Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “protocol”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23

A Transient-Response methodology based on experiments and modeling for Cu-Redox Half-Cycle kinetic analysis on a Cu-SSZ-13 SCR catalyst

In this report the Standard SCR reaction catalyzed by Cu-SSZ-13 is a redox process consisting of a reduction half cycle (RHC) and an oxidation half cycle (OHC) that cycle the active Cu sites between the Cu(II) and Cu(I) states. In the current work, a transient-response methodology consisting of experimental transient response Cu-redox (TRCR) measurements and kinetic modeling was developed for detailed study of individual SCR Cu-redox half cycles. The TRCR protocol allows quantification of the reducible Cu density, Cu(II)-Cu(I) partitioning, relative native RHC and OHC rates, and limiting half cycle during SCR. The half-cycle kinetics are studied over a wide (170–450 °C) temperature, and in differential segments along the catalyst length using spatially resolved capillary inlet mass-spectrometry (SpaciMS). The protocol alone provides two independent measures indicating that OHC increases faster than RHC with temperature, and that at all but the lowest temperature SCR is RHC limited. Introducing the transient kinetic models allows half-cycle reaction pathways, orders and activation energies to be studied and determined; e.g., RHC involves surface and gas-phase NH 3 routes, and OHC with O 2 involves dimer formation. Application of the full methodology provides further confirmation and quantification of the protocol conclusions, and specifically the first measurements of RHC and OHC activation energies; specifically for parallel RHC surface and gas-phase routes, and experimentally obtained OHC activation energy vs. first-principle calculations in the literature. Beyond insights related to the degreened catalyst studied here, the methodology provides a broadly available approach for quantifying how aging processes selectively impact RHC and OHC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Examining the effects of chemical cleaning, leaching, and partial dissolution on zinc and cadmium isotope fractionation in marine carbonates

The application of zinc (Zn) and cadmium (Cd) isotopes as palaeo-proxies in carbonate sediment is rapidly expanding due to their potential for tracing changes in biological productivity in the modern and past oceans. However, there are limited investigations into the chemical cleaning methods required to produce the most consistent and accurate data for these novel isotope systems. This could impact their use as palaeo-proxies to reconstruct ocean-atmosphere-climate interactions throughout Earth's history. To address this concern and expand the utility of the Zn and Cd stable isotope systems as palaeo-productivity tracers, the performance of two standard chemical cleaning protocols for acquiring robust and reliable Zn and Cd isotope datasets were assessed. These include (i) the Cd-cleaning method that uses a reductive step to selectively leach contaminating secondary iron (Fe)-manganese (Mn) (oxyhydr)oxide coatings from the carbonate surface, and an oxidative step that is used to remove post-depositional organic matter and sulphide precipitates; and (ii) the magnesium/calcium cleaning protocol that includes an oxidative step only, leaving secondary Fesingle bondMn (oxyhydr)oxide coatings largely intact. Well-preserved Holocene-, and Mesozoic-aged carbonate sediments were used to test the reliability of these two chemical cleaning methods. The Holocene samples comprised not only aliquots of bulk sediment, but also individual species of planktic and benthic foraminifera. Our results show that the best practice chemical cleaning method for retrieving consistent and accurate Zn and Cd isotope, and Zn/Ca and Cd/Ca datasets for carbonate sediments, requires both reductive and oxidative cleaning following the Cd-cleaning method. This differs from most methodological approaches applied to date that remove the reductive step from the chemical cleaning protocol and apply an oxidative step only, or no chemical cleaning at all. Inclusion of the reductive step in the chemical cleaning method typically shifts δ 66 Zn by ~0.1‰ lower and δ 114 Cd by 0.3‰ higher in the solid phase, while Zn/Ca and Cd/Ca typically decrease 2-fold. The benthic foraminifera, C. wuellerstorfi, that live in ocean bottom waters where the seawater Zn and Cd isotope composition is homogeneous display evidence of Zn and Cd isotope fractionation between seawater and carbonate on the order of 0.08 ± 0.08‰ (2SE, n = 4) and -0.25 ± 0.13‰ (2SE, n = 4), respectively, in agreement with experimental constraints. Furthermore, evidence of Zn isotope fractionation effects are recorded in a naturally-dissolved carbonate sediment, together with laboratory-controlled carbonate dissolution experiments. Based on these results, we recommend the Cd-cleaning method and the application of Zn and Cd isotope fractionation factors to accurately reconstruct past seawater Zn and Cd isotope compositions from carbonate sediments.

