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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Large-scale experimental validation of thermochemical water-splitting oxides discovered by defect graph neural networks

Thermochemical water-splitting (TCH) based on 2-step thermal redox cycles in metal oxides is a promising approach to generating H 2 , but state-of-the-art (SOTA) CeO 2 has several practical limitations, which has motivated continued materials discovery efforts in this field. Here, in this study, we improve upon a SOTA defect graph neural network (dGNN) surrogate model's oxygen vacancy predictions and combine them with materials project phase diagrams to down-select and discover structurally diverse, experimentally known metal oxides whose TCH performance was previously unknown. Amongst twelve candidates selected based on our high-throughput screening and down-selection criteria, we achieved ∼80% accuracy in identifying materials with stable redox cycling and hydrogen production in stagnation flow reactor water-splitting experiments. Closer to 100% accuracy can be achieved if higher-accuracy, hybrid DFT-predicted vacancy formation energies were computed and used in lieu of the most uncertain dGNN-based screening predictions, as they correct false positives to true negatives. Notably, two discovered candidates, Sr 3 PrMn 2 O 8 and Ba 2 Fe 2 O 5 , display hydrogen yields greater than CeO 2 under specific redox conditions. In conclusion, these results demonstrate our ability to computationally predict and experimentally validate promising candidate TCH materials that have the potential to compete with CeO 2 .

08 HYDROGEN↗
DOE OSTI2023

Discovering a reaction–diffusion model for Alzheimer’s disease by combining PINNs with symbolic regression

Misfolded tau proteins play a critical role in the progression and pathology of Alzheimer's disease. Recent studies suggest that the spatio-temporal pattern of misfolded tau follows a reaction-diffusion type equation. However, the precise mathematical model and parameters that characterize the progression of misfolded protein across the brain remain incompletely understood. Here, we use deep learning and artificial intelligence to discover a mathematical model for the progression of Alzheimer's disease using longitudinal tau positron emission tomography from the Alzheimer's Disease Neuroimaging Initiative database. Specifically, we integrate physics informed neural networks (PINNs) and symbolic regression to discover a reaction-diffusion type partial differential equation for tau protein misfolding and spreading. First, we demonstrate the potential of our model and parameter discovery on synthetic data. Then, we apply our method to discover the best model and parameters to explain tau imaging data from 46 individuals who are likely to develop Alzheimer's disease and 30 healthy controls. Our symbolic regression discovers different misfolding models f(c) for two groups, with a faster misfolding for the Alzheimer's group, f(c) = 0.23c 3 – 1.34c 2 + 1.11c, than for the healthy control group, f(c) = –c 3 + 0.62c 2 + 0.39c. Our results suggest that PINNs, supplemented by symbolic regression, can discover a reaction-diffusion type model to explain misfolded tau protein concentrations in Alzheimer's disease. Furthermore, we expect our study to be the starting point for a more holistic analysis to provide image-based technologies for early diagnosis, and ideally early treatment of neurodegeneration in Alzheimer's disease and possibly other misfolding-protein based neurodegenerative disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems

Abstract A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many nonlinear spatiotemporal systems to account for losses due to reduced order representations, including many transport phenomena in fluids. Previous data-driven closure modeling efforts have mostly focused on supervised learning approaches using high fidelity simulation data. On the other hand, reinforcement learning (RL) is a powerful yet relatively uncharted method in spatiotemporally extended systems. In this study, we put forth a modular dynamic closure modeling and discovery framework to stabilize the Galerkin projection based reduced order models that may arise in many nonlinear spatiotemporal dynamical systems with quadratic nonlinearity. However, a key element in creating a robust RL agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the RL model and high fidelity simulation data. First, we introduce a multi-modal RL to discover mode-dependant closure policies that utilize the high fidelity data in rewarding our RL agent. We then formulate a variational multiscale RL (VMRL) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. Specifically, our chief innovation is to leverage variational multiscale formalism to quantify the difference between modal interactions in Galerkin systems. Our results in simulating the viscous Burgers equation indicate that the proposed VMRL method leads to robust and accurate closure parameterizations, and it may potentially be used to discover scale-aware closure models for complex dynamical systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Compositionally complex perovskite oxides: Discovering a new class of solid electrolytes with interface-enabled conductivity improvements

