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DOE OSTI2026

Identification of Common Types of Plastics by Vibrational Spectroscopic Techniques

Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polyvinyl Chloride (PVC), Low-Density Polyethylene (LDPE), Polypropylene (PP), and Polystyrene (PS) account for most plastic use worldwide, with production nearing 380 million tons annually. A considerable portion enters municipal solid waste and landfills, creating long-term environmental concerns. Scaling recycling operations requires automated sorting technologies, with spectroscopy and machine learning offering promising solutions. In this study, a six-class convolutional neural network (CNN) was developed for plastic identification using vibrational spectroscopies. Raman Scattering (RS) spectra collected from recycling samples enabled accurate chemical differentiation while assessing the influence of visible features such as color. A CNN trained on RS data achieved 100% classification accuracy. To strengthen field applicability, Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy was incorporated, achieving 95% accuracy with a similar CNN model. These findings demonstrate the potential of integrating spectroscopy with deep learning for reliable plastic classification, advancing development of scalable, field-ready recycling technologies.

Garcia Tovar, Maria P.↗

DOE OSTI2017

Identification and Analysis of Partial Shading Breakdown Sites in CuIn x Ga (1-x) Se 2 Modules

In this paper, CuIn x Ga (1-x) (CIGS) mini-modules are stressed under reverse bias, resembling partial shading conditions, to predict and characterize where failures occur. Partial shading can cause permanent damage in the form of 'wormlike' defects on thin-film modules due to thermal runaway. This results in module-scale power losses. We have used dark lock-in thermography (DLIT) to spatially observe localized heating when reverse-bias breakdown occurs on various CIGS mini-modules. For better understanding of how and where these defects originated and propagated, we have developed techniques where the current is limited during reverse-bias stressing. This allows for DLIT-based detection and detailed studying of the region where breakdown is initiated before thermal runaway leads to permanent damage. Statistics of breakdown sites using current-limited conditions has allowed for reasonable identification of the as-grown defects where permanent breakdown will likely originate. Scanning electron microscope results and wormlike defect analysis show that breakdown originates in defects such as small pits, craters, or cracks in the CIGS layer, and the wormlike defects propagate near the top CIGS interface.

14 SOLAR ENERGY↗

DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗

DOE OSTI2018

Production of bioactive recombinant human Eb-peptide of pro-IGF-I and identification of binding components from the plasma membrane of human breast cancer cells (MDA-MB-231)

Highlights: • A bioactive recombinant E-peptide of human pro-IGF-I (hEb-peptide) with a purity over 95% was prepared. • The recombinant human E-peptide binds reversibly membrane proteins of human breast carcinoma cells, MDA-MB-231. • By employing two dimension polyacrylamide gel electrophoresis and MALDI-MS/MS analysis, we have isolated and identified eight protein molecules bound reversibly with the hEb-peptide from the membrane preparation of MDA-MB-231 cells. E-peptide of the pro-Insulin-like growth factor-I (pro-IGF-I) is produced from pre-pro-IGF-I by proteolytic cleavage in the post-translational processing. The human Eb-peptide (hEb-peptide), derived from the E domain of pro-IGF-IB isoform, is a bioactive molecule whose exact physiological role remains elusive. Accumulated evidence reported from our laboratory indicated that hEb-peptide possesses activity against multiple hallmark characteristics of solid tumor in different cancer cell types. In human breast carcinoma cells (MDA-MB-231), it was demonstrated that hEb-peptide can promote cell attachment to substratum, inhibit colony formation in a semisolid medium, reduce cancer cell invasion, and inhibit cancer-induced angiogenesis. Like the action of other peptide hormones, these cellular responses triggered by hEb may be initiated through binding to a receptor molecule residing on the surface of the cell. Our laboratory and the others have previously provided evidence demonstrating the existence of hEb-peptide specific binding components residing on the cell membrane. In this study, we report the isolation and identification of eight protein molecules bound reversibly with hEb-peptide from the membrane preparation of MDA-MB-231 cells. Some of the identified proteins are known to be present at cell surface and function as receptors while the others are not. The functions of these molecules reveal strong correlation with the demonstrated activities of hEb-peptide on MDA-MB-231cells, suggesting hEb-peptide activity on cancer cells might be mediated by these molecules.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Identification and properties of isolated field elliptical galaxies from CFHTLS-W1

