Search NASA⌕ Search

SEARCH · Search NASA

Results for “pattern dependence”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Architecture-Dependent Thin Film Self-Assembly of Star Polystyrene-poly(2-vinylpyridine) Block Copolymers

Controlling the orientation of nanostructured block copolymer (BCP) thin films is essential for their use in templating, transport, and pattern transfer. Conventional efforts mainly focus on adjusting enthalpic interactions between the blocks and interfaces, while entropic contributions are often overlooked. Here, we show that the morphology of BCP thin films can be precisely tuned by the architectural design of star BCPs. Specifically, we synthesized multiarm star BCPs with a polystyrene (PS) core and poly(2-vinylpyridine) (P2VP) corona, which exhibits a lamellar microdomain morphology. The entropic penalty associated with a parallel orientation of the microdomains to the substrate is controlled by varying the number of arms comprising the star BCPs, from 2-arms (triblock) to 3-arms and 4-arms. We systematically investigated the thin film morphology at different depths using grazing incidence small-angle X-ray and neutron scattering (GISAXS and GISANS), atomic force microscopy (AFM), water contact angle (WCA), and interference microscopy. The results show that 2-arm star BCPs show a parallel orientation, the 3-arm star BCPs form a uniform PS film at the air surface with a vertical orientation of the microdomains underneath, and the 4-arm star BCPs exhibit a parallel microdomain orientation at the air surface with mixed parallel and perpendicular microdomain orientation in the bulk. Additionally, we found that the inclination angle of microdomains at the edges of islands and holes, resulting from the incommensurability between film thickness and the characteristic period of the microdomain morphology, increases with a higher number of arms. This suggests a greater grain boundary tilt angle in the microdomains of star-shaped block copolymers (BCPs). When silicon substrates were modified with PS homopolymer, the selective interaction between substrate and core blocks promotes a parallel orientation for the 4-arm star BCPs. In conclusion, this work shows that control of arm number in star BCPs affords diverse BCP thin film morphologies, offering insights into the star BCP conformations in thin films across different depths.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effect of pattern transfer process on roughness of block copolymer patterns from directed self-assembly

Block copolymer-directed self-assembly (DSA) remains promising for improving pattern quality and reducing the stochastic variations that challenge high numerical aperture extreme ultraviolet lithography. Equally critical is refining pattern transfer methods for accurately transferring the rectified DSA features to the underlying substrate. We compare two atomic layer deposition (ALD)-based techniques: sequential infiltration synthesis (SIS) and dry liftoff, applied to polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA) DSA patterns. Both methods utilize aluminum oxide hard masks, with one synthesized through infiltration into the PMMA domains and the other through conformal ALD coating. High-resolution scanning electron micrographs were analyzed to measure line edge, width, and placement roughness for both the line (PMMA) and space (PS) features. Although both methods yielded similar overall 3σ rms roughness, they differed significantly in the frequency-dependent power spectral density (PSD) profiles. SIS reduced line placement roughness at length scales associated with the polymer pitch, but increased space width roughness at low frequencies, whereas dry liftoff mimicked the frequency content of the original guiding pattern. This study underscores the importance of PSD evaluation in selecting optimal pattern transfer strategies for specific applications.

Block copolymers↗
DOE OSTI2024

Quantitative x-ray scattering of free molecules

Advances in x-ray free electron lasers have made ultrafast scattering a powerful method for investigating molecular reaction kinetics and dynamics. Accurate measurement of the ground-state, static scattering signals of the reacting molecules is pivotal for these pump-probe x-ray scattering experiments as they are the cornerstone for interpreting the observed structural dynamics. Here, this article presents a data calibration procedure, designed for gas-phase x-ray scattering experiments conducted at the Linac Coherent Light Source x-ray Free-Electron Laser at SLAC National Accelerator Laboratory, that makes it possible to derive a quantitative dependence of the scattering signal on the scattering vector. A self-calibration algorithm that optimizes the detector position without reference to a computed pattern is introduced. Angle-of-scattering corrections that account for several small experimental non-idealities are reported. Their implementation leads to near quantitative agreement with theoretical scattering patterns calculated with ab-initio methods as illustrated for two x-ray photon energies and several molecular test systems.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)

