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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

2012-2013 Delaware Valley Household Travel Survey

The 2012-2013 Delaware Valley Household Travel Survey collected data for multiple planning purposes such as the calibration of a new activity-based travel demand model. It features data from households across nine counties in the region, including southern New Jersey and southeastern Pennsylvania. The Delaware Valley Regional Planning Commission (DVRPC) sponsored the survey, which was administered by Abt Srbi Inc. A sampling strategy was designed to recruit households for survey participation that would best represent overall regional travel trends. Households were selected randomly, but with special consideration given to under-represented geographies and transit propensity. On their assigned travel day, households were asked to record all trips made within a 24-hour period. Additionally, select households were chosen to participate in a wearable global positioning system (GPS) technology-based component of the study. A total of 811 participants wore the GPS system.

1Hz data↗
DOE OSTI2022

Micro-spectroscopic and freezing characterization of ice-nucleating particles collected in the marine boundary layer in the eastern North Atlantic

Abstract. Formation of atmospheric ice plays a crucial role in the microphysical evolution of mixed-phase and cirrus clouds and thus climate. How aerosol particles impact ice crystal formation by acting as ice-nucleating particles (INPs) is a subject of intense research activities. To improve understanding of atmospheric INPs, we examined daytime and nighttime particles collected during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign conducted in summer 2017. Collected particles, representative of a remote marine environment, were investigated for their propensity to serve as INPs in the immersion freezing (IMF) and deposition ice nucleation (DIN) modes. The particle population was characterized by chemical imaging techniques such as computer-controlled scanning electron microscopy with energy-dispersive X-ray analysis (CCSEM/EDX) and scanning transmission X-ray microscopy with near-edge X-ray absorption fine-structure spectroscopy (STXM/NEXAFS). Four major particle-type classes were identified where internally mixed inorganic–organic particles make up the majority of the analyzed particles. Following ice nucleation experiments, individual INPs were identified and characterized by SEM/EDX. The identified INP types belong to the major particle-type classes consisting of fresh sea salt with organics or processed sea salt containing dust and sulfur with organics. Ice nucleation experiments show IMF events at temperatures as low as 231 K, including the subsaturated regime. DIN events were observed at lower temperatures of 210 to 231 K. IMF and DIN observations were analyzed with regard to activated INP fraction, ice-nucleation active site (INAS) densities, and a water activity-based immersion freezing model (ABIFM) yielding heterogeneous ice nucleation rate coefficients. Observed IMF and DIN events of ice formation and corresponding derived freezing rates demonstrate that the marine boundary layer aerosol particles can serve as INPs under typical mixed-phase and cirrus cloud conditions. The derived IMF and DIN parameterizations allow for implementation in cloud and climate models to evaluate predictive effects of atmospheric ice crystal formation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Utilizing Expert Knowledge in Estimating Future STS Costs

A method of estimating the costs of future space transportation systems (STSs) involves classical activity-based cost (ABC) modeling combined with systematic utilization of the knowledge and opinions of experts to extend the process-flow knowledge of existing systems to systems that involve new materials and/or new architectures. The expert knowledge is particularly helpful in filling gaps that arise in computational models of processes because of inconsistencies in historical cost data. Heretofore, the costs of planned STSs have been estimated following a "top-down" approach that tends to force the architectures of new systems to incorporate process flows like those of the space shuttles. In this ABC-based method, one makes assumptions about the processes, but otherwise follows a "bottoms up" approach that does not force the new system architecture to incorporate a space-shuttle-like process flow. Prototype software has been developed to implement this method. Through further development of software, it should be possible to extend the method beyond the space program to almost any setting in which there is a need to estimate the costs of a new system and to extend the applicable knowledge base in order to make the estimate.

Fortner, David B.↗
NASA NTRS2002

Agent Based Modeling of Collaboration and Work Practices Onboard the International Space Station

The International Space Station is one the most complex projects ever, with numerous interdependent constraints affecting productivity and crew safety. This requires planning years before crew expeditions, and the use of sophisticated scheduling tools. Human work practices, however, are difficult to study and represent within traditional planning tools. We present an agent-based model and simulation of the activities and work practices of astronauts onboard the ISS based on an agent-oriented approach. The model represents 'a day in the life' of the ISS crew and is developed in Brahms, an agent-oriented, activity-based language used to model knowledge in situated action and learning in human activities.