58 GEOSCIENCES↗

Toward equitable environmental exposure modeling through convergence of data, open, and citizen sciences: an example of air pollution exposure modeling amidst increasing wildfire smoke

Exposure modeling is critical in environmental epidemiology and human health but may face challenges (e.g., skewed data, unequal error, context-insensitive validation, and computational demands). Modeling decisions reflect the intended use of the models and the values that modelers prioritize. We aimed to provide a conceptual framework and machine learning (ML) modeling protocols that address these issues. With 500m-gridded hourly PM 2.5 and O 3 levels in Illinois before, during, and after the 2023 Canadian wildfire season as a motivating example, we conducted modeling experiments to evaluate modeling methods, guided by three domains we propose based on theories of science: 1) Data Diversity, leveraging open and citizen science data to enhance inclusivity, parsimony, and representativeness; 2) Equitable Accuracy, ensuring fairly distributed uncertainties across subpopulations; and 3) Sustainable Modeling, balancing accuracy with reducing computational demands to promote accessibility for under-resourced researchers. Here, we found that ML with publicly available data can achieve high accuracy. Depending on methods, performance may vary substantially, even with identical input data. Large but skewed data may reduce performance. Misuse of cross-validation protocols can underestimate prediction error; although we observed R 2 s of ∼98 %, the modeled estimates varied significantly, indicating the need for careful model validation. By using new modeling protocols including representativeness-considered training and validation data and a new loss function, we achieved high agreement between estimates and ground-based measurements (e.g., R 2 = ∼90 % for PM 2.5 ; ∼80 % for O 3 ), equally distributed errors across sociodemographic strata and urban–rural divides, and reduction in computation time—from several weeks or months to a few days.

Exposure assessment↗

Chemical specificity in polyzwitterion-polyelectrolyte coacervates: polycations vs polyanions

Aqueous solutions containing polyzwitterions and polyelectrolytes were studied to probe the effects of chemical specificity on the complexation among different types of chains and resulting liquid–liquid phase separation (coacervation). Two kinds of blends were studied. A polyzwitterion, poly(sulfobetaine methacrylate) (PSBMA), was blended with either (1) a polycation, poly(diallydimethylammonium chloride) (PDADMAC), or (2) a polyanion, sodium poly(styrene sulphonate) (NaPSS). Coacervation was observed in both blends after equilibration, while the freshly prepared blend solutions exhibited distinct behavior dependent on the mixing protocols. Mixing solutions of the polyzwitterions and the polycations led to coacervation almost instantaneously. In contrast, the blends containing the same polyzwitterions and the polyanions exhibited no such coacervation by following the same mixing protocol. However, blends prepared after dissolving a solid mixture containing the same polyzwitterions and the polyanions in water exhibited coacervation. Based on the small-angle X-ray scattering (SAXS), cryo-electron microscopy, and rheology measurements, complexation and coacervation in the polyzwitterion-polyelectrolyte blends is rationalized in terms of the entropy change associated with reorganization of solvent (water), which appears to be larger in the polyzwitterion-polycation case in comparison with the polyzwitterion-polyanion blends. These results highlight the importance of chemical specificity and mixing protocols leading to metastable structures in designing a new class of materials based on macromolecular complexation of polyzwitterions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Separation of thorium, uranium, and rare earths from a strip solution generated from coarse coal refuse

This work systematically evaluated the separation performance of thorium, uranium, and rare earths when coal and coal byproduct were utilized as a non-traditional feedstock for rare earth production. A synthetic solution was prepared following the elemental composition of a strip solution generated from coarse coal refuse and used throughout the study. Various separation techniques including selective precipitation, solvent extraction, and a modified experimental protocol incorporating the two were applied to extract rare earths while minimizing the non-selective recovery of thorium and uranium into the product stream. Test results indicate that selective precipitation was effective for the removal of thorium at a pH value approaching 5 while solvent extraction preferentially removed uranium from rare earths. Therefore, a modified experimental protocol consisting of both selective precipitation and solvent extraction was subsequently developed and implemented. The findings following the modified experimental protocol indicate that extractant concentration, solvent extraction feed pH, and organic to aqueous (O/A) ratio all played a significant role in the removal of uranium. Among all the tests conducted, the best separation performance was achieved using a one-stage precipitation at a pH value of 4.8, 50 v% TBP, solvent extraction feed pH of 3.5, and an O/A ratio of 3, which corresponded to an overall rare earth, thorium, and uranium recovery of 79.6%, 0%, and 3.1%, respectively.

