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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Diagnosing the origin and impact of low-mode asymmetries in ignition experiments at the National Ignition Facility

Inertial confinement fusion ignition requires high inflight shell velocity, good energy coupling between the hotspot and shell, and high areal density at peak compression. Three-dimensional asymmetries caused by imperfections in the drive symmetry or target can grow and damage the coupling and confinement. Recent high-yield experiments have shown that low-mode asymmetries are a key degradation mechanism and contribute to variability. Here we show the experimental signatures and impacts of asymmetry change with increasing implosion yield given the same initial cause. This letter has implications for improving robustness to a key degradation in ignition experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Modeling In-Reactor Temperature Gradients in Lithium Aluminate Ceramic Pellets

In reactor thermal performance of lithium aluminate ceramic pellets are calculated to determine operating temperatures if internal components shift during irradiation. A 2D conduction model was setup to model this scenario with additional physics, including thermal expansion of components to reactor temperatures, creep down of the cladding due to differential pressure between the inside of the rod and reactor coolant system, and net radiation exchange between components. The non-linear coupling of the governing equations presented convergence challenges due to the very different effects from a small temperature change on the radiation vs radial and circumferential conduction equations. Initial temperature guesses for the multidimensional solution were provided from conservative 1D models simulating bounding scenarios. Convergence was greatly improved by taking small steps towards the root solutions and averaging with initial guesses. The final method robustly converged all 132 points at an axial plane after a few dozen iterations. The temperature results show that the additional physics have a secondary effect as the dominant heat transfer mechanism is radial conduction across the gas gap followed by circumferential conduction around components. Circumferential conduction in the lithium aluminate pellets is enhanced by the relatively high thermal conductivity of this ceramic.

Carstens, Nathan A.↗
DOE OSTI2024

Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field

Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

For centuries, flow visualization has been the art of making fluid motion visible in physical and biological systems. Although such flow patterns can be, in principle, described by the Navier-Stokes equations, extracting the velocity and pressure fields directly from the images is challenging. We addressed this problem by developing hidden fluid mechanics (HFM), a physics-informed deep-learning framework capable of encoding the Navier-Stokes equations into the neural networks while being agnostic to the geometry or the initial and boundary conditions. We demonstrate HFM for several physical and biomedical problems by extracting quantitative information for which direct measurements may not be possible.

Raissi, Maziar↗
DOE OSTI2022

Robust Atomic-Resolution Imaging of Lithium in Battery Materials by Center-of-Mass Scanning Transmission Electron Microscopy

The performance of energy storage materials is often governed by their structure at the atomic scale. Conventional electron microscopy can provide detailed information about materials at these length scales, but direct imaging of light elements such as lithium presents a challenge. While several recent techniques allow lithium columns to be distinguished, these typically either involve complex contrast mechanisms that make image interpretation difficult or require significant expertise to perform. In this work, we demonstrate how center-of-mass scanning transmission electron microscopy (CoM-STEM) provides an enhanced ability for simultaneous imaging of lithium and heavier element columns in lithium ion conductors. Through a combination of experiments and multislice electron scattering calculations, we show that CoM-STEM is straightforward to perform and produces directly interpretable contrast for thin samples, while being more robust to variations in experimental parameters than previously demonstrated techniques. As a result, CoM-STEM is positioned to become a reliable and facile method for directly probing all elements within energy storage materials at the atomic scale.

