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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 OSTI2020

Development of Acoustic Backscatter for In Situ Monitoring of Nuclear Sludge Wastes During Transfer - 20117

This paper presents an overview into the use of acoustic backscatter systems (ABS) for the in situ characterization of nuclear waste sludge concentration, size and aggregation, highlighting key achievements from the University of Leeds. Previous projects funded through the Engineering and Physical Sciences Research Council (EPSRC) UK and Innovate UK are firstly reviewed and linked to current research within the new TRANSCEND consortium. Detailed are a number of developments at various stages of technology readiness level, from the use of ABS to monitor small-scale sludge settling in columns, to large-scale jet-mixing and waste transfer operations. Current work with Sellafield Ltd will see new technology and world leading analysis methods deployed to measure the consolidation of complex wastes currently undergoing containment, the first time full industrial utilization of these systems has been achieved. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

When can we detect lianas from space? Toward a mechanistic understanding of liana‐infested forest optics

Abstract Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing could offer a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global geographic specificity of spectral signals—reflectance across wavelengths—from liana‐infested tree crowns and forest stands, examining the underlying mechanisms of these signals. We compiled a uniquely comprehensive database, including leaf reflectance spectra from 5424 leaves, fine‐scale airborne reflectance data from 999 liana‐infested canopies, and coarse‐scale satellite reflectance data covering 775 ha of liana‐infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, establishing a synthesis of the relative importance of different mechanisms, which we corroborate with field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signal at canopy and stand scales across globally distributed sites. This signature mainly arises at the canopy level due to direct effects of more horizontal leaf angles, resulting in a larger projected leaf area, and indirect effects from increased light scattering in the near and short‐wave infrared regions, linked to lianas' less costly leaf construction compared with trees on average. The existence of a consistent global spectral signal for lianas suggests that large‐scale quantification of liana infestation is feasible. However, because the traits responsible for the liana canopy‐reflectance signal are not exclusive to lianas, accurate large‐scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross‐site patterns also prompt ecological questions about lianas' adaptive similarities in optical traits across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits.

Environmental Sciences & Ecology↗
DOE OSTI2025

Small-scale validation tests for MFIX-Exa CFD-DEM

This report describes several bench-scale fluidization experiments that can be used to validate the CFD-DEM method as encapsulated in the MFIX-Exa code. The five cases considered are the cold-flow fluidized beds of Müller et al., Link et al. (spout-fluid), and Goldschmidt et al. (bi-disperse), the hot fluidized bed of Patil et al., and the adsorbing fluidized bed of Li et al. and Janssen. In most cases, MFIX-Exa with “standard” or “typical” CFD-DEM settings, the Gidaspow drag model, and the Gunn heat transfer provide a relatively good prediction of the quantities considered: mean void fraction profiles, mean velocity profiles, fluctuating velocity profiles, mean particle temperature and segregation index. These results, with other verification and validation tests reported elsewhere, contribute to a body of work providing confidence and credibility in CFD predictions from the MFIX-Exa code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Transient cycling of nitrogen, organic carbon and oxygen within the free-flowing Columbia River corridor: Linking exposure time dependent biogeochemical reactions to river stage fluctuations (Final project report)

The objective of this project was to develop next-generation techniques for representing the transformations of complex reaction networks like those impacting transient river-corridors, then apply them to develop upscaling tools. The project contributed significantly to the development of Lagrangian “mass transfer particle tracking” (MTPT) tools that are the first numerical framework that explicitly separates mixing and spreading processes; a crucial distinction for accurate representations of reaction rates across scales. The MTPT approach uses an operator splitting scheme where physical transport processes are simulated using classical random walk methods and each particle is treated as a “container” that carries with it any number of chemical species. Mixing between particles is simulated using a colocation-probability based mass transfer kernel and reactions are evaluated on each particle after mixing. The method is stable, accurate, and also allows the explicit modeling of residence time distributions of the individual containers of mass. The MTPT scheme allowed us to explore several research questions related to reactive transport in river corridors and complex reaction networks. We found that it is possible to quantify the exposure time distributions (ETDs) of reactants and that this does suggest some pathways to upscaling. However, comparable approximations can be obtained using simplified (reduced dimensionality) MTPT simulations in less time with similar uncertainty, so development of extensive ETD-based methods was not productive. The major findings are that i) the MTPT schemes are robust and highly accurate across a range of arbitrarily complex reactions, ii) the methods can be efficiently parallelized and the parallel performance characteristics of MTPT are predictable, and iii) the MTPT tools allow simultaneous tracking of residence time. The development of these research tools into efficient software packages continues. The methods are already available to the community because open-source, working examples have been included with all publications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bringing discrete-time Langevin splitting methods into agreement with thermodynamics