We present a catalogue of isolated field elliptical (IfE) galaxies drawn from the W1 field of the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS). 228 IfEs were identified from a flux-limited (r < 21.8) galaxy catalogue which corresponds to a density of 3 IfE/sq.deg. For comparison we consider a sample of elliptical galaxies living in dense environments, based on identification of the brightest cluster galaxies (BGCs) in the same survey. Using the same dataset for the comparison sample ensures a uniform selection, including in the redshift range as IfEs (i.e. 0.1 < z <0.9). A comparison of elliptical galaxies in different environments reveals that IfEs and BCGs have similar behaviours in their colours, star formation activities, and scaling relations of mass–size and size–luminosity. IfEs and BCGs have similar slopes in the scaling relations with respect to cluster ellipticals within the $ -24 \leq M_{r} \leq -22 $ magnitude and $10.2 < \textrm{log}(M_{*}/ \textrm M_\odot)\leq12.0 $ mass ranges. Three IfEs identified in this study can be associated with fossil groups found in the same survey area which gives clues for future studies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Comprehensive Metabolite Identification Strategy Using Multiple Two-Dimensional NMR Spectra of a Complex Mixture Implemented in the COLMARm Web Server

Identification of metabolites in complex mixtures represents a key step in metabolomics. A new strategy is introduced, which is implemented in a new public web server, COLMARm, that permits the co-analysis of up to three 2D NMR spectra, namely 13C-1H HSQC, 1H-1H TOCSY, and 13C-1H HSQC-TOCSY for the comprehensive, accurate, and efficient performance of this task. The highly versatile and interactive nature of COLMARm permits its application to a wide range of metabolomics samples independent of the magnetic field. Database query is performed using the HSQC spectrum and the top metabolite hits are then validated against the TOCSY-type experiment(s) by superimposing the expected cross-peaks on the mixture spectrum. In this way the user can directly accept or reject candidate metabolites by taking advantage of the complementary spectral information offered by these experiments and their different sensitivities. The power of COLMARm is demonstrated for a human serum sample uncovering the existence of 14 metabolites that hitherto were not identified by NMR.

Bingol, Kerem↗

DOE OSTI2016

Multiplexed Post-Experimental Monoisotopic Mass Refinement ( m PE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation

Mass spectrometry (MS)-based proteomics, which uses high-resolution hybrid mass spectrometers such as the quadrupole-orbitrap mass spectrometer, can yield tens of thousands of tandem mass (MS/MS) spectra of high resolution during a routine bottom-up experiment. Despite being a fundamental and key step in MS-based proteomics, the accurate determination and assignment of precursor monoisotopic masses to the MS/MS spectra remains difficult. The difficulties stem from imperfect isotopic envelopes of precursor ions, inaccurate charge states for precursor ions, and cofragmentation. We describe a composite method of utilizing MS data to assign accurate monoisotopic masses to MS/MS spectra, including those subject to cofragmentation. The method, “multiplexed post-experiment monoisotopic mass refinement” (mPE-MMR), consists of the following: multiplexing of precursor masses to assign multiple monoisotopic masses of cofragmented peptides to the corresponding multiplexed MS/MS spectra, multiplexing of charge states to assign correct charges to the precursor ions of MS/ MS spectra with no charge information, and mass correction for inaccurate monoisotopic peak picking. When combined with MS-GF+, a database search algorithm based on fragment mass difference, mPE-MMR effectively increases both sensitivity and accuracy in peptide identification from complex high-throughput proteomics data compared to conventional methods.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2019

Probabilistic limit of detection for ricin identification using a shotgun proteomics assay

Robust and highly specific methods for the detection of the protein toxin ricin are of interest to the law enforcement community. In previous studies, methods based on liquid chromatography-tandem mass spectrometry shotgun proteomics have been proposed. The successful implementation of this approach relies on specific data evaluation criteria addressing (1) the quality of the mass spectrometric data, (2) the confidence of peptide identifications (peptide-spectrum matches), and (3) the number and sequence specificity of peptides detected. We present such data evaluation criteria, and use a novel approach to establish the limit of detection for this ricin assay. Specifically, we use logistic regression to determine the probability of detection for individual ricin peptides at different concentrations. We then apply basic rules from probability theory, combining these individual peptide probabilities into an overall assay limit of detection. This procedure yields an assay limit of detection for ricin at 42.5 ng on column or 21.25 ng/µL for a 2-µL injection. We also show that, in spite of the conventional wisdom that detergents are deleterious to mass spectrometric analyses, the presence of Tween-20 did not prevent detection of ricin peptides, and indeed assays performed in buffers that included Tween-20 gave better results than assays performed using other buffer formulations with or without detergent removal.