25 ENERGY STORAGE↗
DOE OSTI2021

Simple, Secure, Internet Delivery of MOOSE-based Applications

Application packaging and distribution are the final steps for delivering software to end-users; both are frequently neglected when creating scientific software. Commercial businesses rely on electronic distribution systems that have rendered disk drives obsolete. Still, national laboratories continue to rely heavily on removable media to distribute and limit access to controlled applications. With increasing concerns of unauthorized copying of sensitive applications, a modern distribution system that utilizes cryptographically secure communication and authentication protocols has been developed. This new distribution system will secure the chain of custody for nuclear software while simultaneously simplifying access to these tools. This report summarizes four primary advancements made toward the secure distribution of Nuclear Energy Advanced Modeling and Simulation (NEAMS)-developed, Multiphysics Object Oriented Simulation Environment (MOOSE)-based applications: application installation, package distribution, automated package building, and distribution of documentation. NEAMS is currently developing more than ten separate applications based on the open-source MOOSE Framework. Distribution of these applications has primarily been accomplished by distributing source code, with end-users compiling the applications themselves. This work created a mechanism where MOOSE applications can be installed in a similar way to any other software. This allows both administrators and end-users simplified access to runnable executables. With this new installation capability, it was then possible to rethink distribution. A new, secure capability for delivering MOOSE-based applications over the internet has been created. This system requires unique cryptographic tokens for authentication, greatly securing the custody chain for software. Once granted access, installation of any NEAMS code can be accomplished with these terminal commands: "conda install ncrc" "ncrc install ncrc-bison." After these two commands (and authenticating) the BISON application will be securely down- loaded from Idaho National Laboratory (INL)’s servers, installed, and ready to use. To enable this new distribution capability to be successful, the open-source Continuous Integration, Verification, Enhancement, and Testing (CIVET) Continuous Integration (CI) capability was augmented to add Continuous Delivery (CD). CD enables the automated building and packaging of MOOSE-based applications as they are modified by development teams, ensuring that our customers can obtain up-to-date versions of the software at any time. The need for instruction on how to use these applications was addressed through modifications to the MOOSE documentation system. The MooseDocs capability, which enables robust documentation of MOOSE-based applications, has been extended to allow both for the installation of documentation and the packaging of documentation with installed applications. Together, these enhancements form the core of a new, secure distribution mechanism for nuclear simulation tools. In concert with the Nuclear Computational Resource Center (NCRC), NEAMS- developed applications will now be straightforward to obtain securely.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Enabling the Broader Use of MOOSE for Nuclear Energy and Other Simulation

This Final Scientific and Technical Report summarizes work performed under the Phase IIA SBIR project “Enabling the Broader Use of MOOSE for Nuclear Energy and Other Simulation” (DE-SC0020906) from August 2023 through August 2025. The objective of the Phase IIA effort was to mature and harden capabilities developed during Phase II, with the goal of enabling practical interoperability between Coreform’s isogeometric analysis (IGA) technologies and the Multiphysics Object-Oriented Simulation Environment (MOOSE), while improving robustness, performance, and scalability for complex, nuclear-relevant geometries. Over the course of Phase IIA, the project established and validated an extraction-based interoperability pathway between Coreform tools and MOOSE. A combined mesh and matrix format was defined collaboratively with MOOSE developers and integrated into the solver, enabling standard MOOSE workflows to operate on data exported from Coreform’s IGA and Flex Representation Method (FRM) pipelines. Early demonstrations validated architectural compatibility using linear solid mechanics problems, while later efforts focused on benchmark testing and external use. By the end of the project period, engineers at BWXT were able to independently set up and execute a simulation using the Coreform–MOOSE workflow and provide direct feedback that informed further refinement. In parallel, substantial effort was devoted to improving the robustness of trimmed U-spline construction for complex CAD geometries. A growing test suite of nuclear-relevant models was compiled through collaboration with multiple stakeholders and used to drive extensive bug fixing and reliability improvements. These efforts resulted in improved robustness and performance, including the addition of fallback capabilities that enhance reliability when the underlying commercial CAD kernel fails. Performance-oriented work progressed later in the project, with the development and demonstration of methods to decompose complex geometries into structured subregions and updated data representations to support more efficient solver processing. Additionally, extensive enhancements to threadsafe parallel data structures and trimming operations established a foundation for scalable processing of large assemblies. Collaboration with Sandia National Laboratories on the SGM geometric modeling kernel advanced to a functioning interface test case, positioning the workflow for future kernel integration. Overall, the Phase IIA effort successfully transitioned the project from architectural proof-of-concept to externally exercised, solver-integrated capability, while clarifying remaining technical challenges related to standardization, performance optimization, and kernel integration.