In light of the recently published complete set of statistically correct Grønbech–Jensen (GJ) methods for discrete-time thermodynamics, we revise a differential operator splitting method for the Langevin equation in order to comply with the basic GJ thermodynamic sampling features, namely, the Boltzmann distribution and Einstein diffusion, in linear systems. This revision, which is based on the introduction of time scaling along with flexibility of a discrete-time velocity attenuation parameter, provides a direct link between the ABO splitting formalism and the GJ methods. This link brings about the conclusion that any GJ method has at least weak second order accuracy in the applied time step. It further helps identify a novel half-step velocity, which simultaneously produces both correct kinetic statistics and correct transport measures for any of the statistically sound GJ methods. Explicit algorithmic expressions are given for the integration of the new half-step velocity into the GJ set of methods. Finally, numerical simulations, including quantum-based molecular dynamics (QMD) using the QMD suite Los Alamos Transferable Tight-Binding for Energetics, highlight the discussed properties of the algorithms as well as exhibit the direct application of robust, time-step-independent stochastic integrators to QMD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Defect Quantification in Metal Halide Perovskites Anticipates Photoluminescence and Photovoltaic Performance

Semiconductor material optimization requires quantification of performance and stability-dependent near-valence maximum and near-conduction minimum defects with sufficient energy resolution and sensitivity. In this work, we utilize a spectroscopy-electrochemistry approach to resolve the energy-distinct donor and acceptor defect concentrations in wide-gap (Cs .05 FA .79 MA .16 )Pb(I .87 Br .13 ) 3 perovskites, benchmarked against photoluminescence and photovoltaic device performance. Monitoring charge transfer events to electron acceptor and donor molecules within solid electrolyte top contacts enables defect quantification below 10 15 cm –3 at an energy resolution of 10 meV under device-relevant bias, well below levels reported by other methods. Further method sensitivity is demonstrated for defects arising from <2% formamidinium concentration modifications, mimicking compositional imperfections resulting from nonoptimized processing. This method provides the first complete perovskite energetic diagrams with small changes in composition, is nondestructive, compatible with in-line processing characterization, and will enable the semiconductor community to link molecular origins of defects with limitations in device performance across a wide array of optoelectronic platforms.

14 SOLAR ENERGY↗
DOE OSTI2022

Advancing river corridor science beyond disciplinary boundaries with an inductive approach to catalyse hypothesis generation

Abstract A unified conceptual framework for river corridors requires synthesis of diverse site‐, method‐ and discipline‐specific findings. The river research community has developed a substantial body of observations and process‐specific interpretations, but we are still lacking a comprehensive model to distill this knowledge into fundamental transferable concepts. We confront the challenge of how a discipline classically organized around the deductive model of systematically collecting of site‐, scale‐, and mechanism‐specific observations begins the process of synthesis. Machine learning is particularly well‐suited to inductive generation of hypotheses. In this study, we prototype an inductive approach to holistic synthesis of river corridor observations, using support vector machine regression to identify potential couplings or feedbacks that would not necessarily arise from classical approaches. This approach generated 672 relationships linking a suite of 157 variables each measured at 62 locations in a fifth order river network. Eighty four percent of these relationships have not been previously investigated, and representing potential (hypothetical) process connections. We document relationships consistent with current understanding including hydrologic exchange processes, microbial ecology, and the River Continuum Concept, supporting that the approach can identify meaningful relationships in the data. Moreover, we highlight examples of two novel research questions that stem from interpretation of inductively‐generated relationships. This study demonstrates the implementation of machine learning to sieve complex data sets and identify a small set of candidate relationships that warrant further study, including data types not commonly measured together. This structured approach complements traditional modes of inquiry, which are often limited by disciplinary perspectives and favour the careful pursuit of parsimony. Finally, we emphasize that this approach should be viewed as a complement to, rather than in place of, more traditional, deductive approaches to scientific discovery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Molecular Design of a Highly Stable Single-Ion Conducting Polymer Gel Electrolyte