01 COAL, LIGNITE, AND PEAT↗

Role of washing solvents in defining the magnetic performance of Sm 2 Fe 17 N 3 obtained by reduction diffusion

Calciothermic reduction-diffusion (RD) is one of the leading synthesis methods for Sm 2 Fe 17 N 3 (SmFeN), but sintering of such RD-derived powders is hampered by oxidation and CaO byproducts. Here, we systematically evaluate how post-synthesis washing solvents govern powder chemistry and magnetic performance. RD-synthesized SmFeN powder was washed with several aqueous and non-aqueous solvents using identical washing protocols. This was followed by an assessment of both the as-washed powder as well as after annealing at 425 °C. Phase content was quantified by synchrotron PXRD and Rietveld refinement and SEM/EDS. The oxygen content of the powders was determined via inert gas fusion and the magnetization by DC magnetometry. While all aqueous-based solutions were able to remove CaO, the non-aqueous solutions were only effective with extended washing times. Prior to annealing, the crystalline phases and magnetic properties of the as-washed powders were largely the same regardless of washing solvent. However, differences emerge after annealing, where water-based washing markedly lowers the coercivity (H c ~3.8-4.3 kOe). In contrast, non-aqueous NH 4 Cl-methanol washing protocols were more effective in preserving coercivity (H c ~4.9-5.9 kOe). We attribute this to lower oxygen content in the non-aqueous samples (~9,400 ppm v ~6,400 ppm, respectively) which in turn reduced the formation of α-Fe during annealing. These results highlight the importance of solvent choice in washing RD-synthesized SmFeN and demonstrate that non-aqueous protocols, which better limit oxidation, outperform aqueous solvents despite the need for longer washing times.

36 MATERIALS SCIENCE↗

A systematic analytical framework for multi-source municipal solid waste characterization for energy recovery

Advancing municipal solid waste (MSW) management from disposal-oriented practices toward circular, value-driven systems requires standardized methodologies capable of identifying material composition and resource recoverable potential at the point of generation. Despite extensive research, MSW characterization remains fragmented due to inconsistences in sampling methodologies, waste sorting categories, and temporal coverage across previous studies which limit cross-site comparability, reproducibility, and constrain the reliable evaluation of potential resource recovery pathways. This lack of consistency has hindered the development of a unified framework for MSW characterization and resource assessment. This study introduces a standardized, field-validated protocol for MSW sampling and composition analysis that ensures consistent, traceable data across diverse waste sources. The protocol integrates randomized spatial sampling, systematic material sorting, and controlled subsampling for multi-site and multi-season field campaigns. Validation included MSW collection from residential, grocery, restaurant, and school MSW streams across five U.S. states, including Maryland, Idaho, Virginia, Ohio, and Mississippi, to demonstrate the protocol’s ability to identify source-based composition patterns relevant to resource recovery applications. Grocery and restaurant streams were dominated by food waste and high-moisture organics, while school waste contained higher paper content and residential waste showed greater heterogeneity. Aggregation into energy-relevant fractions highlighted practical recovery pathways via anaerobic digestion or gasification, supporting data-driven planning, policy, and circular economy strategies for sustainable waste management across waste sources.

09 BIOMASS FUELS↗

Novel data analysis method for obtaining better performance from a complex 3D-printed collimator

Additively manufactured scattered beam collimators are increasingly being employed to boost the sample to cell peak signal ratio in high pressure neutron diffraction studies because of manufacturing versatility and performance improvements. We study how the measured diffraction pattern is affected by the presence of a collimator downstream of the sample, and develop a novel protocol that provides more effective background rejection. This protocol takes into account critical performance-determinants that were identified in this study, namely: (i) effectively identifying the collimator pattern on the detector; (ii) understanding the dependence of this pattern on sample and cell composition; and (iii) accurately identifying and differentiating the different regions of the pattern on the detector based on the dependency of the cell or sample and finally (iv) resolving the intensities at regions of the detector where neutrons scattered from the sample are preferentially represented, in order to boost the sample to cell peak signal ratio. Application of this novel analysis protocol is shown to increase the collimator performance over the traditional method.