42 ENGINEERING↗
DOE OSTI2023

A heat flux sensor leveraging the transverse Seebeck effect in elemental antimony

Certain configurations of anisotropic single crystal materials can generate a thermoelectric voltage orthogonal to an induced temperature gradient. This phenomenon is known as the Transverse Seebeck Effect (TSE) and can be leveraged to fabricate simple and robust heat flux sensors. Only a small number of materials have been considered as TSE-based transducers and, among these, few have been developed into sensors with ruggedization against chemical and mechanical degradation. Here, we report on the fabrication and characterization of a rugged TSE-based heat flux sensor using prismatic antimony single crystals. The heat flux sensor was tested under static and dynamic heating scenarios. The sensor has a linear responsivity of 16.8 µV/(W/cm2) to heat fluxes spanning more than two orders of magnitude and a time constant of 4.4 s. The sensor’s response to localized heating, probed with a laser scanning technique, validated that the transduction mechanism is primarily the TSE by ruling out a sizable contribution from the conventional Seebeck effect. Finite element analysis corroborated that components used in the sensor package are the primary determinants of the time constant and the decrement of the responsivity from its theoretical maximum. Design principles that may be applied to elicit a faster transient response or higher responsivity are proposed. Furthermore, the results establish single crystal antimony as a promising transducer material for heat flux measurement systems and demonstrate potential effects of ruggedization on sensor performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unveiling Synergetic Photocatalytic Activity from Heterometallic Ti/Ce Clusters

Titanium-oxo clusters, with their robust structure and suitable optical and electronic properties, have been widely investigated as photocatalysts. Heterometallic Ti/M-oxo clusters provide additional tunability and functionality, which enable systematic structure-activity investigations to elucidate reaction mechanisms and improve catalyst de-sign. Incorporating cerium into Ti-oxo clusters can provide additional redox (Ce IV /Ce III ) and oxygen harvesting ability, but to date only limited number of structurally defined titanium-cerium (Ti/Ce) clusters have been reported due to their synthetic challenges. Herein, we report the synthesis and photocatalytic properties of two structurally defined Ti/Ce-oxo clusters, Ti 8 Ce 2 (BA) 16 and Ti 9 Ce 4 (BA) 20 , as well as a TiCe-BA cluster with a calculated formula of Ti 20 Ce 9 O 36 (BA) 42 . Photocatalytic study of these clusters demonstrates that the amount of Ce 3+ species greatly impacts its photocatalytic oxidation performance, and their superior photocatalytic reactivity towards aerobic alcohol oxidation can be contributed to the synergistic effects of the multiple radical species generated upon light absorption. Furthermore, this work represents a significant milestone in the construction of stable Ti/Ce-oxo clusters, enriching the current library of known heterometallic Ti/M-oxo clusters, and providing a series of crystalline materials with great promise of photo-luminescence and photovoltaic chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Biopolymer-supramolecular polymer hybrids for photocatalytic hydrogen production

Solar generation of H 2 is a promising strategy for dense energy storage. Supramolecular polymers composed of chromophore amphiphile monomers containing perylene monoimide (PMI) have been reported as crystalline light-harvesting assemblies for aqueous H 2 -evolving catalysts. Gelation of these supramolecular polymers with multivalent ions creates hydrogels with high diffusivity but insufficient mechanical stability and catalyst retention for reusability. We report here on using sodium alginate (SA) biopolymer to both induce supramolecular polymerization of PMI and co-immobilize them with catalysts in a robust hydrogel with high diffusivity that can also be 3D-printed. Faster mass transfer was achieved by controlling the material macrostructure by reducing gel diameter and microstructure by reducing biopolymer loading. Optimized gels produce H 2 at rates rivaling solution-based PMI and generate H 2 for up to 6 days. The PMI assemblies in the SA matrix create a percolation network capable of bulk-electron transfer under illumination. These PMI–SA materials were then 3D-printed on conductive substrates to create 3D hydrogel photoelectrodes with optimized porosity. The design of these versatile hybrid materials was bioinspired by the soft matter environment of natural photosynthetic systems and opens the opportunity to carry out light-to-fuel conversion within soft matter with arbitrary shapes and particular local environments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Simulating molecular polaritons in the collective regime using few-molecule models

The study of molecular polaritons beyond simple quantum emitter ensemble models (e.g., Tavis–Cummings) is challenging due to the large dimensionality of these systems and the complex interplay of molecular electronic and nuclear degrees of freedom. This complexity constrains existing models to either coarse-grain the rich physics and chemistry of the molecular degrees of freedom or artificially limit the description to a small number of molecules. In this work, we exploit permutational symmetries to drastically reduce the computational cost of ab initio quantum dynamics simulations for large N . Furthermore, we discover an emergent hierarchy of timescales present in these systems, that justifies the use of an effective single molecule to approximately capture the dynamics of the entire ensemble, an approximation that becomes exact as N → ∞. We also systematically derive finite N corrections to the dynamics and show that addition of k extra effective molecules is enough to account for phenomena whose rates scale as 𝒪( N − k ). Based on this result, we discuss how to seamlessly modify existing single-molecule strong coupling models to describe the dynamics of the corresponding ensemble. We call this approach collective dynamics using truncated equations (CUT-E), benchmark it against well-known results of polariton relaxation rates, and apply it to describe a universal cavity-assisted energy funneling mechanism between different molecular species. Beyond being a computationally efficient tool, this formalism provides an intuitive picture for understanding the role of bright and dark states in chemical reactivity, necessary to generate robust strategies for polariton chemistry.