Single-ion conducting (SIC) polymer electrolytes with a high Li transference number ( t Li + ) have shown the capability to enable enhanced battery performance and safety by avoiding liquid–electrolyte leakage and suppressing Li dendrite formation. However, issues of insufficient ionic conductivity, low electrochemical stability, and poor polymer/electrode interfacial contact have greatly hindered their commercial use. In this work, a Li-containing boron-centered fluorinated SIC polymer gel electrolyte (LiBFSIE) was rationally designed to achieve a high t Li + and high electrochemical stability. Owing to the low dissociation energy of the boron-centered anion and Li + , the as-prepared LiBFSIE exhibited an ionic conductivity of 2 × 10 –4 S/cm at 35 °C, which is exclusively contributed by Li ions owing to a high t Li + of 0.93. Both simulation and experimental approaches were applied to investigate the ion diffusion and concentration gradient in the LiBFSIE and non-cross-linked dual-ion systems. Typical rectangular Li stripping/plating voltage profiles demonstrated the uniform Li deposition assisted by LiBFSIE. The interfacial contact and electrolyte infiltration were further optimized with an in situ UV–vis-initiated polymerization method together with the electrode materials. By virtue of the high electrochemical stability of LiBFSIE, the cells achieved a promising average Coulombic efficiency of 99.95% over 200 cycles, which is higher than that of liquid–electrolyte-based cells. No obvious capacity fading was observed, indicating the long-term stability of LiBFSIE for lithium metal batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Transductive Graph Neural Network learning for Grid Resilience Analysis

Power grids are critical infrastructures that require robust resilience analysis to ensure reliable and uninterrupted electricity supply. Traditional simulation-based methods for grid resilience analysis suffer from computational complexity and limited ability to capture the full spectrum of potential disruptions. This paper presents a novel approach to enhance grid resilience by leveraging transductive graph neural network (GNN) learning to identify critical nodes and links. By leveraging the graph structure and system features, GNNs effectively learn resilience metrics and accurately identify critical nodes based on actual grid operational behavior. The efficacy of the proposed approach is demonstrated through case studies on node criticality scoring and critical node/line identification in cascading outage scenarios. The results highlight the advantages of learning-based methods over traditional simulation-based approaches and their potential to revolutionize grid resilience analysis. The contributions of this paper include a graph-based scalable approach for fast cascading analysis, an inductive formulation for training GNN models, and a transfer learning-based approach to scale the model to largescale power systems.

grid resilience, graph neural networks, transducti↗
DOE OSTI2024

A Contextually Supervised Optimization-Based HVAC Load Disaggregation Methodology

This paper presents a novel contextually supervised optimization-based approach for disaggregating heating, ventilation, and air-conditioning (HVAC) loads using smart meter or Supervisory Control and Data Acquisition data. To disaggregate the load into HVAC loads, large and infrequently used loads (LIUL), and base loads, we formulate an optimization problem to minimize a set of five loss terms, consisting of the reconstruction errors of the overall load profile, the ramp rate losses, and three distinct loss functions linked with the HVAC load, base load, and LIUL, respectively. To enhance accuracy, we incorporate two forms of contextual information into the problem formulation. First, we utilize mutual information to estimate HVAC energy consumption. Second, we employ a base load dictionary to constrain HVAC load estimation errors. The obtained HVAC load profiles are fine-tuned by abnormal ramp detection followed by binary hypothesis testing. Here, the proposed method is developed and tested using sub-metered residential and commercial building data. Simulation results show that the proposed method outperforms existing methods across various data resolutions and load aggregation levels, showing excellent transferability and generalizability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Renewable Integration in Hybrid ac-dc Systems using Multi-port Autonomous Reconfigurable Solar power plant (MARS)

To increase inertia and improve primary frequency response of future grids, grid-forming inverters connecting PV to grid and energy storage systems (ESSs) are likely to play an important role. Moreover, high-voltage direct current (HVdc) links will also be an enabler to transfer remote PV generation and for improved grid stability. That is, with increased penetration of PV, discrete development of PV and ESS connecting to transmission ac grid and HVdc links is one of the solutions for stable operation of grid. Here, an integrated concept for integration of PV and ESS to transmission ac grid and HVdc links is proposed that is named as multi-port autonomous reconfigurable solar power plant (MARS). The proposed integrated development reduces the cost associated with respect to discrete development of PV-ESS systems connecting to transmission ac grid and HVdc links. The integrated development incorporates advanced control methods to provide inertial and primary frequency response, reactive power support, and transient stability to manage PV and ESS resources. In this paper, high-fidelity switched system model of the integrated system and grids are developed and detailed simulation results are provided to showcase the stable operation of the integrated system and provision of grid support functions.