Acquisti, Alessandro↗
DOE OSTI2026

Comparing Delay-, Distance-, and Cordon-Based Congestion Pricing Strategies Via Large-Scale Simulation

This study compares the impacts of delay-, distance-, and cordon-based congestion pricing strategies for Austin, Texas, using the POLARIS agent-based activity-based travel demand simulation model. This approach enables agent-level heterogeneity and realistic choice options (including destination, mode, and activity scheduling) for dynamic traffic assignment and congestion feedbacks across a major metro region, which are features lacking in past work. To ensure comparability, distance-based tolls were set to generate the same revenue as delay-based tolling of $3.5 M/day, averaging $1.17/resident/day or $0.42/vehicle-trip. Delay-based pricing delivers 44% lower network delay and 13% lower VHT compared to the no-toll baseline, levels unmatched by other pricing strategies. At the height of the AM peak, drivers pay up to $0.13/mile on average, though most links in the network remain untolled. Distance-based pricing is the most effective at reducing VMT (by 4%), but VHT reductions (of 6%) primarily stem from drivers selecting closer destinations, achieving only one-fourth the delay reduction of delay-based pricing. Across various implementations of delay- and distance-based pricing, the results suggest that spatial variations of tolls are far more important than temporal variations. Cordon tolls produce minimal impacts at the network-wide level, but offer substantial delay reductions inside the cordon. Other major findings include: 1) delay-based pricing increases trip-making during the PM peak period due to backward shifts in discretionary-activity start times by higher-income residents; and 2) tolls’ spatial impacts, including changes in network flows and tolls paid by residents, vary substantially between delay- and distance-based pricing strategies.

Agent-based modeling↗
DOE OSTI2024

DEPLOYING FAST CHARGING INFRASTRUCTURE FOR ELECTRIC VEHICLES IN URBAN NETWORKS: AN ACTIVITY-BASED APPROACH

This paper explores an important problem under the domain of network modeling, the optimal configuration of charging infrastructure for electric vehicles (EVs) in urban networks considering EV users' daily activities and charging behavior. This study proposes a charging behavior simulation model considering different initial state of charge (SOC), travel distance, availability of home chargers, and the daily schedule of trips for each traveler. The proposed charging behavior simulation model examines the complete chain of trips for EV users as well as the interdependency of trips traveled by each driver. The problem of finding the optimum charging configuration is then formulated as a mixed-integer nonlinear programming problem that considers the dynamics of travel time and travel distance, the interdependency of trips made by each driver, limited range of EVs, remaining battery capacity for recharging, waiting time in queue, and detour to access a charging station. This problem is solved using a metaheuristic approach for a large-scale case network. A series of examples are presented to demonstrate the model efficacy and explore the impact of energy consumption on the final SOC and the optimum charging infrastructure.

Chain of Trips↗
DOE OSTI2023

A Design of Activity-Based Mobility Intervention

Human mobility influences our society and vice versa. During the COVID-19 pandemic, non-pharmaceutical intervention that alters activity-based mobility such as work-from-home greatly impacted human mobility patterns. Many studies on developing mitigation strategies have employed or implemented their own mobility intervention within their model assumption. For fair evaluation between intervention strategies across models, it is significant to set up compatible experimental environments. However, it is difficult to apply the identical intervention to different kinds of models and compare their effectiveness because each model might have different assumptions, capabilities, and implementations. Even if one can apply intervention to heterogeneous models, it may produce undesirable artifacts due to difference of models and integration with intervention. Therefore, minimizing undesirable artifacts and facilitating intervention experiments across heterogeneous models are substantial. Taking this into account, this paper investigates a design of activity-based mobility intervention (ABMI). We define ABMI together with related concepts and develop an extensible data model and schema of ABMI based on the 5W1H method that can be used in different models. As a case study, we apply the ABMI model to a micro-simulation to demonstrate the usability of the proposed model. We expect that standardized ABMI and interfaces may help to streamline development and experiments of intervention strategies across heterogeneous models.

