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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Quantifying Resolution Implications for Agent-based Distributed Energy Resource Customer Adoption Models

In this report we explore tradeoffs of top-down and bottom-up methods in their precision and computational burden using NREL's dGen model, an agent-based model of residential and non-residential distributed PV adoption. In particular, we assess the role of agent resolution in instantiating statistically-representative populations in the model--and the resulting variance of model projections at the state, sector and county levels. Running the model for a single state with fixed macroeconomic assumptions, we vary both the number of agents instantiated and the number of unique simulations. Notably, in each run we introduce sources of uncertainty in cumulative DPV adoption results during agent instantiation as we stochastically sample agent attributes from localized building characteristic and solar resource distributions. We find three key trends which guide what can be expected of results. First, precision of the model projections, as measured by the variance of the unique simulations increased as a function of the number of agents sampled, though with diminishing returns to scale. The result is expected given the Law of Large Numbers. Second, the mean or expected value of projections decreased as the number of agents instantiated increased. Empirically, aggregate DPV capacities projected by dGen are 7% lower on average at a 10-agent resolution compared to 1-agent resolutions, and up to 10% lower in the non-residential sector. We attribute this outcome to a system sizing scheme that biases lower cumulative installed capacity as agent resolution increases when roof size and load are not positively correlated. Finally, the variance of model projections differed by sector, where variance was substantially larger in the commercial sector, largely due to differences in parameterization by sector. We explain the larger variance in the non-residential sector as an outcome of the greater variance in building uses and load shapes as compared to the residential sector, and we find that a resolution on the order of 100-agents per county would be necessary to bring non-residential variance in line with residential variance at a 2-agent resolution. We propose that further research explore increasing precision and agent resolution in the non-residential sector. Moreover, given that days of intensive computing may be required to resolve a suitable estimate even at the extent of a single state within acceptable levels of precision, to promote computational efficiency we also suggest more clearly quantifying sources of variability at the county level such that resolution can by dynamically increased where county level results are least precise.

14 SOLAR ENERGY↗
DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multi-Tiered Estimation for Correlation Spectroscopy in 3D (MTECS3D) v0.1

This innovative software estimates rotational diffusion coefficients of particles from X-ray photon correlation spectroscopy (XPCS) data of monodispersed particle systems. It is the first method capable of extracting rotational diffusion information from three-dimensional particle systems using XPCS. Using the angular-temporal cross-correlation of the XPCS images, this software is able to estimate the rotational diffusion coefficients with only a few percent relative errors while requiring minimal prior knowledge of particle structures. This software enhances XPCS analysis capabilities, allowing researchers to study translational and rotational Brownian dynamics of particles in suspension across various temporal and spatial scales.

Hu, Zixi↗
DOE OSTI2022

Results of Oklo Diffusion Couple Experiments

Through the Gateway for Accelerated Innovation in Nuclear (GAIN) initiative, the U.S. Department of Energy funded a GAIN Nuclear Energy Voucher to perform out-of-pile experiments related to the fuel design for Oklo Inc., headquartered in Santa Clara, CA. GAIN’s goal is to accelerate the innovation and application of advanced nuclear technologies, and voucher recipients do not receive direct financial awards but are provided access to national laboratory capabilities at no cost. This report documents the results of diffusion couple experiments performed as part of the GAIN-funded project for Oklo. This work addresses the specific problem of filling the gap in understanding of the fuel-cladding chemical interaction (FCCI) that result from the past focus on a particular fuel design. These knowledge and mechanistic modeling gaps make it difficult to extrapolate from prior fuel performance data to advance an economically competitive fuel design. Specifically, the current state-of-the-art fuel performance modeling cannot separate the entangled effects of temperature ranges, burnups, materials, and geometries.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Discovery of true electrochemical reactions for ultrahigh catalyst mass activity in water splitting

Better understanding of true electrochemical reaction behaviors in electrochemical energy devices has long been desired. It has been assumed so far that the reactions occur across the entire catalyst layer (CL), which is designed and fabricated uniformly with catalysts, conductors of protons and electrons, and pathways for reactants and products. By introducing a state-of-the-art characterization system, a thin, highly tunable liquid/gas diffusion layer (LGDL), and an innovative design of electrochemical proton exchange membrane electrolyzer cells (PEMECs), the electrochemical reactions on both microspatial and microtemporal scales are revealed for the first time. Surprisingly, reactions occur only on the CL adjacent to good electrical conductors. On the basis of these findings, new CL fabrications on the novel LGDLs exhibit more than 50 times higher mass activity than conventional catalyst-coated membranes in PEMECs. In conclusion, this discovery presents an opportunity to enhance the multiphase interfacial effects, maximizing the use of the catalysts and significantly reducing the cost of these devices.