Compositionally complex ceramics (CCCs), including high-entropy ceramics, offer a vast, unexplored compositional space for materials discovery. Herein, we propose and demonstrate strategies for tailoring CCCs via a combination of non-equimolar compositional designs and control of grain boundaries (GBs) and microstructures. Using oxide solid electrolytes for all-solid-state batteries as an example, we have discovered a class of compositionally complex perovskite oxides (CCPOs) with improved lithium ionic conductivities beyond the limit of conventional doping. For example, we demonstrate that the ionic conductivity can be improved by >60% in (Li 0.375 Sr 0.4375 )(Ta 0.375 Nb 0.375 Zr 0.125 Hf 0.125 )O 3-δ compared with the (Li 0.375 Sr 0.4375 )(Ta 0.75 Zr 0.25 )O 3-δ (LSTZ) baseline. Furthermore, the ionic conductivity can be improved by another >70% via quenching, achieving >270% of the LSTZ. Notably, we demonstrate GB-enabled conductivity improvements via both promoting grain growth and altering GB structures through compositional designs and processing. In a broader perspective, this work suggests new routes for discovering and tailoring CCCs for energy storage and many other applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Discovering methylated DNA motifs in bacterial nanopore sequencing data with MIJAMP

Abstract Bacterial DNA methylation is involved in diverse cellular functions, including modulation of gene expression, DNA repair, and restriction–modification systems for defense against viruses and other foreign DNA. Restriction systems hinder efforts to engineer organisms to produce fuels and chemicals from waste and renewable feedstocks by degrading DNA during transformation. Methylome analysis allows identification of motifs within a bacterial chromosome that may be targeted by native restriction enzymes. Further expression of the corresponding methyltransferases in Escherichia coli allows plasmid DNA to be protected from restriction in the target organism, thereby drastically enhancing transformation efficiency. Nanopore sequencing can detect methylated bases, but software is needed to transform modified base coordinates into methylated motifs. Here, we develop MIJAMP (MIJAMP Is Just A MethylBED Parser), a software package that was developed to discover methylated motifs from the output of ONT’s Modkit or other data in the methylBED format. MIJAMP employs a human-driven refinement strategy that empirically validates all motifs against genome-wide methylation data, thus eliminating incorrect motifs. MIJAMP also reports methylation data on specific, user-defined motifs. Using MIJAMP, we determined the methylated motifs both in a control strain (wild-type E. coli) and in Synecococcus sp. strain PCC7002, laying the foundation for improved transformation in this organism. MIJAMP is available at https://code.ornl.gov/alexander-public/mijamp/. One Sentence Summary: Here we describe software written to discover DNA methylation motifs from nanopore sequencing data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The kinematics, metallicities, and orbits of six recently discovered Galactic star clusters with Magellan/M2FS spectroscopy