Anthropogenic climate change is unfolding rapidly, yet its regional manifestation can be obscured by internal variability. A primary goal of climate science is to identify the externally forced climate response from among the noise of internal variability. Separating the forced response from internal variability can be addressed in climate models by using a large ensemble to average over different possible realizations of internal variability. However, with only one realization of the real world, it is a major challenge to isolate the forced response directly in observations. In the Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP), contributors used existing and newly developed statistical and machine learning methods to estimate the forced response over 1950–2022 within individual realizations of the climate system. Participants used neural networks, linear inverse models, fingerprinting methods, and low-frequency component analysis, among other approaches. These methods were trained using large ensembles from multiple climate models and then applied to observations. Here, we evaluate method performance within large ensembles and investigate the estimates of the forced response in observations. Our results show that many different types of methods are skillful for estimating the forced response in climate models, though the relative skill of individual methods varies depending on the variable and evaluation metric. Methods with comparable skill in models can give a wide range of estimates of the forced response pattern in observations, illustrating the epistemic uncertainty in forced response estimates. ForceSMIP gives new insights into the forced response in observations, its uncertainty, and methods for its estimation.

Climate attribution↗
DOE OSTI2024

Multiscale Interactions between Local Short- and Long-Term Spatio-Temporal Mechanisms and Their Impact on California Wildfire Dynamics

California has experienced a surge in wildfires, prompting research into contributing factors, including weather and climate conditions. This study investigates the complex, multiscale interactions between large-scale climate patterns, such as the Boreal Summer Intraseasonal Oscillation (BSISO), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) and their influence on moisture and temperature fluctuations, and wildfire dynamics in California. The combined impacts of PDO and BSISO on intraseasonal fire weather changes; the interplay between fire weather index (FWI), relative humidity, vapor pressure deficit (VPD), and temperature in assessing wildfire risks; and geographical variations in the relationship between the FWI and climatic factors within California are examined. The study employs a multi-pronged approach, analyzing wildfire frequency and burned areas alongside climate patterns and atmospheric conditions. The findings reveal significant variability in wildfire activity across different climate conditions, with heightened risks during specific BSISO phases, La-Niña, and cool PDO. The influence of BSISO varies depending on its interaction with PDO. Temperature, relative humidity, and VPD show strong predictive significance for wildfire risks, with significant relationships between FWI and temperature in elevated regions (correlation, r > 0.7, p ≤ 0.05) and FWI and relative humidity along the Sierra Nevada Mountains (r ≤ -0.7, p ≤ 0.05).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Ectopic expression of pectate lyase PtxtPL1-27 in aspen affects leaf cuticle development

Cuticle - a hydrophobic barrier of cutin and waxes covering the outer cell wall surface of plants - enables survival in terrestrial habitats. However, it is not understood how the hydrophobic cuticle precursors travel through the homogalacturonan-rich hydrophilic cell wall. To elucidate the role of homogalacturonan in cuticle development, we disrupted its integrity by overexpressing a pectate lyase, PtxtPL1-27, in aspen. PtxtPL1-27 had pleiotropic effects on shoot development, including the reduction of cuticle thickness and changes in cutin and wax composition, but the expression of cutin biosynthetic genes was little affected. Despite a reduction in homogalacturonan content in the leaves, labeling with the homogalacturonan-specific antibody JIM5 in the outer epidermal cell wall layer increased and displayed an altered pattern. Moreover, the ultrastructure of cell walls was changed concomitant with lipid accumulation. We propose that the disruption of homogalacturonan integrity affected the cutinsome-dependent transport and polymerization of cutin monomers in the cell wall.

Plant Biology↗
DOE OSTI2025

Shape Anisotropy-Dependent Leaking in Magnetic Neurons for Bio-Mimetic Neuromorphic Computing

Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magnetic domain wall (DW) propagation in a patterned nanotrack and firing tied to the resistance change of a magnetic tunnel junction (MTJ), captured in the domain wall-magnetic tunnel junction (DW-MTJ) device. Leaking, relaxation of a neuron when it is not under stimulation, is also predicted to be implemented based on DW drift as a DW relaxes to a low energy position, but it has not been well explored or demonstrated in device prototypes. Here, in this work, we study DW-MTJ artificial neurons capable of leaky integrate-and-fire (LIF) behavior and demonstrate geometry-dependent leaking dynamics that results in repeatable, tunable LIF operation. Studying the behavior of five different device designs, we show tuning the geometry, stimulating fields and currents, and location of electrical contacts results in a wide range of neuron behavior. Additionally, implementation of an asymmetric notch allows for nonlinear pinning which increased expressivity without sacrificing leaking. The measured behavior is implemented in a simulated spiking neural network that outperforms a 1D model of continuous DW motion and approaches the performance of an ideal LIF activation function. The results show that the analog LIF capability of DW-MTJ neurons combines many desirable neuron functions into a single device, which can result in varied forms of multifunctional neuromorphic computing.