08 HYDROGEN↗
DOE OSTI2024

Terrestrial photosynthesis inferred from plant carbonyl sulfide uptake

Terrestrial photosynthesis, or gross primary production (GPP), is the largest carbon flux in the biosphere, but its global magnitude and spatiotemporal dynamics remain uncertain. The global annual mean GPP is historically thought to be around 120 PgC yr −1 , which is about 30–50 PgC yr −1 lower than GPP inferred from the oxygen-18 ( 18 O) isotope and soil respiration. This disparity is a source of uncertainty in predicting climate–carbon cycle feedbacks. Here, in this study, we infer GPP from carbonyl sulfide, an innovative tracer for CO 2 diffusion from ambient air to leaf chloroplasts through stomata and mesophyll layers. We demonstrate that explicitly representing mesophyll diffusion is important for accurately quantifying the spatiotemporal dynamics of carbonyl sulfide uptake by plants. From the estimate of carbonyl sulfide uptake by plants, we infer a global contemporary GPP of 157 (±8.5) PgC yr −1 , which is consistent with estimates from 18 O (150–175 PgC yr −1 ) and soil respiration ($149^{+29}_{-23}$) PgC yr −1 ), but with an improved confidence level. Our global GPP is higher than satellite optical observation-driven estimates (120–140 PgC yr –1 ) that are used for Earth system model benchmarking. This difference predominantly occurs in the pan-tropical rainforests and is corroborated by ground measurements, suggesting a more productive tropics than satellite-based GPP products indicated. As GPP is a primary determinant of terrestrial carbon sinks and may shape climate trajectories, our findings lay a physiological foundation on which the understanding and prediction of carbon–climate feedbacks can be advanced.

Lai, Jiameng↗
DOE OSTI2016

A multi-probe thermophoretic soot sampling system for high-pressure diffusion flames

Optical diagnostics and physical probing of the soot processes in high pressure combustion pose challenges that are not faced in atmospheric flames. One of the preferred methods of studying soot in atmospheric flames is in situ thermophoretic sampling followed by transmission electron microscopy imaging and analysis for soot sizing and morphology. The application of this method of sampling to high pressures has been held back by various operational and mechanical problems. In this work, we describe a rotating disk multi-probe thermophoretic soot sampling system, driven by a microstepping stepper motor, fitted into a high-pressure chamber capable of producing sooting laminar diffusion flames up to 100 atm. Innovative aspects of the sampling system design include an easy and precise control of the sampling time down to 2.6 ms, avoidance of the drawbacks of the pneumatic drivers used in conventional thermophoretic sampling systems, and the capability to collect ten consecutive samples in a single experimental run. Proof of principle experiments were performed using this system in a laminar diffusion flame of methane, and primary soot diameter distributions at various pressures up to 10 atm were determined. High-speed images of the flame during thermophoretic sampling were recorded to assess the influence of probe intrusion on the flow field of the flame.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Multi-Objective design of interlocking metasurfaces using conditional diffusion models

Unit cell design remains a major challenge for interlocking metasurfaces, a promising joining technology for dissimilar materials, due to the complex, competing, multivariate design space and the need for rapid adaptation to varying performance requirements. This study explores Conditional Diffusion Models as a design optimization tool for interlocking metasurfaces. Given the complex, competing, multivariate design space for interlocking metasurfaces, unit cell design remains a major challenge for this joining technology. We trained a conditional diffusion model on 25,000 finite element analysis-simulated interlocking metasurface unit cells to generate designs with tailored thermo-mechanical properties (tensile strength, shear strength, and thermal conductivity) based on specified performance criteria. The model demonstrated a success rate of approximately 72 % in producing designs that met specified property bounds. The conditional diffusion model generated both thermally resistive and conductive designs, revealing clear trends in design characteristics: taller, dendritic structures were advantageous for tensile loads, while shorter, robust designs excelled in shear applications. Our findings indicate that the model's performance is more influenced by the breadth of the design space than by the quantity of training data, highlighting the importance of expansive design domains for generating innovative solutions. This work establishes conditional diffusion models as a highly efficient and adaptable tool for rapid interlocking metasurface unit cell design, paving the way for advancements in multi-material joining technologies, as well as highlighting the justification to leverage conditional diffusion models as design tools across complex design domains.