Kim, Joon-Seok↗
DOE OSTI2024

A Strategic Condition Framework for Energy Innovation

Building upon previous work in innovation, technology, and economic fields, we propose a conceptual framework to address the interplay dynamics of public policy and the value chains of clean energy technology innovation systems, by focusing on market structural and strategic conditions in relation to the rate and direction of knowledge diffusion within activity-based value chains. Our framework extends beyond socio-technical transition models by considering such nuances as: 1) the implications of geography, 2) the locus of knowledge types, 3) the dynamics of financial and knowledge flow, and 4) emphasizing the importance of both market structure and institutional conditions. At the firm level, we consider dynamics of interactions between suppliers and customers, innovation activities, the competitive environment, and the firm’s broader strategic relationship to governance activities and structures; we allow for the fact that each of these aspects of a firm’s identity may have a strategically relevant geographic dimension (e.g., service territories, locational concentration of input resources, etc.). We provide a proof-of-concept application of the U.S. utility-scale solar photovoltaic (PV) and onshore wind sectors, including their interactions with the U.S. power market, which we in present in a web-based information platform (Energy I-SPARK, ei-spark.lbl.gov).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data

Growing vaccine hesitancy is contributing to the decline in immunization rates for highly contagious, vaccine-preventable childhood diseases. Therefore, there has been a significant interest in understanding how hesitancy is spreading at higher spatio-temporal resolutions, enabling more targeted interventions. Motivated by this, we study the problem of prediction of vaccine hesitancy at the ZIP Code level, referred to as the VaxHesitancy problem. A significant challenge for this problem is the lack of high-resolution data that indicates hesitancy. Here, we develop a hybrid VaxHesSTL framework that combines a Graph Neural Network (GNN) and a Recurrent Neural Network (RNN) to address the VaxHesitancy problem. The GNN uses a ZIP Code-level network to capture spatial signals from neighboring areas, while the RNN models the temporal dynamics present in the data. We train and evaluate VaxHesSTL using a large dataset, namely the All-Payer Claims Databases (APCD), for Virginia, consisting of insurance claims from over five million individuals for six years. We find that an aggregated contact network or graph, developed from a detailed activity-based population network, plays an important role in the performance of VaxHesSTL, compared to graph models based solely on spatial proximity. Experiments demonstrate that VaxHesSTL outperforms a range of state-of-the-art baselines, which rely solely on historical time series data without accounting for spatial relationships. Since hesitancy data at higher spatial resolution is often unavailable or hard to get, we incorporate an active learning approach with our VaxHesSTL framework to optimize the training set without compromising the prediction performance. We find that hesitancy data for only 18% of ZIP Codes selected by active learning allows us to forecast hesitancy for all the ZIP Codes in the Virginia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Impact of moisture on microbial decomposition phenotypes and enzyme dynamics

Soil organic matter decomposition is a complex process reflecting microbial composition and environmental conditions. Moisture can modulate the connectivity and interactions of microbes. Due to heterogeneity, a deeper understanding of the influence of soil moisture on the dynamics of organic matter decomposition and resultant phenotypes remains a challenge. Soils from a long-term field experiment exposed to high and low moisture treatments were incubated in the laboratory to investigate organic matter decomposition using chitin as a model substrate. By combining enzymatic assays, biomass measurements, and microbial enrichment via activity-based probes, we determined the microbial functional response to chitin amendments and field moisture treatments at both the community and cell scales. Chitinolytic activities showed significant responses to the amendment of chitin, independent of differences in field moisture treatments. However, for other measurements of carbon metabolism and cellular functions, soils from high moisture field treatments had greater potential enzyme activity than soils from low moisture field treatments. A cell tagging approach was used to enrich and quantify bacterial taxa that are actively producing chitin-degrading enzymes. By integrating organism, community, and soil core measurements we show that (i) a small subset of taxa compose the majority (>50%) of chitinase production despite broad functional redundancy, (ii) the identity of key chitin degraders varies with moisture level, and (iii) extracellular enzymes that are not cell-associated account for most potential chitinase activity measured in field soil.