ABSTRACT We present Magellan/M2FS spectroscopy of four recently discovered Milky Way star clusters (Gran 3/Patchick 125, Gran 4, Garro 01, and LP 866) and two newly discovered open clusters (Gaia 9 and Gaia 10) at low Galactic latitudes. We measure line-of-sight velocities and stellar parameters ([Fe/H], log g, Teff, and [Mg/Fe]) from high-resolution spectroscopy centred on the Mg triplet and identify 20–80 members per star cluster. We determine the kinematics and chemical properties of each cluster and measure the systemic proper motion and orbital properties by utilizing Gaia astrometry. We find Gran 3 to be an old, metal-poor (mean metallicity of [Fe/H] = −1.83) globular cluster located in the Galactic bulge on a retrograde orbit. Gran 4 is an old, metal-poor ([Fe/H] = −1.84) globular cluster with a halo-like orbit that happens to be passing through the Galactic plane. The orbital properties of Gran 4 are consistent with the proposed LMS-1/Wukong and/or Helmi streams merger events. Garro 01 is metal-rich ([Fe/H] = −0.30) and on a near-circular orbit in the outer disc but its classification as an open cluster or globular cluster is ambiguous. Gaia 9 and Gaia 10 are among the most distant known open clusters at $R_{\mathrm{GC}}\sim 18,~21.2~\mathrm{\, kpc}$ and most metal-poor with [Fe/H] ∼−0.50, −0.34 for Gaia 9 and Gaia 10, respectively. LP 866 is a nearby, metal-rich open cluster ([Fe/H] = +0.10). The discovery and confirmation of multiple star clusters in the Galactic plane shows the power of Gaia astrometry and the star cluster census remains incomplete.

Pace, Andrew B. (ORCID:0000000260218760)↗
DOE OSTI2025

Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble

In this work we explore the capability of physics-informed neural networks (PINNs) to discover multiple solutions. Many real-world phenomena governed by nonlinear differential equations (DEs), such as fluid flow, exhibit multiple solutions under the same conditions, yet capturing this solution multiplicity remains a significant challenge. A key difficulty lies in providing appropriate initial conditions or guesses, as widely used time-marching schemes and Newton’s method are highly sensitive to these choices when solving complex computational problems. While machine learning models, particularly PINNs, have shown promise in solving DEs, their ability to capture multiple solutions remains underexplored. In this work, we propose a simple and practical approach using PINNs to learn and discover multiple solutions. We first demonstrate that PINNs, when combined with random initialization and deep ensemble method—originally developed for uncertainty quantification—can effectively uncover multiple solutions to nonlinear ordinary and partial DEs. Although training large ensembles of PINNs may appear computationally demanding, this can be done efficiently using vectorization techniques supported by modern deep learning frameworks, allowing many networks to be trained simultaneously. Our approach highlights the critical role of initialization in shaping solution diversity, addressing an often-overlooked aspect of machine learning for scientific computing. Furthermore, we propose utilizing PINN-generated solutions as initial conditions or initial guesses for conventional numerical solvers to enhance accuracy and efficiency in capturing multiple solutions. Extensive numerical experiments, including the Allen–Cahn equation and cavity flow, where our approach successfully identifies both stable and unstable solutions, validate the effectiveness of our method. These findings establish a general and efficient framework for addressing solution multiplicity in nonlinear DEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Tools for probing new physics with newly discovered gamma-ray targets

Here, we present a computational tool, TweedleDEE, for empirically modeling diffuse gamma-ray background emission in a 1° region of the sky, using publicly available gamma-ray data off-axis from the region of interest. This background model allows a user to perform a purely data-driven search for anomalous localized sources of gamma-ray emission, including new physics. A major application of this tool would be in searching for dark matter annihilation in newly discovered astrophysical targets. For this purpose, we derive a scaling relation for determining velocity-dependent 𝐽-factors using only the stellar parameters, which can be broadly applied to obtain dark matter constraints from new targets. As an application of these tools, we use TweedleDEE and MADHATv2 to perform the first search for dark matter annihilation in the newly discovered Leo VI dwarf spheroidal galaxy, and present model constraints for a variety of choices of the annihilation channel and velocity dependence of the cross section.

dark matter↗
DOE OSTI2023

Deep reinforcement learning to discover multi-fuel injection strategies for compression ignition engines