Conditional diffusion models↗
DOE OSTI2025

Nonlinear programming optimization of a single-stack electrodialysis desalination system for cost efficiency

Electrodialysis (ED) presents a competitive method for desalinating brackish waters. In this work, we perform cost optimization of a single-stack ED system across a range of feed salinities and water recoveries while optimizing operating voltage, number of cell pairs, and cell length. The results of our optimization show that the levelized cost of water (LCOW) increases with an increase in feed salinity. The outcomes of our optimization show that cost-optimal design generally increases cell length while decreasing cell pair number and operating voltage with an increase in salinity. These trends are nonlinear, with the number of cell pairs and applied voltage exhibiting local maxima when operating at low salinity and high recovery. We discuss the underlying mechanism for cell length becoming a leveraging design parameter by inspecting the length-dependent profiles of key electrochemical properties of the ED cell. Finally, we present how increasing performance metrics and decreasing costs impact LCOW, demonstrating that innovations that decrease counter-current diffusion have resulted in the highest decrease of LCOW.

42 ENGINEERING↗
DOE OSTI2020

Mercury Speciation via Diffusive Gradients Thin-Films Technology

Objectives: Develop and test various diffusive gradient in thin films (DGT) samplers for mercury - SRNL is developing reactive DGTS (rDGTs). Test chemistry to differentiate methylmercury from total and/or inorganic mercury in environmental samples. Fabricate and test rDGT samplers for deployment. Deploy the rDGTs in variable settings. Mercury is a persistent-bioaccumulative-toxic environmental pollutant. Mercury exists in different species such as elemental, inorganic, and methyl. Mercury speciation determine mercury behavior and toxicity. For example, methylmercury strongly accumulates in biota such as fish resulting in potential human health impacts. DGTs are innovative samplers to measure water concentration by diffusion and capture. Types of Hg DGTs used: Total Hg, Inorganic Hg, and Methyl Hg. Deployment Sites: Upper Three Runs, Lower Three Runs, Steele Creek, East Fork Poplar Creek (SRNL Sites), Horizons Creek, NOA Creek, Bruners Creek (ORNL Sites). DGT concentration estimates closely matched alternative measurements for soluble mercury at each Oak Ridge site - DGTs did not respond to particulate mercury. DGTs provide a representative measure for biota uptake since they are left out longer which allows for an average exposure concentration. DMA-80 provided an efficient and quick analysis. Variability in site location made a difference in Hg species. DGT preparations and sampling wasn't as quick and simple as microcolumn technology. Copper reagent degraded agarose gel - more work and cleaner data are needed to assess speciation in rDGTs. Future Work: Use cross-linked polyacrylamide for the collection layer for a stronger plastic. Make a copper diffusion layer without agarose. Test copper separation in lab using realistic stream conditions such as high organic carbon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Hydrogen-bond Specific Materials Modification in Group IV Semiconductors