activity-based probes↗
DOE OSTI2026

Human Factors and Technologies Design to Improve User Acceptance of Pooled Rideshare for Increasing Transportation System Energy Efficiency

This multi-year project delivered a comprehensive, human-factors-driven framework to understand, model, and improve pooled rideshare (PR) adoption in the United States. Through three large-scale national survey studies involving more than 16,000 participants across multiple cities and demographic groups, the research established one of the most extensive datasets to date on user perceptions, behavioral barriers, and service expectations related to pooled rideshare. These data revealed key human factors barriers of user acceptance of PR and suggested potential actionable experience optimizations that could lead to increased PR usage. This foundational knowledge guided the development of novel human-factors models and behavioral choice models that quantify how psychological, demographic, and trip-level factors influence willingness to pool. Building on these empirical insights, the project developed advanced behavioral modeling tools, including mixed logit and integrated choice and latent variable models, to capture both observable and latent influences on PR adoption. These models significantly improved the ability to predict riders’ acceptance of pooled trips, explaining choice heterogeneity through latent constructs such as safety, service experience, privacy concerns, time sensitivity, and environmental attitudes. Together, these models provide a robust analytical foundation for designing PR systems that more effectively meet user needs. The project translated human-factors insights and behavioral models into actionable technology innovations by extending POLARIS—an agent-based, activity-based travel simulation platform—into a fully functional pooled rideshare simulation environment. New PR modules, acceptance models, and regional scenarios were implemented for Greenville, SC and Austin, TX, enabling high-fidelity validation of algorithmic strategies under realistic demand and traffic conditions. The simulation platform supported the development and evaluation of adaptive discount-based assignment algorithms, enhanced willingness-to-pay formulations, demographic-aware incentive mechanisms, and a proactive joint assignment and repositioning strategy. Simulation results demonstrated substantial gains in pooling uptake, average vehicle occupancy, energy efficiency, and fleet profitability. In Greenville, pooling adoption more than doubled, while reductions in vehicle-miles traveled and energy consumption were significant. In Austin, pooling improvements were achieved with minimal service-quality trade-offs, and profitability increased across all fleet sizes. Through this research, we developed a comprehensive understanding of the human factors barriers that limit user acceptance of pooled rideshare services. These insights enabled the design of human-factors-aware pooled rideshare technologies that more effectively address user concerns and improve adoption rates. By integrating these models into an advanced agent-based simulation framework, we demonstrated that higher adoption of pooled rideshare can lead to measurable improvements in energy efficiency and system performance. Together, these contributions establish a validated pathway from human-centered analysis to technology development and energy-saving outcomes, supporting national goals for more sustainable and efficient mobility systems.

Jia, Yunyi↗
DOE OSTI2026

1991 Boston Household Travel Survey

The Central Transportation Planning Staff conducted a household travel survey in 1991, featuring demographic, socioeconomic, and travel features of 2,347 households, generating 39,372 weekday trips. The survey took the form of an activity-based travel diary that each subject household filled out for one weekday. The data was used to estimate new models for trip generation, automobile ownership, trip distribution, and mode choice.

1Hz data↗
DOE OSTI2023

Geochemical Modeling Using OLI Systems Mixed Solvent Electrolyte Model in Support of the Waste Isolation Pilot Plant

Aqueous electrolyte thermodynamic models were developed for synthetic Salado (GWB) and Castile (ERDA-6) brines to inform the experimental design and to facilitate laboratory data interpretation in geochemical studies supporting disposal in the Waste Isolation Pilot Plant (WIPP). The thermodynamic models, which include the relevant vapor-liquid-solid equilibria for the oxidation-reduction conditions of interest, enable computation of system parameters such as activity-based pH, E h [vs. standard hydrogen electrode (SHE)], ionic strength, total dissolved solids, and solution density; the chemical speciation of all phases; saturation tendencies for thermodynamically feasible solids; and changes in brine chemistry because of the addition of H 2 (g), argon, MgO, Fe(0), and other waste form components. The impacts of oxidation-reduction potential (ORP) and pH on aqueous speciation and dominant solid phases were also assessed with the aid of real-solution Pourbaix diagrams for key elements of interest. In addition to the OLI modeling results, limited measurement data for pH and ORP standards are provided, analyzed, and discussed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Activity-Based Protein Profiling to Probe Relationships between Cytochrome P450 Enzymes and Early-Age Metabolism of Two Polycyclic Aromatic Hydrocarbons (PAHs): Phenanthrene and Retene