Over the past several decades, regulation of compression ignition engine emissions has become increasingly stringent as concern about the environmental and health implications of these emissions has grown. These changing constraints have led to a series of new, alternative fuel injection strategies that aim to maintain power output while reducing in-cylinder generated emissions by operating in the low-temperature combustion (LTC) regime. These advanced injection strategies are created and retuned for individual combinations of engine geometry, fuel, and emissions constraints. Deep reinforcement learning has been shown to be an effective alternative to traditional optimization approaches for highly combinatorial control problems, such as discovering the optimal injection schedules for compression ignition engines. In this study, we deploy a previously presented deep reinforcement learning framework to iteratively optimize a series of engine geometries over a range of increasingly strict NO x emissions regulations. We then examine the resulting injection schedules. We discuss the potential for using this deep reinforcement learning framework for fuel selection screening and for discovering unique injection strategies for different engine geometries and future emissions standards.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Six More Ultra-faint Milky Way Companions Discovered in the DECam Local Volume Exploration Survey

We report the discovery of six ultra-faint Milky Way satellites identified through matched-filter searches conducted using Dark Energy Camera (DECam) data processed as part of the second data release of the DECam Local Volume Exploration (DELVE) survey. Leveraging deep Gemini/GMOS-N imaging (for four candidates) as well as follow-up DECam imaging (for two candidates), we characterize the morphologies and stellar populations of these systems. We find that these candidates all share faint absolute magnitudes (M$_{V}$ ≥ −3.2 mag) and old, metal-poor stellar populations (τ > 10 Gyr, [Fe/H] < −1.4 dex). Three of these systems are more extended (r$_{1/2}$ > 15 pc), while the other three are compact (r$_{1/2}$ < 10 pc). From these properties, we infer that the former three systems (Boötes V, Leo Minor I, and Virgo II) are consistent with ultra-faint dwarf galaxy classifications, whereas the latter three (DELVE 3, DELVE 4, and DELVE 5) are likely ultra-faint star clusters. Using data from the Gaia satellite, we confidently measure the proper motion of Boötes V, Leo Minor I, and DELVE 4, and tentatively detect a proper-motion signal from DELVE 3 and DELVE 5; no signal is detected for Virgo II. We use these measurements to explore possible associations between the newly discovered systems and the Sagittarius dwarf spheroidal, the Magellanic Clouds, and the Vast Polar Structure, finding several plausible associations. Our results offer a preview of the numerous ultra-faint stellar systems that will soon be discovered by the Vera C. Rubin Observatory and highlight the challenges of classifying the faintest stellar systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps

We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learning (ML) architecture and adopt interactive machine learning to construct a training sample with multiple classes to address common types of false positives. Our ML model reduces ∼236 million DES cutout images to 22,564 targets of interest, including ∼85% of previously reported galaxy–galaxy lens candidates discovered in DES. These targets were visually inspected by citizen scientists, who ruled out ∼90% as false positives. Of the remaining 2618 candidates, 149 were expert-classified as “definite” lenses and 516 as “probable” lenses, for a total of 665 systems, with 147 of these candidates being newly identified. Additionally, we trained a second ViT to find double-source plane lens systems, finding at least one double-source system. Our main ViT excels at identifying galaxy–galaxy lenses, consistently assigning high scores to candidates with high expert assessments. The top 800 ViT-scored images include ∼100 of our “definite” lens candidates. This selection is an order of magnitude higher in purity than previous convolutional neural-network-based lens searches and demonstrates the feasibility of applying our methodology for discovering large samples of lenses in future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Discovering Ni/Cu Single-Atom Alloy as a Highly Active and Selective Catalyst for Direct Methane Conversion to Ethylene: A First-Principles Kinetic Study