42 ENGINEERING↗
DOE OSTI2026

A Unified Theory for the Global Thunderstorm Distribution and Land–Sea Contrast

This article evaluates Entraining CAPE (ECAPE) as a thunderstorm proxy in climate studies using Global Precipitation Measurement satellite observations. ECAPE modifies traditional CAPE to account for the dependence of entrainment on the vertical wind shear, the lifted condensation level (LCL) height, and the properties of a cloud's surrounding atmosphere. ECAPE shows stronger pattern correlations with global regions of intense thunderstorms than previous metrics for updraft speed. In these regions, large CAPE, large shear, and high LCLs conspire to produce wide updrafts that are shielded from the negative effects of dry-air entrainment. ECAPE more skillfully discriminates intense thunderstorms from their less intense counterparts than other metrics commonly used in climatology and climate change studies of thunderstorms. We provide evidence that the well-known land-sea contrast in thunderstorm intensity is a consequence of larger CAPE and higher LCL heights over land than over the ocean.

entraining CAPE↗
DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Determining Key Factors for the Open-Loop Control of Molecular Fragmentation Using Shaped Strong Fields

Pulse shaping has long been employed for tailoring femtosecond laser pulses to study and control the fragmentation of polyatomic molecules. In many cases, a physical explanation connecting the properties of the field to the observed control is difficult to ascertain. We utilized 80 bit binary spectral phase functions to parametrize and map the search space, gaining insight into which pulse parameters most impact the ion yield and fragmentation pattern for the relatively large triethylamine [N(C 2 H 5 ) 3 ] molecule. Pulse structures used to control the m/z 86 branching ratio beyond a simple intensity dependence are identified and compared to pump–probe results. All of these findings are explained in terms of control via a dissociative Rydberg state in the neutral molecule. This methodology may be used to discover new control mechanisms and shed light onto which pulse parameters most influence the interaction between strong field lasers and matter.

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

Scalable Surface Micro-Texturing of LLZO Solid Electrolytes for Battery Applications

A challenge for lithium lanthanum zirconate (LLZO)-based solid-state batteries is to increase the critical current density (CCD) to enable high current cycling. A promising strategy is to modify the LLZO surface morphology to provide a larger contact area with the Li metal. Here, a surface-textured thin LLZO electrolyte was prepared through an easily scalable process. The texturing process is a simple pressing of green LLZO tapes between micro-textured substrates. A variety of textures can be produced, depending on the type of substrate, and texturing can be on either one side or both sides. For this work, after pressing and sintering, several micro-patterns are formed on thin LLZO (~118 μm thick). The properties of the various samples were characterized to investigate the impact of surface texturing, and the most promising ones were selected for electrochemical testing in symmetrical lithium cells and full cells. Li symmetric cells using a coarse ridge-textured LLZO exhibit ~2.5 times increased CCD compared to planar non-textured LLZO, and a solid-state full cell shows stable cycling and improved rate performance. Finally, we believe this process offers a favorable trade-off of processing complexity vs structural optimization to maximize CCD.

25 ENERGY STORAGE↗
DOE OSTI2025

Regional and Temporal Variability of Atmospheric River Seasonality: Influences of Detection Algorithms and Moisture Transport Dynamics

Abstract Understanding the regional and temporal variability of atmospheric river (AR) seasonality is crucial for preparedness and mitigation of extreme events. While ARs were thought to peak in winter, recent research shows they exhibit region‐specific seasonality and are heavily influenced by the chosen detection algorithm. This study examines the link between the year‐to‐year consistency of peak‐AR activity to the presence of a dominant seasonal pattern, considering both location and algorithm choice. Regions are categorized by their temporal characteristics: consistent patterns (e.g., East Asia), patterns with occasional outliers (e.g., British Columbia coast), and regions lacking a clear dominant peak season (e.g., South Atlantic, parts of Australia). Hence, not all regions display a consistent seasonal cycle of AR activity. This study quantifies the extent to which a region experiences a dominant peak season of AR activity (or lacks one) and offers insights to enhance decision‐making in water management, natural hazard preparedness, and forecasting. Furthermore, given our finding that detection algorithms influence the peak season of AR activity, we also examine two diagnostic variables representative of moisture transport to corroborate our results. Integrated vapor transport, which captures meridional and zonal moisture transport, and Moist Wave Activity, representing moisture intrusions from lower to higher latitudes, are examined. Our analysis indicates that inconsistencies in the seasonal cycle of AR activity are not solely due to discrepancies in detection algorithms but also arise from changes in moisture transport. Plain Language Summary Atmospheric rivers (ARs) are critical weather phenomena that can cause extreme events like heavy rainfall and flooding. Understanding when and where ARs are most likely to occur throughout the year is essential for preparing and responding to these events. Traditionally, ARs were thought to peak in winter, but recent studies show this varies by region. Our study helps address the challenge decision‐makers face in anticipating and preparing for AR events by providing insights into the consistency of peak seasonal patterns across different areas. Some regions, like East Asia, and the British Columbia coast, show a consistent peak season, while others, like the South Atlantic and parts of Australia, have significant year‐to‐year variations, making it hard to identify a dominant season. To better understand these changes over time, the study also examines how moisture moves in the atmosphere, using Integrated Vapor Transport (which looks at moisture movement in various directions) and Moist Wave Activity (which tracks moisture shifts from lower to higher latitudes). The findings suggest that inconsistencies in AR patterns are due not only to detection methods but also due to changes in moisture transport. Key Points The peak season of atmospheric river activity can change depending on the year in some areas Interannual variations in the peak season can make identifying a dominant season challenging for some regions Frequent shifts in peak season across years reflect inconsistencies tied to detection algorithms and to underlying dynamics