Heller, Natalie C.↗

DOE OSTI2019

Uniformly 15N-Labeled Recombinant Ricin A-chain as an Internal Retention Time Standard for Increased Confidence in Forensic Identification of Ricin by Untargeted Nanoflow Liquid Chromatography-Tandem Mass Spectrometry

Ricin, a toxic protein from the castor plant, is of forensic and biosecurity interest because of its high toxicity and common occurrence in crimes and attempted crimes. Qualitative methods to detect ricin are therefore needed. Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics methods are well-suited because of their high speci-ficity. Specificity in LC-MS/MS comes from both the LC and MS components. However, modern untargeted proteomics methods often use nanoflow LC, which has less reproducible retention times than standard-flow LC, making it challenging to use retention time as a point of identification in a forensic assay. We address this challenge by using retention times rela-tive to a standard, namely uniformly 15N–labeled ricin A-chain produced recombinantly in a bacterial expression system. This material, added as an internal standard prior to trypsin digestion, produces a stable isotope labeled standard for every ricin tryptic peptide in the sample. We show that the MS signals for 15N and natural isotopic abundance ricin peptides are distinct, with mass shifts that correspond to the numbers of nitrogen atoms in each peptide or fragment. We also show that, as expected, labeled and unlabeled peptides coelute, with relative retention time differences of less than 0.2%.

ricin, mass spectrometry, isotope, nitrogen-15, re↗

DOE OSTI2021

Rapid Identification of Synthetic Routes to Functional Metastable Phases Using X-ray Probed Laser Anneal Mapping (XPLAM) Time-Temperature Quench Maps

Many material systems have known or predicted functional phases that are metastable at standard temperature and pressure. While substantial advances have been made in the high-throughput and combinatorial synthesis of materials with a range of stoichiometries, investigation of thermal processing remains largely the domain of iterative uniform anneals or static gradients. Here we develop X-ray probed laser anneal mapping (XPLAM), a high throughput technique coupling spatially resolved X-ray diffraction with microsecond to millisecond laser gradient anneals to produce temperature–dwell–transformation (TDT) diagrams of the phase as a function of quench time and temperature. In addition to showing regimes where specific metastable phases form preferentially, TDT diagrams provide insight into the submillisecond kinetics of solid–solid phase transitions. This is a unique tool for mapping reaction pathways for metastable phases. As a first demonstration of XPLAM, we study Bi2O3, which has a rich set of polytypes, including the d-phase with an exceptionally high oxygen ion conductivity. We demonstrate the first annealing-driven synthesis of room temperature d-Bi2O3. We expect XPLAM to prove a powerful technique for rapid identification of synthetic routes to metastable phases and to generate the exhaustive data sets required for machine learning-guided exploration of materials processing.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Identification of Suitable Vacuum Gas Oils as Plasticizers Using HT-GC × GC-HRMS

Vacuum gas oils (VGOs) have long been heralded as effective plasticizers due to their high boiling-points and lubricating properties. One of these VGOs, HyVac Oil 93050, is a paraffinic plasticizer that is currently utilized in polymer–plasticizer explosive formulations, while this oil effectively increases the elasticity and decreases the sensitivity for current formulations. We seek to identify alternative VGOs with similar density, viscosity, molecular composition, and impurities for future formulations. In this study, 18 VGOs, including HyVac Oil 93050, were initially evaluated for their density and viscosity. The oils were then ranked based on physical characteristics and analyzed for molecular composition using high-temperature comprehensive two-dimensional gas chromatography with high-resolution time-of-flight mass spectrometry (HT-GC × GC-HRMS). Further, the HT-GC × GC-HRMS chromatograms of the top 10 most similar VGOs to HyVac Oil 93050, according to density, were compared utilizing the Pearson correlation coefficient. Three of the oils with densities similar to those of HyVac Oil 93050 had a Pearson correlation within the lot-to-lot variation of HyVac Oil 93050. For the top 3 candidates, Pearson correlation was then utilized as a feature selection technique to discover significant chemical differences. Positive chemical ionization (PCI) and negative chemical ionization (NCI) HT-GC × GC-HRMS chromatograms were also evaluated and aided in the discovery and identification of several key compounds including additives that acted as stabilizers for the VGOs. After this further chemical analysis, two of the original 17 potential VGOs were determined to be physically and chemically similar to HyVac Oil 93050.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Force-Free Identification of Minimum-Energy Pathways and Transition States for Stochastic Electronic Structure Theories