Direct methane conversion to liquid fuels or value-added chemicals is a promising technology to utilize natural resources without resorting to further petroleum extraction. However, discovering efficient catalysts for this reaction is challenging due to either coke formation or unfavorable C-H bond activation. Herein, we design single-atom alloy (SAA) catalysts to simultaneously eliminate the above two bottlenecks based on mechanism-guided strategies: (1) the active single atom enables favorable C-H bond breaking and (2) the less reactive host metal facilitates C-C coupling and thus avoids strong binding of carbonaceous species. Employing electronic structure theory calculations, we screened the stability of multiple SAAs with 3d-5d transition metals atomically dispersed on a copper surface in terms of avoiding dopant aggregation and segregation. We then evaluated reactivities of the stable SAAs as catalysts for direct methane conversion to C2 products, including methane dehydrogenation and C-C coupling mechanisms. Combining selectivity analysis with kinetic modeling, we predicted that nickel dispersed on copper, i.e., Ni/Cu SAA, is a highly active and selective catalyst that can efficiently transform methane to ethylene. This work designs efficient SAA catalysts for direct methane activation and provides chemical insights into engineering compositions of SAAs to tune their catalytic performances.

Kothakonda, Manish↗
DOE OSTI2025

Predicted Ferromagnetism in Discovered Co–Bi Binary Phases

Binary solid-state materials offer unique insight into how the interplay of factors such as stoichiometry and bonding interactions affects magnetism and electronic properties. In this work, we considered systems where a transition metal provides the spin moment and a heavy main group element bolsters strong spin–orbit coupling. Within this context, cobalt, a known component of permanent magnets, and bismuth, functionally the heaviest element stable to radioactive decay, form a compelling combination. The Co–Bi system has been previously shown to exhibit superconductivity in a phase recovered from high pressure. We expected the Co−Bi system could also be ferromagnetic, resulting in two sets of compounds within one chemical system, one superconducting and one ferromagnetic. Subsequently, we investigated the Co–Bi system through both experimental and theoretical approaches to discover new candidates for permanent magnets. Ab initio random structure searching calculations identified five new compounds with diverse structural motifs that may form at higher pressures than previously reported. Experimental high-pressure synthesis yielded four compounds: α-CoBi, α-CoBi 2 , β-CoBi, and β-CoBi 2 . Three of these phases, α-CoBi 2 , β-CoBi, and β-CoBi 2 , were consistent with the calculated structures, corresponding to a 60% success rate for our structure search and underscoring the strength of combining computation with experiment. Theory predicts β-CoBi and β-CoBi2 are ferromagnetic, with β-CoBi possessing larger magnetocrystalline anisotropy energy than familiar permanent magnets such as CoPt and Nd–Fe–B. These results suggest the Co–Bi system could be a platform for understanding the factors that underpin magnetism and, to an extent, superconductivity in a chemically simple binary system.

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

Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS

Type 1 polyketides are a major class of natural products used as antiviral, antibiotic, antifungal, antiparasitic, immunosuppressive, and antitumor drugs. Analysis of public microbial genomes leads to the discovery of over sixty thousand type 1 polyketide gene clusters. However, the molecular products of only about a hundred of these clusters are characterized, leaving most metabolites unknown. Characterizing polyketides relies on bioactivity-guided purification, which is expensive and time-consuming. To address this, we present Seq2PKS, a machine learning algorithm that predicts chemical structures derived from Type 1 polyketide synthases. Seq2PKS predicts numerous putative structures for each gene cluster to enhance accuracy. The correct structure is identified using a variable mass spectral database search. Benchmarks show that Seq2PKS outperforms existing methods. Applying Seq2PKS to Actinobacteria datasets, we discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamide-actiphenol.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Bayesian blacksmithing: discovering thermomechanical properties and deformation mechanisms in high-entropy refractory alloys

Finding alloys with specific design properties is challenging due to the large number of possible compositions and the complex interactions between elements. This study introduces a multi-objective Bayesian optimization approach guiding molecular dynamics simulations for discovering high-performance refractory alloys with both targeted intrinsic static thermomechanical properties and also deformation mechanisms occurring during dynamic loading. The objective functions are aiming for excellent thermomechanical stability via a high bulk modulus, a low thermal expansion, a high heat capacity, and for a resilient deformation mechanism maximizing the retention of the BCC phase after shock loading. Contrasting two optimization procedures, we show that the Pareto-optimal solutions are confined to a small performance space when the property objectives display a cooperative relationship. Conversely, the Pareto front is much broader in the performance space when these properties have antagonistic relationships. Density functional theory simulations validate these findings and unveil underlying atomic-bond changes driving property improvements.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Exact Gaussian processes for massive datasets via non-stationary sparsity-discovering kernels