Kamnani, Diya↗
DOE OSTI2026

Open system approach to neutrinos propagating in an ultralight scalar background

We examine decoherence in neutrino oscillations induced by an ultralight scalar field coupled to neutrinos. The scalar induces time- and position-dependent shifts in the neutrino mass matrix. Neutrinos sample different field configurations throughout an experimental data-taking period, which leads to damping effects in the oscillation pattern in the form of decoherence. By recasting the neutrino-scalar dynamics within the open quantum systems framework, we establish a mapping between a complete model and phenomenological decoherence approaches. We find that the parameter driving decoherence scales as L 2 / E 2 , where L is the baseline and E is the neutrino energy, as opposed to L / E typically assumed in phenomenological studies of open system approaches to neutrino oscillations.

F.T. Airoldi, Lua [Sao Paulo U.; Fermilab] (ORCID:↗
DOE OSTI2024

Interpreting Transformers for Jet Tagging

Machine learning (ML) algorithms, particularly attention-based transformer models, have become indispensable for analyzing the vast data generated by particle physics experiments like ATLAS and CMS at the CERN LHC. Particle Transformer (ParT), a state-of-the-art model, leverages particle-level attention to improve jet-tagging tasks, which are critical for identifying particles resulting from proton collisions. This study focuses on interpreting ParT by analyzing attention heat maps and particle-pair correlations on the $\eta$-$\phi$ plane, revealing a binary attention pattern where each particle attends to at most one other particle. At the same time, we observe that ParT shows varying focus on important particles and subjets depending on decay, indicating that the model learns traditional jet substructure observables. These insights enhance our understanding of the model's internal workings and learning process, offering potential avenues for improving the efficiency of transformer architectures in future high-energy physics applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Experimental Investigation of Uranium and Iron Condensation from High-Temperature Plasma Conditions

We used a plasma flow reactor (PFR) to generate synthetic fallout nanoparticles from vapor-phase condensation of two different input concentrations of uranium and iron analytes (U/Fe = 1:1 and 1:2). Synthetic fallout from complex chemical matrices (e.g., mixtures of U and Fe) has not been generated in this setup before and allows for the observation of relative condensation and fractionation of nuclear debris. Experiments were conducted under two different temperature histories with variations in particle flow patterns along the PFR. Transmission electron microscopy (TEM) observation and analysis of the nanoparticles revealed variations in speciation of uranium oxides (UO 2 and α-UO 3 ) depending on the competition between flow mixing and oxygen sequestration by iron. A ternary metal oxide, UFeO 4 , was observed in addition to iron oxides (e.g., FeO and Fe 3 O 4 ), which suggests that fallout models should account for chemical speciation of ternary metal oxides (i.e., UFeO 4 ) and their relative condensation behaviors in addition to those of singular metal oxides (e.g., FeO and UO 2 ). X-ray Energy Dispersive Spectroscopy (EDS) elemental maps showed that some particles had Fe-rich cores surrounded by U-rich regions. This suggests that either condensed U oxides coagulate onto molten Fe oxides or that the increase in iron analyte concentration might drive the system toward a higher degree of supersaturation, leading to earlier formation of iron oxide particles and providing an energetically favored pathway for nucleation of uranium oxides around iron oxide particles.