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

Developing nanometer scale, atomically precise metallo-catalysts with Molecular Lego

We propose to create atomically precise, highly robust, nanometer scale macromolecules that organize biomimetic metal binding groups in designed three-dimensional pockets to accelerate specific polymerization reactions. These atomically precise catalysts will create atomically precise polymers with improved mechanical and environmental properties from renewable building blocks. We seek to create and study nanometer scale catalysts that mimic metallo-enzymes in their activity and selectivity because these larger catalysts will be able to make more extensive non-covalent contacts with the transition state of the growing polymer. These catalysts will have extended lifetimes because they will better protect the metals and higher activity because additional reactive groups can be brought in close contact with the active site to enhance reactivity. They will be far more tolerant to extremes of temperature and will not denature in non-aqueous solvents because they are held together by multiple, strong, covalent bonds. Our approach to catalysts could be used to develop catalysts to replace noble metal-based catalysts when targeting other reactions, because the geometry of the active site enforced by the Molecular Lego scaffolding will access new reactivity, mimicking how nature uses earth abundant metals for much of its catalysis. We will initially target Lewis acid-based catalysts that assemble aliphatic polyesters with improved stereoselectivity, tacticity and complex alternation of monomers. Aliphatic polyesters have received growing attention as attractive, environmentally benign, and sustainable alternatives to polymers developed from petroleum feedstocks. Aliphatic polyesters are environmentally friendly because they undergo facile hydrolytic degradation to benign products, and they have excellent properties and high biocompatibility. We will assemble these catalysts using unique “Spiroligomers” (aka Molecular Lego) chemistry developed in the Schafmeister group combined with the organometallic chemistry expertise of the Dobereiner group. Polymers are an attractive synthetic target of nanoscale atomically precise catalysts because their resulting structure, stereochemistry and tacticity has profound impact on their properties (crystalline vs amorphous) and provides a readout of the catalytic mechanism. Polymer synthesis also serves as an example of “atomically precise manufacturing” where atomically precise nanoscale catalysts construct polymers with atomic precision at increasing length scales.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A generalized machine learning workflow to visualize mechanical discontinuity

Accurate detection and mapping of mechanical discontinuity in materials has widespread industrial and research applications. Herein, we developed a generalized machine-learning framework for visualizing single mechanical discontinuity embedded in material of any composition, velocity, density, porosity, and size with limited data. The proposed visualization of discontinuity requires accurate estimations of the length, location, and orientation of the embedded discontinuity by processing multipoint wave-transmission measurements. k-Wave simulator is used to create a large dataset of elastic waveforms recorded during multi-point wave-transmission measurements through materials containing single mechanical discontinuity. k-Wave simulator considers the wave attenuation, dispersion, and mode conversion in wave motion. Discrete wavelet transform (DWT) and statistical feature extraction are essential for data preprocessing prior to the data-driven model development. DWT also minimizes the effect of noise. Using hyper-parameter tuning and cross validation, gradient boosting regression can visualize the mechanical discontinuity with an accuracy of 0.85, in terms of coefficient of determination. A double-layered neural network-based regression has better performance with an accuracy of 0.95. Use of convolutional neural network converts the predictive task from a waveform processing to an image processing problem. Convolutional neural network achieved a generalization performance of 0.91. The proposed generalized workflow requires robust simulation of wave propagation, signal processing, feature engineering, and model evaluation. Sensors closest to the source and those located opposite the source are the most significant for the desired visualization. Notably, the sensors closest to the source capture the non-linear associations, whereas the sensor on the border opposite to the source capture the linear associations between the measured waveforms and the properties of the mechanical discontinuity.