Abstract A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. Its success is largely attributed to the GP’s analytical tractability, robustness, and natural inclusion of uncertainty quantification. Unfortunately, the use of exact GPs is prohibitively expensive for large datasets due to their unfavorable numerical complexity of $$O(N^3)$$ O ( N 3 ) in computation and $$O(N^2)$$ O ( N 2 ) in storage. All existing methods addressing this issue utilize some form of approximation—usually considering subsets of the full dataset or finding representative pseudo-points that render the covariance matrix well-structured and sparse. These approximate methods can lead to inaccuracies in function approximations and often limit the user’s flexibility in designing expressive kernels. Instead of inducing sparsity via data-point geometry and structure, we propose to take advantage of naturally-occurring sparsity by allowing the kernel to discover—instead of induce—sparse structure. The premise of this paper is that the data sets and physical processes modeled by GPs often exhibit natural or implicit sparsities, but commonly-used kernels do not allow us to exploit such sparsity. The core concept of exact, and at the same time sparse GPs relies on kernel definitions that provide enough flexibility to learn and encode not only non-zero but also zero covariances. This principle of ultra-flexible, compactly-supported, and non-stationary kernels, combined with HPC and constrained optimization, lets us scale exact GPs well beyond 5 million data points.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Discovering nuclear models from symbolic machine learning

Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. To address this challenge, we developed a Multi-objective Iterated Symbolic Regression approach that handles symbolic regressions over multiple target observables, accounts for experimental uncertainties and is robust against high-dimensional problems. As a proof of principle, we applied this method to describe the nuclear binding energies and charge radii of light and medium mass nuclei. Our approach identified simple analytical relationships based on the number of protons and neutrons, providing interpretable models with precision comparable to state-of-the-art nuclear models. Additionally, we integrated this ML-discovered model with an existing complementary model to estimate the limits of nuclear stability. These results highlight the potential of symbolic ML to develop accurate nuclear models and guide our description of complex many-body problems.

Nuclear structure↗
DOE OSTI2022

Newly discovered Ca ii absorbers in the early Universe: statistics, element abundances, and dust

ABSTRACT We report discoveries of 165 new quasar Ca ii absorbers from the Sloan Digital Sky Survey (SDSS) Data Releases 7 and 12. Our ca ii rest-frame equivalent width distribution supports the weak and strong subpopulations, split at ${W}^{\lambda 3934}_{0}=0.7$ Å. Comparison of both populations’ dust depletion shows clear consistency for weak absorber association with halo-type gas in the Milky Way (MW), while strong absorbers have environments consistent with halo and disc-type gas. We probed our high-redshift Ca ii absorbers for 2175 Å dust bumps, discovering 12 2175 Å dust absorbers (2DAs). This clearly shows that some Ca ii absorbers follow the Large Magellanic Cloud (LMC) extinction law rather than the Small Magellanic Cloud extinction law. About 33 per cent of our strong Ca ii absorbers exhibit the 2175 Å dust bump, while only 6 per cent of weak Ca ii absorbers show this bump. 2DA detection further supports the theory that strong Ca ii absorbers are associated with disc components and are dustier than the weak population. Comparing average Ca ii absorber dust depletion patterns to that of Damped Ly α absorbers (DLAs), Mg ii absorbers, and 2DAs shows that Ca ii absorbers generally have environments with more dust than DLAs and Mg ii absorbers, but less dust than 2DAs. Comparing 2175 Å dust bump strengths from different samples and also the MW and LMC, the bump strength appears to grow stronger as the redshift decreases, indicating dust growth and the global chemical enrichment of galaxies in the Universe over time.

Astronomy & Astrophysics↗