and nuclear chemistry↗
DOE OSTI2026

Relaxor Ferroelectric-Like Spatiotemporal Memory in Field-Driven Lipid Bilayers

Lipid membranes are often regarded as passive barriers, yet their nonlinear dielectric response remains poorly understood. Using all-atom molecular dynamics, we show that fully hydrated dipalmitoylphosphatidylcholine bilayers exhibit relaxor ferroelectric-like behavior under time-dependent electric fields. Unlike crystalline relaxors, which are bipolar and display little remanent polarization, lipid bilayers exhibit a unipolar polarization response: even an alternating current field produces persistent, asymmetric polarization. Furthermore, the underlying free-energy landscape contains two distinct minima, a nonpolarized state and a unipolarly polarized state, between which stochastic thermally activated transitions occur. Directionally resolved Van Hove analysis reveals pronounced anisotropy arising from out-of-plane electric dipole alignment, interleaflet coupling, and lateral polarization domains. Each field cycle nucleates polarization at distinct sites and monitors their relaxation, marking a crossover from thermal fluctuations to field-sustained polarization. Remarkably, these polarized domains persist after field removal, generating long-lived, spatially coherent dipolar patterns that encode nanoscale polarization memory. Potassium chloride amplifies these effects via dielectric screening and a modified hydration structure, enhancing electric dipole flexibility and cooperativity. Together, these results establish protein-free bilayers as nonlinear, history-dependent dielectrics capable of sustaining field-tunable electromechanical coupling, providing an emergent physical foundation for nanoscale information storage and memory phenomena reminiscent of short- and long-term plasticity in soft neuromorphic systems.

Insulators↗
DOE OSTI2026

Governing in Time: Temporal Capacity and the Feasibility of Energy Transitions

Energy systems function as both technological systems and temporal institutions that shape how societies coordinate, justify, and support collective choices over time. This paper introduces the concept of governance horizons to explain why energy transitions can remain morally supported yet become institutionally weak under increasing pressure. We argue that governability depends on institutions' capacity to synchronize across multiple timeframes - aligning short-term decisions with intermediate coordination and long-term commitments. When this synchronization fails, transitions struggle not because their goals are dismissed, but because governance lacks sufficient time to justify, coordinate, and uphold decisions. Comparative analysis of San Antonio, Texas, and Interior Alaska reveals how energy system pressures generate distinct temporal configurations: San Antonio exhibits governance horizon stretching, where institutions must simultaneously meet near-term reliability demands and long-term transformation goals, while Interior Alaska exhibits horizon compression, where extreme environmental constraints force decision-making into short stabilization cycles. In both contexts, public support for sustainability goals coexists with institutional strain because evaluative judgments are unevenly distributed over time. A temporal configuration analysis is introduced as a diagnostic analytic stance for identifying these patterns. By treating temporal alignment as an explanatory variable rather than a background condition, this approach clarifies how feasibility, sequencing, and legitimacy are shaped by constraints on institutional time. The analysis demonstrates that successful energy transitions depend not only on technological innovation or institutional support, but on governance systems’ ability to sustain credible coordination across multiple time horizons.

Comparative case study↗
DOE OSTI2026

Characterizing skyrmion flow phases with principal component analysis

Principal component analysis (PCA) is a powerful method that can identify patterns in large, complex data sets by constructing low-dimensional order parameters from higher-dimensional feature vectors. There are increasing efforts to use space-and-time-dependent PCA to detect transitions in nonequilibrium systems that are difficult to characterize with equilibrium methods. Here, we demonstrate that feature vectors incorporating the position and velocity information of driven skyrmions moving through random disorder permit PCA to resolve different types of disordered skyrmion motion as a function of driving force and the ratio of the Magnus force to the dissipation. Since the Magnus force creates gyroscopic motion and a finite Hall angle, skyrmions can exhibit a greater range of flow phases than what is observed in overdamped driven systems with quenched disorder. We show that in addition to identifying previously known skyrmion flow phases, PCA detects several additional phases, including different types of channel flow, moving fluids, and partially ordered states. Guided by the PCA analysis, we further characterize the disordered flow phases to elucidate the different microscopic dynamics and show that the changes in the PCA-derived order parameters can be connected to features in bulk transport measures, including the transverse and longitudinal velocity-force curves, differential conductivity, topological defect density, and changes in the skyrmion Hall angle as a function of drive. We discuss how asymmetric feature vectors can be used to improve the resolution of the PCA analysis, and how this technique can be extended to find disordered phases in other nonequilibrium systems with time-dependent dynamics.

36 MATERIALS SCIENCE↗