to interoperability, in addition to assessing the operational details with grid constraints imposed at the transformer level. The technology implementation also identified the subtle gaps in the standards definition to close the feedback loop on the accuracy of the standards as written, which are being implemented as revisions to the appropriate protocols. The battery impacts assessment developed a test cycle that the batteries from a real PHEV were subjected to for both the mobility-only and mobility with V2G specific energy cycling. While the battery impact results show a clear incremental degradation in response to additional throughput for V2G application purposes, the amount of additional kWh that can be made available without exceeding the end-of-life capacity definition of the battery at its 10-year warranty period was found to be significant. Given that the PEV battery being exercised during testing was a PHEV battery (smaller energy capacity), and 44% of its usable energy (i.e., SOC) was utilized daily for V2G applications, the EVs with much larger batteries will fare much better, either in terms of extracting more value in terms of incremental kWh for V2G, or in terms of relative incremental degradation of the capacity through the battery lifespan. Further, this battery was the second-generation Lithium-Ion chemistry circa 2015, which is at least two generations ago. Continual improvements in electrochemistry and manufacturing techniques as well as on-board capacity will enable the future batteries to provide even more energy for non-mobility-related services. Finally, the valuation of V2G energy services at the premise, distribution, and the ISO/market level was carried out for both the on-vehicle and off-vehicle cases, to understand both the GHG mitigation and operational efficiency improvements in quantitative terms. The analysis indicates that between $\$400$ and $\$1400$ per year of total value to the grid can be realized. When netted of costs to implement V2G either on or off-vehicle, the net value is available to be shared between the ratepayers and the EV owners. Over the life of the EV, therefore, this value becomes significant and can easily provide both ratepayer and EV owner benefits more than the cost of the equipment, either on or off-board, in addition to providing GHG mitigation benefits both on the grid and on the mobility sides. For these benefits to accrue, the key learnings from both the on- and off-vehicle V2G parts of the project indicate the need for at-scale demonstrations through involvement of real EV owners to validate both the technical feasibility, interoperability, as well as real grid benefits by performing extensive data collection and analysis. This will help inform the grid planners, the program designs, and tariff designers, as well as automotive and equipment manufacturers how best to create their products to maximize grid, ratepayer, and EV owner value.
Abstract Operations and maintenance (O&M) of floating offshore wind farms (FOWFs) poses various challenges in terms of greater distances from the shore, harsher weather conditions, and restricted mobility options. Robotic systems have the potential to automate some parts of the O&M leading to continuous feature‐rich data acquisition, operational efficiency, along with health and safety improvements. There remains a gap in assessing the techno‐economic feasibility of robotics in the FOWF sector. This paper investigates the costs and benefits of incorporating robotics into the O&M of a FOWF. A bottom‐up cost model is used to estimate the costs for a proposed multi‐robot platform (MRP). The MRP houses unmanned aerial vehicle (UAV) and remotely operated vehicle (ROV) to conduct the inspection of specific FOWF components. Emphasis is laid on the most conducive O&M activities for robotization and the associated technical and cost aspects. The simulation is conducted in Windfarm Operations and Maintenance cost‐Benefit Analysis Tool (WOMBAT), where the metrics of incurred operational expenditure (OPEX) and the inspection time are calculated and compared with those of a baseline case consisting of crew transfer vessels, rope‐access technicians, and divers. Results show that the MRP can reduce the inspection time incurred, but this reduction has dependency on the efficacy of the robotic system and the associated parameterization e.g., cost elements and the inspection rates. Conversely, the increased MRP day rate results in a higher annualized OPEX. Residual risk is calculated to assess the net benefit of incorporating the MRP. Furthermore, sensitivity analysis is conducted to find the key parameters influencing the OPEX and the inspection time variation. A key output of this work is a robust and realistic framework which can be used for the cost‐benefit assessment of future MRP systems for specific FOWF activities.