Semiconductor dielectric crystals consist of two fundamental components: lattice atoms and electrons. The former component provides a crystalline structure that can be disrupted by various defects or the presence of an interface, or by transient oscillations known as phonons. The latter component produces an energetic structure that is responsible for the optical and electronic properties of the material, and can be perturbed by lattice defects or by photo-excitation. Over the period of this project, August 15, 1999 to March 31, 2015, a persistent theme has been the elucidation of the fundamental role of defects arising from the presence of radiation damage, impurities (in particular, hydrogen), localized strain or some combination of all three. As our research effort developed and evolved, we have experienced a few title changes, which reflected this evolution. Throughout the project, ultrafast lasers usually in a pump-probe configuration provided the ideal means to perturb and study semiconductor crystals by both forms of excitation, vibrational (phonon) and electronic (photon). Moreover, we have found in the course of this research that there are many interesting and relevant scientific questions that may be explored when phonon and photon excitations are controlled separately. Our early goals were to explore the dynamics of bond-selective vibrational excitation of hydrogen from point defects and impurities in crystalline and amorphous solids, initiating an investigation into the behavior of hydrogen isotopes utilizing a variety of ultrafast characterization techniques, principally transient bleaching spectroscopy to experimentally obtain vibrational lifetimes. The initiative could be divided into three related areas: (a) investigation of the change in electronic structure of solids due to the presence of hydrogen defect centers, (b) dynamical studies of hydrogen in materials and (c) characterization and stability of metastable hydrogen impurity states under transient compression. This research focused on the characterization of photon and ion stimulated hydrogen related defect and impurity reactions and migration in solid state matter, which requires a detailed understanding of the rates and pathways of vibrational energy flow, of the transfer channels and of the coupling mechanisms between local vibrational modes (LVMs) and phonon bath as well as the electronic system of the host material. It should be stressed that researchers at Vanderbilt and William and Mary represented a unique group with a research focus and capabilities for low temperature creation and investigation of such material systems. Later in the program, we carried out a vigorous research effort addressing the roles of defects, interfaces, and dopants on the optical and electronic characteristics of semiconductor crystals, using phonon generation by means of ultrafast coherent acoustic phonon (CAP) spectroscopy, nonlinear characterization using second harmonic generation (SHG), and ultrafast pump-and-probe reflectivity and absorption measurements. This program featured research efforts from hydrogen defects in silicon alone to other forms of defects such as interfaces and dopant layers, as well as other important semiconducting systems. Even so, the emphasis remains on phenomena and processes far from equilibrium, such as hot electron effects and travelling localized phonon waves. This program relates directly to the mission of the Department of Energy. Knowledge of the rates and pathways of vibrational energy flow in condensed matter is critical for understanding dynamical processes in solids including electronically, optically and thermally stimulated defect and impurity reactions and migration. The ability to directly probe these pathways and rates allows tests of theory and scaling laws at new levels of precision. Hydrogen embedded in model crystalline semiconductors and metal oxides is of particular interest, since the associated local mode can be excited cleanly, and is usually well-separated in energy from the phonon bath. These basic dynamical studies have provided new insights for example into the fundamental mechanisms that control proton diffusion in these oxides. This area of materials science has largely fulfilled its promise to identify degradation mechanisms in electronic and optoelectronic devices, and to advance solid oxide proton conductors for fuel cells, gas sensors and proton-exchange membrane applications. It also provides the basis for innovations in materials synthesis involving atomic-selective diffusion and desorption.

36 MATERIALS SCIENCE↗
DOE OSTI2017

The AmeriFlux network: A coalition of the willing

AmeriFlux scientists were early adopters of a network-enabled approach to ecosystem science that continues to transform the study of land-atmosphere interactions. In the 20 years since its formation, AmeriFlux has grown to include more than 260 flux tower sites in the Americas that support continuous observation of ecosystem carbon, water, and energy fluxes. Many of these sites are co-located within a similar climate regime, and more than 50 have data records that exceed 10 years in length. In this prospective assessment of AmeriFlux's strengths in a new era of network-enabled ecosystem science, we discuss how the longevity and spatial distribution of AmeriFlux data make them exceptionally well suited for disentangling ecosystem response to slowly evolving changes in climate and land-cover, and to rare events like droughts and biological disturbances. More recently, flux towers have also been integrated into environmental observation networks that have broader scientific goals; in North America these include the National Ecological Observatory Network (NEON), Critical Zone Observatory network (CZO), and Long-Term Ecological Research network (LTER). AmeriFlux stands apart from these other networks in its reliance on voluntary participation of individual sites, which receive funding from diverse sources to pursue a wide, transdisciplinary array of research topics. This diffuse, grassroots approach fosters methodological and theoretical innovation, but also challenges network-level data synthesis and data sharing to the network. In conclusion, while AmeriFlux has had strong ties to other regional flux networks and FLUXNET, better integration with networks like NEON, CZO and LTER provides opportunities for new types of cooperation and synergies that could strengthen the scientific output of all these networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The spread of technological innovations: effects of psychology, culture and policy interventions

Technological innovations drive the evolution of human societies. The success of innovations depends not only on their actual benefits but also on how potential adopters perceive them and how their beliefs are affected by their social and cultural environment. To deepen our understanding of socio-psychological processes affecting the new technology spread, we model the joint dynamics of three interlinked processes: individual learning and mastering the new technology, changes in individual attitudes towards it, and changes in individual adoption decisions. We assume that the new technology can potentially lead to a higher benefit but achieving it requires learning. We posit that individual decision-making process as well as their attitudes are affected by cognitive dissonance and conformity with peers and an external authority. Individuals vary in different psychological characteristics and in their attitudes. We investigate both transient dynamics and long-term equilibria observed in our model. We show that early adopters are usually individuals who are characterized by low cognitive dissonance and low conformity with peers but are sensitive to the effort of an external authority promoting the innovation. We examine the effectiveness of five different intervention strategies aiming to promote the diffusion of a new technology: training individuals, providing subsidies for early adopters, increasing the visibility of peer actions, simplifying the exchange of opinions between people, and increasing the effort of an external authority. We also discuss the effects of culture on the spread of innovations. Finally, we demonstrate that neglecting the cognitive forces and the dynamic nature of individual attitudes can lead to wrong conclusions about adoption of innovations. Our results can be useful in developing more efficient policies aiming to promote the spread of new technologies in different societies, cultures and countries.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Introducing a risk aggregation rationale for mapping risks to aquifers from point- and diffuse-sources–proof-of-concept using contamination data from industrial lagoons