14 SOLAR ENERGY↗
DOE OSTI2026

Structural coherence model for predicting molten salt thermal conductivity informed by the pair distribution function

To enable thermal behavior prediction and design optimization of molten salt reactors, thermal conductivity of molten salts must be characterized in terms of salt composition and temperature. Current theoretical models fail to provide consistent approximations for all halide mixtures, particularly actinide-bearing melts. This study aims to link the short-range order structure of molten salts to the mean free path of energy carriers through a simple structural coherence model informed by the partial pair distribution function. The proposed method is used to predict the thermal conductivity of 33 alkali and alkaline earth halide salts. Predictions approximate experimental measurements with a mean absolute error of 15.7% for dissociating, complexing, and actinide salts, including unary LiCl, NaCl, and MgCl 2 as well as mixtures LiF–NaF–KF (FLiNaK), LiF–BeF 2 (FLiBe), and NaCl–UCl 3 . The work provides evidence for the validity of energy carrier descriptions of molecular-level heat transfer in molten salts, with implications for improved theories of liquid energy transport in general.

Actinide mixtures↗
DOE OSTI2020

Expanded Phylogenetic Diversity and Metabolic Flexibility of Mercury-Methylating Microorganisms

Methylmercury is a potent bioaccumulating neurotoxin that is produced by specific microorganisms that methylate inorganic mercury. Methylmercury production in diverse anaerobic bacteria and archaea was recently linked to the hgcAB genes. However, the full phylogenetic and metabolic diversity of mercury-methylating microorganisms has not been fully unraveled due to the limited number of cultured experimentally verified methylators and the limitations of primer-based molecular methods. Here, we describe the phylogenetic diversity and metabolic flexibility of putative mercury-methylating microorganisms by hgcAB identification in publicly available isolate genomes and metagenome-assembled genomes (MAGs) as well as novel freshwater MAGs. We demonstrate that putative mercury methylators are much more phylogenetically diverse than previously known and that hgcAB distribution among genomes is most likely due to several independent horizontal gene transfer events. The microorganisms we identified possess diverse metabolic capabilities spanning carbon fixation, sulfate reduction, nitrogen fixation, and metal resistance pathways. We identified 111 putative mercury methylators in a set of previously published permafrost metatranscriptomes and demonstrated that different methylating taxa may contribute to hgcA expression at different depths. Overall, we provide a framework for illuminating the microbial basis of mercury methylation using genome-resolved metagenomics and metatranscriptomics to identify putative methylators based upon hgcAB presence and describe their putative functions in the environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Diabatization with Electrostatic Embedding for Studying Photophysics in Organic Molecular Crystals

Highly emissive organic molecular crystals find applications in several areas, such as organic electronics, solar cells, and sensors. Understanding the excited-state mechanisms underlying these applications is essential for optimizing and controlling them effectively. Exciton models coupled with nonadiabatic dynamics, particularly quantum dynamics, provide crucial insights into photochemical and photophysical processes in molecular crystals. Nevertheless, there remains a lack of general tools and automated workflows to facilitate such simulations. In this paper, we present a computational strategy to investigate the photoactivated dynamics of organic molecular crystals, bridging methodologies traditionally used for molecular systems and materials science, with a particular focus on the interplay between local excitations and charge transfer (CT) processes. We have implemented an interface between the fromage and Overdia programs, enabling the construction of vibronic Hamiltonians for molecular crystals within an excited-state ONIOM(QM:QM′) framework, incorporating long-range electrostatics through a RESP-based Ewald summation. Fragment-based diabatization provides a route to quantum dynamics simulations in weak-to-intermediate coupling regimes. The method was applied to the photophysics of dibenzo[g,p]chrysene (DBC) crystals using time-dependent DFT. The fromage/ Overdia interface was employed to compute the couplings of local excitations and CT states for 18 unique DBC dimers in the crystal and to quantify the influence of electrostatic embedding, which was found to be modest (10−20%). Simulations on π-stacked dimers reproduced the small red shift observed experimentally from solution to crystal, attributed to electronic interactions among fixed monomers rather than crystal electrostatics. Quantum dynamics simulations revealed ultrafast population transfer from bright local excitations to CT states. This approach establishes a robust framework linking molecular and solid-state excited-state dynamics, with potential applications for studying excitations, defects, and impurities in molecular crystals.