3D printing↗

Capturing the Page curve and entanglement dynamics of black holes in quantum computers

Quantum computers are emerging technologies expected to become important tools for exploring various aspects of fundamental physics in the future. Therefore, we pose the question of whether quantum computers can help us to study the Page curve and the black hole information dynamics, which has been a key focus in fundamental physics. In this regard, we rigorously examine the qubit transport model, a toy qubit model of black hole evaporation on IBM’s superconducting quantum computers, to shed light on this question. Specifically, we implement the quantum simulation of the scrambling dynamics in black holes using an efficient random unitary circuit. Furthermore, we employ the swap-based many-body interference protocol and the randomized measurement protocol to measure the entanglement entropy of Hawking radiation qubits in this model. Finally, by incorporating quantum error mitigation techniques into our challenging implementation of entanglement entropy measurement protocols on the IBM quantum hardware, we accurately determine the Rényi entropy in the qubit transport model, thus showcasing the utility of quantum computers for future investigations of complex quantum systems.

97 MATHEMATICS AND COMPUTING↗

High-Resolution Tandem Mass Spectrometry-Based Analysis of Model Lignin–Iron Complexes: Novel Pipeline and Complex Structures

Understanding the chemical nature of soil organic carbon (SOC) with great potential to bind iron (Fe) minerals is critical for predicting the stability of SOC. Organic ligands of Fe are among the top candidates for SOCs able to strongly sorb on Fe minerals, but most of them are still molecularly uncharacterized. To shed insights into the chemical nature of organic ligands in soil and their fate, this study developed a protocol for identifying organic ligands using ultrahigh-performance liquid chromatography-high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) and metabolomic tools. The protocol was used for investigating the Fe complexes formed by model compounds of lignin-derived organic ligands, namely, caffeic acid (CA), p-coumaric acid (CMA), vanillin (VNL), and cinnamic acid (CNA). Isotopologue analysis of 54/56 Fe was used to screen out the potential UHPLC-HRMS (m/z) features for complexes formed between organic ligands and Fe, with multiple features captured for CA, CMA, VNL, and CNA when 35/37 Cl isotopologue analysis was used as supplementary evidence for the complexes with Cl. MS/MS spectra, fragment analysis, and structure prediction with SIRIUS were used to annotate the structures of mono/bidentate mono/biligand complexes. The analysis determined the structures of monodentate and bidentate complexes of FeL x Cl y (L: organic ligand, x = 1–4, y = 0–3) formed by model compounds. The protocol developed in this study can be used to identify unknown organic ligands occurring in complex environmental samples and shed light on the molecular-level processes governing the stability of the SOC.

54 ENVIRONMENTAL SCIENCES↗

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time-consuming and Edisonian processes. Information gleaned from the scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, here we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ~35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles’ text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% accuracy on morphology prediction, 0.99 AUC on protocol identification, and up to a 0.87 F1-score on chemical entity recognition. In addition to processing articles’ text, microscopy images of nanomaterials within the articles are also automatically identified and analyzed to determine the nanomaterials’ morphologies and size distributions. To enable users to easily explore the database, we developed a complementary browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design.

36 MATERIALS SCIENCE↗

Molecular Dynamics Driven by the Many-Body Expansion (MBE-MD)

Here, we present a protocol for classical and nuclear quantum dynamics, in which the energies and forces are generated by the many-body expansion (MBE), and apply it to water clusters using the TTM2.1-F and MB-Pol interaction potentials at various temperatures. We carry out MBE-MD classical and nuclear quantum dynamical simulations in which the energies and forces of the full system are approximated by the 2-, 3-, and 4-body terms of the MBE expansion and compare the average potential and the vibrational density of states (VDOS) with the full simulation, i.e., the one for which no MBE is used. Our results indicate that the thermally averaged potential energy from the MBE up to the 4-body term converges with near identical behavior to the one from the full simulation. The 3-body makes a substantial contribution (~20%) to the energy, whereas the 4- body is necessary for obtaining quantitatively accurate energetics and forces, albeit making a small contribution to each (~2%). We further show that the harmonic frequencies are reproduced to within a few wavenumbers (cm -1 ) at the 4-body level, and that the slowest modes to converge with the MBE rank are those involving the strongest hydrogen bonds. Anharmonicity exacerbates this effect, so that a 4-body description of the energies and forces is needed to achieve accurate anharmonic vibrational frequencies in the hydrogen-bonded OH-stretching region. We also discuss the asymptotic scaling of the MBE-MD protocol with respect to the cost of the underlying potential energy evaluation, suggesting that electronic structure methods that scale at least as N 4 ,N being the size of the system, are needed to result in savings over the traditional full MD simulation. We anticipate that MBE-MD protocol can evolve into a powerful and practical method, which will allow for highly accurate ab initio MD simulations on a much broader range of molecular systems than can be currently handled.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation

Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn–Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structures and chemical bonds of such systems. In the complete active space self-consistent field (CASSCF) method, one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. Here, we present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a “black-box” mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Writing the Programs of Programmable Catalysis

It has long been known that non-steady state and periodic catalytic reactor operation in terms of temperature, pressure, and composition can lead to higher overall productivity and/or product selectivity than the best steady operation. Recently, the emergence of catalysts whose intrinsic properties can be made to oscillate with time, introduces advanced forcing capabilities that can be “programmed” into the catalysts to broaden the scope and applicability of periodic operation to surface chemistry. In this work, an algorithmic approach is implemented to significantly accelerate the discovery and optimization of periodic steady states of catalytic reactors. Decomposition of complex dynamics into fundamental mechanistic fast–slow steps is seen to improve conceptual understanding of the relationship between binding energy oscillation protocols and overall catalytic rates. Finding structured forcing protocols, optimally tailored to the multiple time scales of a given individual mechanism, requires an efficient search of high-dimensional parameter spaces. Here, this is enabled here through active learning (Bayesian optimization, enhanced by our proposed Bayesian continuation). Implementation of these methods is shown to accelerate the evaluation of catalyst programs by up to several orders of magnitude. Faster screening of programmable catalysts to discover periodic steady states enables the optimization of catalytic operating protocols and thus opens the possibility for catalyst engineering based on optimal forcing programs to control rate and product selectivity, even for complex multistep catalytic mechanisms.

catalysis↗

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗

The Terrestrial Biosphere Model Farm

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

54 ENVIRONMENTAL SCIENCES↗

Onboard early detection and mitigation of lithium plating in fast-charging batteries

Fast-charging is considered as one of the most desired features needed for lithium-ion batteries to accelerate the mainstream adoption of electric vehicles. However, current battery charging protocols mainly consist of conservative rate steps to avoid potential hazardous lithium plating and its associated parasitic reactions. A highly sensitive onboard detection method could enable battery fast-charging without reaching the lithium plating regime. Here, we demonstrate a novel differential pressure sensing method to precisely detect the lithium plating event. By measuring the real-time change of cell pressure per unit of charge (dP/dQ) and comparing it with the threshold defined by the maximum of dP/dQ during lithium-ion intercalation into the negative electrode, the onset of lithium plating before its extensive growth can be detected with high precision. In addition, we show that by integrating this differential pressure sensing into the battery management system (BMS), a dynamic self-regulated charging protocol can be realized to effectively extinguish the lithium plating triggered by low temperature (0 °C) while the conventional static charging protocol leads to catastrophic lithium plating at the same condition. We propose that differential pressure sensing could serve as an early nondestructive diagnosis method to guide the development of fast-charging battery technologies.

25 ENERGY STORAGE↗

Measuring error rates of mid-circuit measurements

High-fidelity mid-circuit measurements, which read out the state of specific qubits in a multiqubit processor without destroying them or disrupting their neighbors, are a critical component for useful quantum computing. They enable fault-tolerant quantum error correction, dynamic circuits, and other paths to solving classically intractable problems. But there are few methods to assess their performance comprehensively. In this work, we address this gap by introducing the first randomized benchmarking protocol that measures the rate at which mid-circuit measurements induce errors in many-qubit circuits. Using this protocol, we detect and eliminate previously undetected measurement-induced crosstalk in a 20-qubit trapped-ion quantum computer. Then, we use the same protocol to measure the rate of measurement-induced crosstalk error on a 27-qubit IBM Q processor, and quantify how much of that error is eliminated by dynamical decoupling.

Hothem, Daniel [Sandia National Laboratories (SNL-↗