Highlights: • Aquifer risk mapping is introduced for contaminants acting as point/diffuse-sources. • Innovations are framed by risk cells based on Source-Pathway-Receptor-Source (SPRC). • Point-source (Risk Cell 1) and diffuse-source (Risk Cell 2) use sets of algorithms. • Algorithms include MODPATH, DRASTIC, GWPI and are integrated on a modular basis. • Risk Cells 1/2 are aggregated giving fit-for-purpose results as proof-of-concept. - Abstract: Proof-of-concept for a methodology is presented on mapping risks to aquifers impacted from point- and diffuse-sources, where mapping or indexing refers to relative but not absolute values. The methodology is generic but tested by investigating impacts of a risk exposure from industrial wastewater lagoons. The methodology is innovative for using the qualitative Source-Pathways-Receptors-Consequences (SPRC) framework to aggregate risks from both point-sources and diffuse-sources through breaking down a study area into SPRC risk cells. In this paper, two risk cells are required as: (i) Cell 1 is directly impacted from a point-source; and (ii) Cell 2 is impacted by diffuse-sources to slowly contaminate the aquifer by infiltration over a large area. Indexing both types of risk cells generically comprise three tiered processes: (i) binary indexing establishes if a grid cell is at a potential risk or not; (ii) graded indexing measures the strength of the risk pathways from source to receptors; and (iii) local indexing measures intrinsic potentials at the grid cell to propagate the risk. These three processes apply to both point- and diffuse-sources but with different mathematical formulations. The proof-of-concept for the methodology of risk aggregation using the SPRC framework is supported by results of a study area, in which a set of performance metrics are used by comparing with the measurements. The results are found to be fit-for-purpose for serving as proactive management tools and to provide a deeper insight into potential impacts of adverse effects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Improved Instrumentation for Solar Resource Measurements and Forecasting: Cooperative Research and Development Final Report, CRADA Number CRD-09-349

Under this Agreement, NREL will work with Participant to improve the instrumentation and measurement systems available for measuring and monitoring solar radiation elements needed by the electric utilities and solar power system integrators to adequately characterize the spatial and temporal variations of the renewable energy resources. This work includes, but is not limited to, research and development for making cost effective radiometers, solar trackers, and related instrumentation for solar resource measurements and forecasting. Cooperative R&D is proposed in three areas: Improved radiometric systems for cost effective irradiance measurements (direct normal, global, diffuse and plane-of-array); evaluation of innovative disruptive technology approaches for deriving the constituent solar energy components utilizing new fast-response thermopile measurement technologies, coupled with innovative shading geometry and tracker motion control; and low-cost solutions for sky imaging needed for resource forecasting. This work will be conducted at NREL and Participant facilities.

14 SOLAR ENERGY↗
DOE OSTI2023

Adaptations for gas exchange enabled the elongation of lepidopteran proboscises

The extensive biodiversification of butterflies and moths (Lepidoptera) is partly attributed to their unique mouthparts (proboscis [Pr]) that can span in length from less than 1 mm to over 280mm in Darwin's sphinx moths. Lepi-doptera, similar to other insects, are believed to inhale and exhale respiratory gases only through valve-like spi-racles on their thorax and abdomen, making gas exchange through the narrow tracheae (Tr) challenging for the elongated Pr. How Lepidoptera overcome distance effects for gas transport to the Pr is an open question that is important to understanding how the Pr elongated over evolutionary time. Here, we show with scanning electron microscopy and X-ray imaging that distance effects on gas exchange are overcome by previously unreported micropores on the Pr surface and by superhydrophobic Tr that prevent water loss and entry. We find that the density of micropores decreases monotonically along the Pr length with the maxima proportional to the Pr length and that micropore diameters produce a Knudsen number at the boundary between the slip and transition flow regimes. By numerical estimation, we further show that the respiratory gas exchange for the Pr predominantly occurs via diffusion through the micropores. In conclusion, these adaptations are key innovations vital to Pr elongation, which likely facilitated lepidopteran biodiversification and the radiation of angiosperms by coevolutionary processes.

59 BASIC BIOLOGICAL SCIENCES↗