Crystals↗
DOE OSTI2023

Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application

Solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in modeling global terrestrial gross primary production (GPP). Canopy total SIF emissions (SIF total ) are mechanistically linked to the plant photosynthesis, and can be estimated from satellite observed SIF (SIF obs ) through radiative transfer modeling. However, the current satellite SIF obs and thus SIF total are available only at coarse spatial resolutions from several kilometers to tens of kilometers, inhibiting the application at fine spatial scales. Here, in this work, we proposed an algorithm to generate both global high-resolution SIF total (HSIF total ) and high-resolution SIF obs (HSIF obs ) at 1 km from low-resolution SIF obs (LSIF obs ) from the TROPOspheric Monitoring Instrument (TROPOMI), which has a spatial resolution at nadir of 3.5 km by 5.6–7 km. Our statistical method is based on the law of energy conservation and uses satellite derived fraction of absorbed photosynthetically active radiation, fluorescence efficiency, and the escape probability of fluorescence. We evaluated the accuracy of our HSIF total using the Orbiting Carbon Observatory-2 SIF (R 2 = 0.78). We found that the spatial resolution had clear effects on the relationship between HSIF total and GPP. We also compared HSIF total to 8-day averaged tower GPP from 135 flux sites and found that they were better correlated when HSIF total was averaged over a 1-km radius around the tower than when averaged over a larger radius. Our study provided a unique high-resolution HSIF total product, which will advance the estimation of GPP by extrapolating site-level relationships to the global scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantitative measurements of reactive oxygen species partitioning in electron transfer flavoenzyme magnetic field sensing

Biological magnetic field sensing that gives rise to physiological responses is of considerable importance in quantum biology. The radical pair mechanism (RPM) is a fundamental quantum process that can explain some of the observed biological magnetic effects. In magnetically sensitive radical pair (RP) reactions, coherent spin dynamics between singlet and triplet pairs are modulated by weak magnetic fields. The resulting singlet and triplet reaction products lead to distinct biological signaling channels and cellular outcomes. A prevalent RP in biology is between flavin semiquinone and superoxide (O 2 •–) in the biological activation of molecular oxygen. This RP can result in a partitioning of reactive oxygen species (ROS) products to form either O 2 •– or hydrogen peroxide (H 2 O 2 ). Here, we examine magnetic sensing of recombinant human electron transfer flavoenzyme (ETF) reoxidation by selectively measuring O 2 •– and H 2 O 2 product distributions. ROS partitioning was observed between two static magnetic fields at 20 nT and 50 μT, with a 13% decrease in H 2 O 2 singlet products and a 10% increase in O 2 •– triplet products relative to 50 µT. RPM product yields were calculated for a realistic flavin/superoxide RP across the range of static magnetic fields, in agreement with experimental results. For a triplet born RP, the RPM also predicts about three times more O 2 •– than H 2 O 2 , with experimental results exhibiting about four time more O 2 •– produced by ETF. The method presented here illustrates the potential of a novel magnetic flavoprotein biological sensor that is directly linked to mitochondria bioenergetics and can be used as a target to study cell physiology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Rays for Roots - Integrating Backscatter X-Ray Phenotyping, Modeling and Genetics to Increase Carbon Sequestration and Switchgrass Resource Use (Final Report)