CO 2 foam helps to increase the viscosity of CO 2 flood fluid and thus improve the process efficiency of the anthropogenic greenhouse gas’s subsurface utilization and sequestration. Successful CO 2 foam formation mandates the development of high-performance chemicals at close to reservoir conditions, which in turn requires extensive laboratory tests and evaluations. This work demonstrates the utilization of a microfluidic reservoir analogue for rapid evaluation and screening of commercial surfactants (i.e., Cocamidopropyl Hydroxysultaine, Lauramidopropyl Betaine, Tallow Amine Ethoxylate, N,N,N' Trimethyl-N'-Tallow-1,3-diaminopropane, and Sodium Alpha Olefin Sulfonate) based on their performance to produce supercritical CO 2 foam at high salinity, temperature, and pressure conditions. The microfluidic analogue was designed to represent the pore sizes of the geologic reservoir rock and to operate at 100 °C and 13.8 MPa. Values of the pressure drop across the microfluidic analogue during flow of the CO 2 foam through its pore network was used to evaluate the strength of the generated foam and utilized only milliliters of liquid. The transparent microfluidic pore network allows in-situ quantitative visualization of CO 2 foam to calculate its half-life under static conditions while observing if there is any damage to the pore network due to precipitation and blockage. The microfluidic mobility reduction results agree with those of foam loop rheometer measurements, however, the microfluidic approach provided more accurate foam stability data to differentiate the foaming agent as compared with conventional balk testing. The results obtained here supports the utility of microfluidic systems for rapid screening of chemicals for carbon sequestration or enhanced oil recovery operations.
In studying compensated semimetals, the two-band model has proven extremely useful in capturing electrical conductivity under magnetic field, as a function of density and mobility of electronlike and holelike carriers. However, it rarely offers practical insight into magnetothermoelectric properties. Here, in this work, we report the field dependence of thermoelectric (TE) coefficients in a highly compensated semimetal NbSb 2 , where we find the Seebeck (𝑆 𝑥𝑥 ) and Nernst (𝑆 𝑥𝑦 ) coefficients increase quadratically and linearly with applied magnetic field, respectively. Such field dependence was predicted in previous work that studied a system of two parabolic bands, within semiclassical Boltzmann transport theory when the following two conditions are simultaneously met: 𝜔 𝑐 𝜏 ≫ 1 and tan 𝜃 𝐻 ≪ 1, where 𝜔 𝑐 and 𝜏 refer to the cyclotron frequency and relaxation time, respectively, and 𝜃 𝐻 is the Hall angle. Under these conditions, we find the field dependence of the TE coefficients directly provides a relation between the electronlike (𝑛 𝑒 ) and holelike (𝑛 ℎ ) carrier densities, which in turn can be used to refine two-band model fitting. With this, we find the compensation factor ($\frac{|Δ𝑛|}{𝑛_{𝑒}}$, where Δ𝑛 = 𝑛 𝑒 −𝑛 ℎ ) of NbSb 2 is two orders of magnitude smaller than what was found in unrestricted fitting, resulting in a larger saturation-field scale for magnetoresistance. Within the same framework of the semiclassical theory, we also deduce that the thermoelectric Hall angle tan 𝜃 𝛾 = $\frac{𝑆_{𝑥𝑦}}{𝑆_{𝑥𝑥}}$ can be expressed as ($\frac{Δ𝑛}{𝑛_{𝑒}}$ ×𝜔 𝑐 𝜏) −1 , which serves as a parameter to predict the degree of compensation. Our findings offer crucial insights into identifying empirical conditions for field-induced enhancement of TE performance and into engineering-efficient thermoelectric devices based on semimetallic materials.