A growing body of literature has linked early life exposures to polycyclic aromatic hydrocarbons (PAH) with adverse neurodevelopmental effects. Once in the body, metabolism serves as a powerful mediator of PAH toxicity by bioactivating and detoxifying PAH metabolites. Since enzyme expression and activity varies considerably throughout human development, we evaluated infant metabolism of PAHs as a potential contributing factor to PAH susceptibility. Here, we measured and compared rates of phenanthrene and retene (two primary PAH constituents of woodsmoke) metabolism in human hepatic microsomes from individuals ≤21 months of age to a pooled sample (n = 200) consisting primarily of adults. We used activity-based protein profiling (ABPP) to characterize cytochrome P450 enzymes (CYPs) in the same hepatic microsome samples. Once incubated in microsomes, phenanthrene demonstrated rapid depletion. Best fit models for phenanthrene metabolism demonstrated either 1 or 2 phases, depending on the sample, indicating that multiple enzymes could metabolize phenanthrene. We observed no statistically significant differences in phenanthrene metabolism as a function of age, although samples from the youngest individuals had the slowest phenanthrene metabolism rates. We observed slower rates of retene metabolism compared to phenanthrene also in multiple phases. Rates of retene metabolism increased in an age-dependent manner until adult (pooled) levels were achieved at ~12 mo. ABPP identified 28 unique CYPs among all samples, and we observed lower amounts of active CYPs in individuals ≤21 months of age compared to the pooled sample. Phenanthrene metabolism correlated to CYPs 1A1, 1A2, 2C8, 4A22, 3A4, and 3A43 and activities and retene metabolism correlated to CYPs 1A1, 1A2, and 2C8 activities measured by ABPP and vendor-supplied substrate marker activities. These results will aid efforts to determine human health risk and susceptibility to PAHs exposures during early life.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

The use of activity-based cost estimation as a management tool for cultural change

It will be shown that the greatest barrier to American exploration of the planet Mars is not the development of the technology needed to deliver humans and return them safely to earth. Neither is it the cost of such an undertaking, as has been previously suggested, although certainly, such a venture may not be inexpensive by some measures. The predicted costs of exploration have discouraged serious political dialog on the subject. And, in fact, even optimistic projections of the NASA budget do not contain the resources required, under the existing development and management paradigm, for human space exploration programs. It will be demonstrated that the perception of the costs of such a venture, and the cultural responses to the perceptions are factors inhibiting American exploration of the moon and the planet Mars. Cost models employed in the aerospace industry today correctly mirror the history of past space programs, and as such, are representative of the existing management and development paradigms. However, if, under this current paradigm no major exploration programs are feasible, then cost analysis methods based in the past may not have great utility in exploring the needed cultural changes. This paper explores the use of a new type of model, the activity based cost model, which will treat management style as an input variable, in a sense providing a tool whereby a complete, affordable program might be designed, including both the technological and management aspects.