Here, the accurate mapping of potential energy surfaces (PESs) is crucial to our understanding of the numerous physical and chemical processes mediated by atomic rearrangements, such as conformational changes and chemical reactions, and the thermodynamic and kinetic feasibility of these processes. Stochastic electronic structure theories, e.g., Quantum Monte Carlo (QMC) methods, enable highly accurate total energy calculations that in principle can be used to construct the PES. However, their stochastic nature poses a challenge to the computation and use of forces and Hessians, which are typically required in algorithms for minimum-energy pathway (MEP) and transition state (TS) identification, such as the nudged elastic band (NEB) algorithm and its climbing image formulation. Here, we present strategies that utilize the surrogate Hessian line-search method, previously developed for QMC structural optimization, to efficiently identify MEP and TS structures without requiring force calculations at the level of the stochastic electronic structure theory. By modifying the surrogate Hessian algorithm to operate in path-orthogonal subspaces and at saddle points, we show that it is possible to identify MEPs and TSs by using a force-free QMC approach. We demonstrate these strategies via two examples, the inversion of the ammonia (NH 3 ) molecule and the nucleophilic substitution (S N 2) reaction F – + CH 3 F → FCH 3 + F – . We validate our results using Density Functional Theory (DFT)- and Coupled Cluster (CCSD, CCSD(T))-based NEB calculations. We then introduce a hybrid DFT-QMC approach to compute thermodynamic and kinetic quantities, free energy differences, rate constants, and equilibrium constants that incorporates stochastically optimized structures and their energies, and show that this scheme improves upon DFT accuracy. Our methods generalize straightforwardly to other systems and other high-accuracy theories that similarly face challenges computing energy gradients, paving the way for highly accurate PES mapping, transition state determination, and thermodynamic and kinetic calculations at significantly reduced computational expense.

Iyer, Gopal R.↗

DOE OSTI2026

Identification of KLHL12 Ligands Using Fragment-Based Methods

Targeted protein degradation can be induced by recruiting a protein of interest to an E3 ligase, resulting in its ubiquitination and subsequent proteasome-mediated degradation. However, only a small number of E3 ligases have been utilized for degradation. Expansion of the repertoire of useful E3 ligases via the identification of ligands to those ligases could broaden the scope and applicability of the degradation paradigm. We have identified KLHL12 as an E3 ligase with higher expression in cancer over normal tissues. We report here the use of NMR-based screening to identify fragments that bind to KLHL12, and X-ray structures of a fragment hit bound to KLHL12. Using this structural information, we optimized the hits, leading to the first reported small molecules that bind to KLHL12 with submicromolar affinity. Derivatives of these compounds may be useful for the construction of PROTACs to selectively degrade protein targets in tumors while sparing normal cells.

Amines↗

DOE OSTI2025

Atom Identification in Bilayer Moiré Materials with Gomb-Net

Moiré patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, deconvoluting the moiré pattern. This enables layer-specific mapping of atomic positions and dopant distributions, unlike other commonly used segmentation models which struggle with moiré-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS 2 -WS 2(1–x) Se 2x heterostructure and found that layer-specific implantation sites are unaffected by the moiré pattern’s local energetic or electronic modulation. In conclusion, this advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

In Situ Identification of NNH and N 2 H 2 by Using Molecular-Beam Mass Spectrometry in Plasma-Assisted Catalysis for NH 3 Synthesis

In this work, ammonia synthesis at 533 K and atmospheric pressure was investigated in a coaxial dielectric barrier discharge (DBD) plasma reactor without packing and with porous γ-Al 2 O 3 , 5 wt % Ru/γ-Al 2 O 3 , or 5 wt % Co/γ-Al 2 O 3 catalyst particles. Gas-phase species were monitored in situ using an electron impact molecular-beam mass spectrometer (EI-MBMS). Gas-phase species NNH and N 2 H 2 were first identified under common conditions of plasma-assisted ammonia synthesis and were present at levels comparable to that of NH 3 in the plasma discharge. Concentrations of NNH, N 2 H 2 , and NH in a reactor packed with γ-Al 2 O 3 or other particles were lower than those observed in an empty reactor, while the concentration of NH 3 increased. These observations point to the importance of NNH and N 2 H 2 in plasma-assisted surface reactions in ammonia synthesis. Reaction pathways of direct adsorption of gas-phase NNH and N 2 H 2 on solid surfaces and subsequent reactions were proposed. This study demonstrated that in situ identification of gas-phase species via EI-MBMS provides a powerful approach to study the kinetics of plasmaassisted catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Identification of the Selective Sites for Electrochemical Reduction of CO to C 2+ Products on Copper Nanoparticles by Combining Reactive Force Fields, Density Functional Theory, and Machine Learning