The Institute for Clean Energy Technology (ICET) at Mississippi State University is exploring the utility of machine learning in augmenting its mobile radiation surveying platforms, which are currently being developed as means to survey depleted uranium contaminated areas in support of remediation and decommissioning efforts. Mobile survey platforms provide a means to efficiently scan large areas of interest while reducing human exposure to radiation and other hazards. The survey platforms can also be used for scanning for any gamma emitting isotope in addition to depleted uranium. The spectral data that the platforms collect may be data starved with relatively low counts and poorly defined spectral features depending on the speed of the platforms and scintillation detector selection. Such data-starved spectra are difficult to use for isotope identification, requiring advanced knowledge of the possible radionuclides that could be present and environmental factors that could attenuate signals or introduce background noise. These factors in combination with the volume of survey data increases the time it takes to perform analysis of survey data when the source type is unknown. There are a number of algorithms in the field of machine learning that can be used to classify data that would be challenging and time-consuming for a human to identify. Supervised machine learning algorithms train models based on extensive amounts of human-labeled training data. Once sufficiently trained, these models can be used to quickly make high-fidelity predictions on new data. Convolutional neural networks are machine learning algorithms that excel in learning representations of 'shapes'. They do this by taking numerical input data and convolving them with spatial feature detectors referred to as filters. These filters are incrementally adjusted to reduce the prediction error on the data during the backpropagation step of training. Discussed in this paper is the development of a convolutional neural network classifier (CNNC) that can utilize spectral survey data for source discrimination and isotope identification. Bench-top laboratory experiments data using LaBr{sub 3}(Ce) scintillation detectors were used to train and evaluate the performance of the developed CNNC. The CNNC is capable of discriminating a variety of gamma emitting source types, differentiating different forms of uranium (depleted vs. natural), and estimating the amount of uranium for a known geometry. The discussed CNNC may be useful in scenarios where survey systems are deployed in situations where hazardous radioactive material maybe present, but the type is unknown. When used in remediation applications the CNNC can be used to screen-out false positives, helping reduce remediation costs. (authors)
Lean-burn gasoline and diesel engines can offer substantially higher fuel efficiency, good driving performance, and reduced carbon dioxide emission compared to stoichiometric gasoline engines. Various catalyst technologies have been developed to remove the pollutants from these engines. For example, a three-way catalyst (TWC) is used to remove hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx) from gasoline engines during the stoichiometric conditions. During the lean-burn conditions, a TWC or a diesel oxidation catalyst (DOC) is used to control HC and CO emissions. NOx is removed by either lean NOx trap catalyst (LNT) that can store NOx under lean conditions and reduce NOx under rich conditions, or selective catalytic reduction catalyst (SCR) that can selectively remove NOx with a reducing agent. Among the NOx reduction catalyst technologies, SCR offers a number of advantages, including excellent NOx reduction efficiency over a wide range of temperatures and overall lower system cost. In fact, the SCR technology using ammonia (NH3) as reductant has been proven effective and used commercially for the removal of NOx emissions from stationary sources since the 1970s. Currently, SCR is being used to meet the NOx emission standards for diesel engines in Europe and North America, and also being considered for meeting the future NOx emission standards for lean-burn gasoline engines. Because of the challenges associated with storage, handling and transportation of ammonia on a vehicle, aqueous urea solution (e.g., Diesel Exhaust Fluid, AdBlue) has been developed as ammonia storage compound for mobile applications. When the aqueous urea solution is sprayed into exhaust gas stream, urea is decomposed to release ammonia, which then reduces NOx over the downstream SCR catalyst. Although aqueous urea solution technology has enabled automakers and engine manufacturers to meet the current NOx emission standards, this process of releasing ammonia requires a hot exhaust gas and sufficient mixing, creating challenges for low temperature NOx emission control and aftertreatment system packaging. For these reasons, alternative technologies have been developed as ammonia sources (e.g., solid urea, ammonium carbamate, metal ammine chloride) during the past few years. These technologies promise more convenient handling and distribution of ammonia sources, and help maximize the low-temperature performance of SCR catalysts and reduce the overall system volume and weight. However, none of these alternative technologies can be successfully implemented without the industry consensus. Therefore, the USCAR SCR work group, which is comprised of representatives from GM, Ford, and Chrysler, has decided to investigate the potential alternative ammonia carriers, define common standard vehicle interfaces, and address personal and environmental safety concerns with part suppliers and chemical companies. Under this CRADA Project, USCAR and Battelle will investigate alternative ammonia carrier materials that are currently under development. Based on the data and information derived under the CRADA project, the USCAR SCR work group plans to build the consensus and make recommendations for the industry.