Mandell, Humboldt↗
DOE OSTI2022

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA). Final report

The goal of the Stahl lab is to use a combination of isotopic, genomic, and activity-based analyses in the field, of selected field isolates, and defined synthetic communities to identify sources and sinks of different nitrogen species in the field, focusing on the FRC at the ORNL. The most general research objective is to characterize variants in the pathway for denitrification among field isolates and synthetic microbial assemblies as a foundation for predicting the biotic and abiotic controls of nitrogen and carbon transformations in the field. Specifically, we have focused on characterizing environmental controls of nitrous oxide emissions, an important greenhouse gas and ozone depleting molecule. Studies have mapped the flux of nitrous oxide and controlling genetic elements within the FRC subsurface, indicating the importance of different enzyme variants in nitrous oxide production and consumption in controlling surface emissions. We have developed methods to quantify and catalog the isotopic signatures imparted on nitrous oxide by different nitrous oxide reductase variants in isolated organisms and model synthetic communities. We have developed methods to quantify and catalog the nitrous oxide affinity of field relevant isolates and their biomass yield when respiring nitrous oxide on different carbon substrates. We continue to expand the collection of field-relevant microbial isolates within ENIGMA by focused enrichment and isolation from field material. These objectives have been facilitated by the development of new technologies, including: 1) the development of a multiplexed automated optical density measurement system for quantifying growth kinetics of microorganisms dependent on gaseous substrates in monoculture or gas exchange in synthetic communities and 2) the development of a microcalorimetric analysis format to quantify the impact of environmental stress on microbial maintenance energy. Since field relevant processes are mediated by assemblies of organisms interacting with each other and their local environment, we have also collaborated with other ENIGMA investigators on the assembly and characterization of synthetic communities. These studies have evaluated the role of organisms lacking a complete pathway for denitrification in the control of field processes, measuring process rates of assemblies of organisms that individually lack the full denitrification pathway but mediate the complete denitrification when growing together. These efforts have so far resulted in the publication of 18 peer reviewed papers ranging from ecological and physiological drivers of pathway optimization to field-scale process characterization.

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

An Integral Activity-Based Protein Profiling Method for Higher Throughput Determination of Protein Target Sensitivity to Small Molecules

Activity-based protein profiling (ABPP) is a chemoproteomic technique that uses small molecule probes to label active enzymes selectively and covalently in complex proteomes. Competitive ABPP, which involves treatment of the active proteome with an analyte of interest, is especially powerful for profiling how small molecules impact specific protein activities. Advances in higher throughput workflows have made it possible to generate extensive competitive ABPP data across diverse biological samples, making this approach highly appealing for characterizing shared and unique proteins affected by perturbations such as drug or chemical exposures. To use the competitive ABPP approach effectively to understand potential adverse effects of chemicals of concern (CoC), a wide range of concentrations may be needed, particularly for chemicals that lack potency or toxicity data. In this work, we present an integral competitive ABPP method that enables target sensitivity determination for different organophosphate (OP) pesticides as model toxicants. Using previously developed OP-ABPs, we optimized conditions for tandem mass tag (TMT) multiplexing of ABPP samples and compared conventional competitive ABPP involving samples at discrete paraoxon concentrations to pooled samples across that same concentration range. We then expanded our approach to compare protein target sensitivities toward two additional OP pesticides, chlorpyrifos oxon and malaoxon. The results showed that differences in integral intensities for the pooled competition sample can be used to evaluate the relative sensitivity of specific proteins without increasing the overall number of samples. For 8 CoC concentrations of interest, this strategy reduced the number of TMT plexes and the corresponding number of LC–MS/MS analyses 3-fold. In conclusion, we envision the integral ABPP (IABPP) method will provide a means to screen diverse chemicals more rapidly to identify both high and low sensitivity protein targets.

activity-based probes↗
DOE OSTI2026

Chemoproteomic Elucidation of β-Lactam Drug Targets in Mycobacterium abscessus

The pathogen Mycobacterium abscessus (Mab) can cause severe and difficult-to-treat chronic lung infections. Despite the rising incidence and clinical concern of Mab infections, treatment options are limited and often ineffective. Treatment is complicated by Mab’s ability to persist in a nonreplicating, drug-resistant state. Several β-lactam antibiotics are potently bactericidal against Mab but are underutilized because their molecular mechanisms of action against Mab are incompletely understood. In the current study, we used β-lactam-derived activity-based probes and chemoproteomics to report the first comprehensive list of Mab enzymes targeted by β-lactams. We compared β-lactam targets across two Mab subspecies in actively replicating and nonreplicating cultures, using a new carbon starvation model of persistence. We identified 17 targets that were active in every condition tested, seven of which were previously unknown to bind β-lactams. Lastly, we characterized the β-lactamase activity and β-lactam inhibition profiles of nine Mab enzymes, demonstrating that imipenem inhibits these targets more effectively than cefoxitin. These findings demonstrate β-lactam target engagement in persistent Mab and provide clarity on the mechanisms of action of clinically relevant β-lactams in Mab, crucial steps toward fully realizing their potential for treating infections caused by this opportunistic pathogen.

Mycobacterium abscessus↗