42 ENGINEERING↗
DOE OSTI2022

Scalable preconditioning for the stabilized contact mechanics problem

We present a family of preconditioning strategies for the contact problem in fractured and faulted porous media. We combine low-order continuous finite elements to simulate the bulk deformation with piecewise constant Lagrange multipliers to impose the frictional contact constraints. This formulation is not uniformly inf-sup stable and requires stabilization. We improve previous work by Franceschini et al. (2020) by introducing a novel jump stabilization technique that requires only local geometrical and mechanical properties. We then design scalable preconditioning strategies that take advantage of the block structure of the Jacobian matrix using a physics-based partitioning of the unknowns by field type, namely displacement and Lagrange multipliers. The key to the success of the proposed preconditioners is a pseudo-Schur complement obtained by eliminating the Lagrange multiplier degrees of freedom, which can then be efficiently solved using an optimal multigrid method. Numerical results, including complex real-world problems, are presented to illustrate theoretical properties, scalability and robustness of the preconditioner. A comparison with other approaches available in the literature is also provided.

58 GEOSCIENCES↗
DOE OSTI2026

A Circular and Tacticity‐Independent Crystalline Mono‐Substituted Nylon‐6 Platform: Unexpected Large Positional Effects on Crystallizability and Performance

Seeking recyclable, more sustainable alternatives to nylon 6 has drawn much attention, but achieving its variants with both crystallinity and enhanced recyclability still remains a challenge. Here, by utilizing bio-derivable mono-substituted racemic lactam monomers, we reveal surprisingly large effects of methyl substitution positions on the nylon-6 backbone on crystallizability, thermomechanical performance, and recyclability of the resulting atactic nylon-6 variants. While γ-methyl substitution gives an amorphous nylon, all other four methyl-substitution positions (α, β, δ, and ε) afford, unexpectedly, crystalline nylons with melting temperatures ranging from 145°C to 200°C and tunable mechanical performance from being stiff and strong (α, ε) to ductile (β, δ). Investigations reveal that the crystallinity of atactic nylons arises independently of stereoregularity, which is driven by the amide backbone with robust hydrogen-bonding interactions and countered by the chain flexibility regulated by the substitution position. These nylons can be chemically recycled back to their parent monomers with high isolated yields up to 92%, enabling a circular, tacticity-independent crystalline nylon platform.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs

In recent years, a new kind of accelerated hardware has gained popularity in the artificial intelligence (AI) community which enables extremely high-performance tensor contractions in reduced precision for deep neural network calculations. In this article, we exploit Nvidia Tensor cores, a prototypical example of such AI-hardware, to develop a mixed precision approach for computing a dense matrix factorization of the inverse overlap matrix in electronic structure theory, S –1 . This factorization of S –1 , written as ZZT = S –1 , is used to transform the general matrix eigenvalue problem into a standard matrix eigenvalue problem. Here we present a mixed precision iterative refinement algorithm where Z is given recursively using matrix–matrix multiplications and can be computed with high performance on Tensor cores. To understand the performance and accuracy of Tensor cores, comparisons are made to GPU-only implementations in single and double precision. Additionally, we propose a nonparametric stopping criteria which is robust in the face of lower precision floating point operations. The algorithm is particularly useful when we have a good initial guess to Z, for example, from previous time steps in quantum-mechanical molecular dynamics simulations or from a previous iteration in a geometry optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Radiative Electronic Bound States in the Continuum from Defects in Semiconductors

Continuum-buried defect states in semiconductors are generally expected to be optically inactive because of their strong coupling to continuum bands. Here, we show that such defects can instead host radiative electronic bound states in the continuum (BICs) using the silicon G center as a prototypical example. Hybrid functional first-principles calculations with a Hubbard U correction reveal that a localized defect state, initially buried below the valence band maximum (VBM) in the ground state, undergoes exchange-driven energy-level reordering under optical excitation and shifts above the VBM. This exchange-induced transition suppresses nonradiative decay and enables a robust radiative emission. By computing temperature-dependent nonradiative lifetimes and comparing them to experimental photoluminescence (PL) lifetimes, we quantitatively reproduce the observed temperature dependence of the emission. These results uncover a stabilization mechanism for continuum-embedded defect states and establish electronic BICs as a general paradigm for designing defect-based optical systems, including quantum emitters and qubits.

Hong, SeongYun↗
DOE OSTI2024

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, in this paper, we describe and illustrate PeakQC’s functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

47 OTHER INSTRUMENTATION↗