Lean-burn gasoline and diesel engines can offer substantially higher fuel efficiency, good driving performance, and reduced carbon dioxide emission compared to stoichiometric gasoline engines. Various catalyst technologies have been developed to remove the pollutants from these engines. For example, a three-way catalyst (TWC) is used to remove hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx) from gasoline engines during the stoichiometric conditions. During the lean-burn conditions, a TWC or a diesel oxidation catalyst (DOC) is used to control HC and CO emissions. NOx is removed by either lean NOx trap catalyst (LNT) that can store NOx under lean conditions and reduce NOx under rich conditions, or selective catalytic reduction catalyst (SCR) that can selectively remove NOx with a reducing agent. Among the NOx reduction catalyst technologies, SCR offers a number of advantages, including excellent NOx reduction efficiency over a wide range of temperatures and overall lower system cost. In fact, the SCR technology using ammonia (NH3) as reductant has been proven effective and used commercially for the removal of NOx emissions from stationary sources since the 1970s. Currently, SCR is being used to meet the NOx emission standards for diesel engines in Europe and North America, and also being considered for meeting the future NOx emission standards for lean-burn gasoline engines. Because of the challenges associated with storage, handling and transportation of ammonia on a vehicle, aqueous urea solution (e.g., Diesel Exhaust Fluid, AdBlue) has been developed as ammonia storage compound for mobile applications. When the aqueous urea solution is sprayed into exhaust gas stream, urea is decomposed to release ammonia, which then reduces NOx over the downstream SCR catalyst. Although aqueous urea solution technology has enabled automakers and engine manufacturers to meet the current NOx emission standards, this process of releasing ammonia requires a hot exhaust gas and sufficient mixing, creating challenges for low temperature NOx emission control and aftertreatment system packaging. For these reasons, alternative technologies have been developed as ammonia sources (e.g., solid urea, ammonium carbamate, metal ammine chloride) during the past few years. These technologies promise more convenient handling and distribution of ammonia sources, and help maximize the low-temperature performance of SCR catalysts and reduce the overall system volume and weight. However, none of these alternative technologies can be successfully implemented without the industry consensus. Therefore, the USCAR SCR work group, which is comprised of representatives from GM, Ford, and Chrysler, has decided to investigate the potential alternative ammonia carriers, define common standard vehicle interfaces, and address personal and environmental safety concerns with part suppliers and chemical companies. Under this CRADA Project, USCAR and Battelle will investigate alternative ammonia carrier materials that are currently under development. Based on the data and information derived under the CRADA project, the USCAR SCR work group plans to build the consensus and make recommendations for the industry.
This work presents a non-invasive method for parameterizing a physically motivated equivalent circuit model of lithium-ion batteries using operando electrochemical impedance spectroscopy and time-domain data. The proposed model consists exclusively of linear circuit elements, enabling computationally efficient simulation and real-time implementation on battery management system chips. By integrating frequency- and time-domain measurements, the model accurately estimates internal states such as the negative electrode potential, achieving a root mean square error of 12.3 mV during fast charging validation. Parameterization requires only rate tests with sinusoidal perturbations at three different ambient temperatures, making the approach experimentally accessible. The model reveals key insights into battery behavior, including rate-dependent overpotentials primarily governed by charge transfer kinetics at the positive electrode, and temperature-dependent impedance contributions from both charge transfer and solid-state diffusion processes. Validation using reference electrodes confirms the model’s ability to detect lithium plating onset and reproduce impedance behavior across a wide range of operating conditions. The approach enables in situ optimization of fast charging profiles and lays the foundation for future extensions incorporating aging effects and plating dynamics.