To increase carbon (C) deposition in the soil and enhance crop resource use efficiency, characterizing root form and function is essential. Several root and soil traits have been linked to increased root-to-soil C transfer. Technology that could provide high-resolution characterization of many of these traits in field conditions would revolutionize our ability to study and understand how to increase C sequestration. In this effort, we developed an initial early prototype backscatter X-ray system for non-destructive imaging of root traits. We collected initial backscatter X-ray data in field and lab settings and carried out early analysis of these data. Along with this prototype, we also developed a suite of root phenotyping approaches including advanced minirhizotron image analysis, soil core imaging, and mesocosm imaging. Minirhizotron (MR) tubes are clear tubes inserted into the soil in the field and used to image roots and the surrounding soil. Our team has developed deep learning-based methods that can segment roots from soil that can learn from imprecise image-level labels. The ability to learn or fine-tune our deep learning algorithms from image-level labels allows easier and faster application of these approaches to new locations and new plant species. We have successfully implemented and applied our MR analysis approaches to thousands of switchgrass MR images collected across geographical regions. An advantage of MR imaging is the ability to collect root and soil images over time. Our soil core analysis included collecting hundreds of soil core samples from harvested switchgrass fields and imaging these cores with both X-ray CT and backscatter X-ray imaging. Initial segmentation approaches for the X-ray CT images of these cores have been developed and applied. An advantage of soil core analysis is that it preserves the three-dimensional structures of the roots and soil in the core collected. Our group also developed photogrammetry-based mesocosm root imaging and phenotyping approaches. In this approach, a plant was grown in a large mesocosm with a three-dimensional grid of thin supporting lines inserted throughout the mesocosm. After the plant (and, correspondingly, the root architecture is grown and established) the soil media was removed and the supporting lines approximately preserved the three-dimensional root architecture. Then, we applied photogrammetry techniques to create a three-dimensional digital representation of the root architecture for which we developed analysis algorithms including skeletonization. We carried out our phenotyping development with powerful switchgrass resources and physiological and agroecosystem modeling to deliver novel technology. This project contributes to multiple ARPA-E missions including reduction of foreign imports of energy, reduction of energy-related emissions including greenhouse gases, and ensuring that the United States maintains a technological lead in developing and deploying advanced energy technology. Furthermore, the developed tools could transform public and private plant breeding and could be broadly applicable to other crops and, potentially, other application areas. Our team of engineers, plant and soil scientists, and modelers i) developed an early prototype backscatter X-ray platform that can operate in field conditions; ii) developed a suite of root phenotyping and characterization approaches as described above; iii) developed and carried out plant biology and physiology roots studies and; iv) developed and implemented mechanistic physiological modeling.

42 ENGINEERING↗
DOE OSTI2022

A Novel Anatomic Reconstruction for Posterior Tibialis Tendon in Treatment of Flexible Adult‐Acquired Flatfoot Deformity

Objective To present a novel approach for the anatomic reconstruction of the posterior tibialis tendon (PTT) in restoring plantar insertions and evaluate its efficiency in treating flexible adult‐acquired flatfoot deformity (AAFD) caused by PTT dysfunction. Methods For AAFD treatment, a novel PTT reconstruction method was presented. The current study involved 16 patients, including three men, and 13 women, from August 2017 to July 2019. The mean age was 43.2 ± 15.1 years (21–64 years). The innovative PTT repair method was used on all patients. The treatment involved performing a traditional Flexor Digitorum Longus (FDL) transfer in the navicular tuberosity and suturing the plantar insertions to FDL as tension was applied to tighten the plantar structures of the foot. The results were retrospectively analyzed. The clinical outcome was assessed using the pain visual analogue scale (VAS), the satisfaction VAS, and the American Orthopedic Foot and Ankle Society ankle–hindfoot scale (AOFAS‐AH). Isokinetic testing was performed using a dynamometer at 60°/s and 120°/s for inversion/eversion and plantarflexion/dorsiflexion, respectively, to determine the mean peak torque. Radiographic measurements were employed to assess the outcomes. Results Bone surgeries combined with the modified anatomic PTT reconstruction were performed on patients with medializing calcaneal osteotomy in 12 (75%) patients and subtalar joint fusion in four (25%) patients. The branch linking to the plantar insertions was detected in every case, with an average width of 3.5 ± 0.8 mm (3.1–4.3 mm). All patients were followed up for the mean of 16.8 ± 1.8 months (range, 15–20 months). The average postoperative functional scores, including pain VAS, satisfaction VAS, total AOFAS‐AH, and all AOFAS‐AH sub‐scales, steadily improved during the follow‐up. In the last follow‐up, isokinetic testing revealed no loss of plantarflexion strength ( p = 0.350 and 0.098) and significant improvement in the inversion strength ( p = 0.007 and 0.008) in the operated ankles at 60°/s and 120°/s. Radiographic outcomes, particularly the talar head uncovering, improved significantly after more than a year ( p < 0.001 for all). Conclusions The novel technique for PTT reconstruction in restoring the plantar insertions serves as an effective procedure in treating AAFD caused by PTT dysfunction in terms of delivering a consistent improvement in ankle inversion strength, medial longitudinal arch restoring, and satisfactory clinical outcomes.

Hu, Yong↗