Recent experiments have shown that CO reduction on oxide derived Cu nanoparticles (NP) are highly selective toward C 2+ products. However, understanding of the active sites on such NPs is limited, because the NPs have ~200 000 atoms with more than 10 000 surface sites, far too many for direct quantum mechanical calculations and experimental identifications. We show here how to overcome the computational limitation by combining multiple levels of theoretical computations with machine learning. This approach allows us to map the machine learned CO adsorption energies on the surface of the copper nanoparticle to construct the active site visualization (ASV). Furthermore, we identify the structural criteria for optimizing selective reduction by predicting the reaction energies of the potential determining step, $ΔE_{OCCOH}$, for the C 2+ product. Based on this structural criterion, we design a new periodic copper structure for CO reduction with a theoretical faradaic efficiency of 97%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Coupling High-Throughput and Targeted Screening for Identification of Nonobvious Metabolic Engineering Targets

Identification of metabolic engineering targets is a fundamental challenge in strain development programs. While high-throughput (HTP) genetic engineering methodologies capable of generating vast diversity are being developed at a rapid rate, a majority of industrially interesting molecules cannot be screened at sufficient throughput to leverage these techniques. We propose a workflow that couples HTP screening of common precursors (e.g., amino acids) that can be screened either directly or by artificial biosensors, with low-throughput targeted validation of the molecule of interest to uncover nonintuitive beneficial metabolic engineering targets and combinations hereof. Using this workflow, we identified several nonobvious novel targets for improving p-coumaric acid (p-CA) and L -DOPA production from two large 4k gRNA libraries each deregulating 1000 metabolic genes in the yeast Saccharomyces cerevisiae. We initially screened yeast cells transformed with gRNA library plasmids for individual regulatory targets improving the production of l-tyrosine-derived betaxanthins, identifying 30 targets that increased intracellular betaxanthin content 3.5–5.7 fold. Hereafter, we screened the targets individually in a high-producing p-CA strain, narrowing down the targets to six that increased the secreted titer by up to 15%. To investigate whether any of the six targets could be additively combined to improve p-CA production further, we created a gRNA multiplexing library and subjected it to our proposed coupled workflow. The combination of regulating PYC1 and NTH2 simultaneously resulted in the highest (threefold) improvement of the betaxanthin content, and an additive trend was also observed in the p-CA strain. Lastly, we tested the initial 30 targets in a L -DOPA producing strain, identifying 10 targets that increased the secreted titer by up to 89%, further validating our screening by proxy workflow. This coupled approach is useful for strain development in the absence of direct HTP screening assays for products of interest.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Identification and Catalysis of the Potential-Limiting Step in Lithium-Sulfur Batteries

The Li-S chemistry is thermodynamically promising for high-density energy storage but kinetically challenging. Over the past few years, many catalyst materials have been developed to improve the performance of Li-S batteries and their catalytic role has been increasingly accepted. However, the classic catalytic behavior, i.e., reduction of reaction barrier, has not been clearly observed. Crucial mechanistic questions, including what specific step is limiting the reaction rate, whether/how it can be catalyzed, and how the catalysis is sustained after the catalyst surface is covered by solid products, remain unanswered. Herein, we report the first identification of the potential-limiting step of Li-S batteries operating under lean electrolyte conditions and its catalysis that conforms to classic catalysis principles, where the catalyst lowers the kinetic barrier of the potential-limiting step and accelerates the reaction without affecting the product composition. After carefully examining the electrochemistry under lean electrolyte conditions, we update the pathway of the Li-S battery reaction: S 8 solid is first reduced to Li 2 S 8 and Li 2 S 4 molecular species sequentially; the following reduction of Li 2 S 4 to a Li 2 S 2 -Li 2 S solid with an almost constant ratio of 1:4 is the potential-limiting step; the previously believed Li 2 S 2 -to-Li 2 S solid-solid conversion does not occur; the recharging reaction is relatively fast. We further demonstrate that supported cobalt phthalocyanine molecules can effectively catalyze the potential-limiting step. After Li 2 S 2 /Li 2 S buries the active sites, it can self-catalyze the reaction and continue driving the discharging process.

25 ENERGY STORAGE↗