It is becoming increasingly urgent to develop and utilize novel, more efficient and stable materials for mobile and stationary emission control applications as the deleterious consequences of anthropogenic air pollution are becoming more evident and pressing. Tightening regulations, particularly related to automotive exhaust treatment, together with continued improvements in engine design, that result in lowering the engine operating temperatures and inadvertently lead to the release of an overwhelming proportion of pollutants during the cold start, present new challenges for materials design, specifically for oxidation catalysts. In particular, improvements in the low-temperature activity while maintaining catalyst stability at high temperatures are required from the next-generation catalyst. Typical catalysts for removal of pollutants from automotive exhaust streams incorporate platinum group metals (PGMs). They tend to be inefficient at low temperatures (below 250 °C), thus accounting for the cold start problem, yet sinter and lose their activity at high temperatures that are frequently encountered during catalyst operation. High PGM loadings are often employed to compensate for catalyst inefficiencies and fast degradation, ultimately resulting in high-cost catalytic converters. Here, we have developed a new approach for the design and formation of catalytic materials that allows for both significantly more efficient PGM incorporation and improved overall catalyst performance at reduced PGM loadings. The method provides control over the composition and geometry of the support through self-assembly of sacrificial composite template — “raspberry” polymeric colloids decorated with catalytic particles — accompanied by infiltration with metal-oxide precursor and subsequent removal of the colloids. This method simultaneously structures the porous network and organizes the catalytic particles within it. Uniquely, the resulting catalytic particles are partially embedded in the support matrix and partially exposed to the pore interior, producing catalytic sites that are both stable and accessible. Herein, the feasibility of this novel and versatile approach for automotive catalytic conversion is demonstrated: the studies include testing alumina-based raspberry-colloid-templated (RCT) catalysts containing Pd nanoparticles (RCT Pd/Al2O3) for oxidation of propane and carbon monoxide under simulated diesel exhaust conditions and hydrothermal aging at 800 °C for 50 h in the simulated stream. The RCT Pd/Al2O3 catalysts exhibit exceptional activity toward CO oxidation, reduced reaction onset temperature, and high stability to elevated temperatures (demonstrated through prolonged exposure to temperatures up to 950 °C) and reactive gas streams, without migration, sintering or loss of the precious metal NPs. Notably, the novel catalyst shows the same or slightly better performance than the commercial catalysts even when the PGM load is reduced by ~80 % compared to the commercial counterparts. These results provide confidence for the utilization of the RCT approach for the fabrication of robust nanostructured catalysts for next-generation, energy-efficient catalytic converters with improved performance at low and high temperatures and reduced costs. The RCT methodology is, in addition, highly generalizable, and can thus be applied for the design of a wide range of catalytic systems in the automotive sector and beyond.
Addressing pertinent and perplexing questions regarding why nonfullerene acceptors (NFAs) promote higher power conversion efficiencies (PCEs) than traditional fullerenes and how photoactive bulk heterojunction (BHJ) film morphology, charge photogeneration, and recombination dynamics dictate solar cell performance have stimulated many studies of polymer solar cells (PSCs), yet quantitative relationships remain limited. Better understanding in these areas offers the potential to advance materials design and device engineering, afford higher PCEs, and ultimate commercialization. Here we probe quantitative relationships between BHJ film morphology, charge carrier dynamics, and photovoltaic performance in model binary and ternary blend systems having a wide bandgap donor polymer, a fullerene, and a promising NFA. We show that optimal PC 71 BM incorporation in a PBDB-TF:ITIC-Th binary system matrix retains the original π-face-on orientation, ITIC-Th crystallinity and BHJ film crystallite dimensions, and reduces film upper surface ITIC-Th segregation. Such morphology changes together simultaneously increase hole (μ h ) and electron (μ e ) mobilities, facilitate light-activated ITIC-Th to PC 71 BM domain electron delocalization, reduce free charge carrier (FC) bimolecular recombination (BR) within PBDB-TF:ITIC-Th mixed regions, and increase FC extraction pathways viaPBDB-TF:PC 71 BM pairs. The interplay of these effects yields significantly enhanced inverted cell short-circuit current density (J SC ), fill factor (FF), and PCE. Unexpectedly, we also find that excessive PC 71 BM in the PBDB-TF:ITIC-Th binary system alters the PBDB-TF orientation to π-edge-on, increases large scale PC 71 BM-rich aggregations and BHJ upper surface PC 71 BM composition. Further, these morphology changes increase parasitic decay processes such as intersystem crossing from photoexcited PC 71 BM, compromising the J SC , FF, and PCE metrics. ITIC-Th X-ray diffraction reveals a unique sidechain-dominated molecular network with previously unknown sidechain-end group stacking, rationalizing the STEM and GIWAXS results, photophysics, and the high μ e . DFT computation reveals charge transfer networks within ITIC-Th crystallites, supporting excited-state electron delocalization from ITIC-Th to PC 71 BM. This structure–property understanding leads to a newly reported NFA blend with PCE near 17%.