Defense Applications Research Articles

Tracking Water Transport with Short-Wave Infrared: Kinetic Phase Diagrams, Dissolution, and Drying.

Short-wave infrared (SWIR) imaging has been extensively used in defense applications but remains underutilized in the study of soft materials and the broader consumer product industry. Water molecules absorb around ∼1450 nm, making moisture-rich objects appear black, whereas surfactants and other common molecules in consumer products do not absorb and provide a good contrast. This experimental study showcases the varied capabilities of SWIR imaging in tracking water transport in soft material systems by analyzing dissolution dynamics, tracking phase transitions (when combined with cross-polarized optical imaging), and monitoring drying kinetics in the surfactant and polymer solutions. The dynamic phase evolution to equilibria of a binary aqueous solution of a nonionic surfactant hexaethylene glycol monododecyl ether (C12E6) is presented. The influence of confined hydration in dynamic-diffusive interfacial transport capillaries was investigated by tracking the micellar to hexagonal phase transition concentration (C*). The effects of varying concentrations of an industrially relevant additive─monovalent common salt (NaCl) on the radial (2D) dissolution of lamellar-structured concentrated sodium lauryl ether sulfate (70 wt % SLE1S) pastes was studied. An equation was developed to estimate the radial dissolution coefficients based on total dissolution time and surfactant concentrations in the sample and solvent. Water loss was investigated by tracking the drying of aqueous poly(vinyl) alcohol films. In situ monitoring of drying kinetics is used to draw correlations between the solution viscosity and drying time. SWIR imaging has already revealed previously inaccessible insights into surfactant hydration and holds the potential to become a turnkey method in tracking water transport, enabling better quality control and product stability analysis.

Production Optimization and Potential Bioactivities of Biosurfactant from PET Surface-Dwelling Oligotrophic Bacillus sp. EIKU23.

The growing demand for efficient biosurfactants in various industrial sectors has driven the search for sustainable alternatives, enhanced production methods, and low-cost substrates. This study aimed to optimize the production, characterize, and assess the bioactivities of biosurfactants produced by an oligotrophic PET plastic-associated Bacillus sp. EIKU23. The bacterium yielded the highest amount of biosurfactant after 6days of incubation in Luria broth medium (pH 7.0) at 30°C without any additives. FTIR and NMR analyses confirmed the lipopeptide nature of the biosurfactant, which exhibited a negative charge. The biosurfactant remained stable at 4°C-80°C and pH 7.0-8.0 for at least 7days. It exhibited antioxidant properties comparable to the ascorbic acid standard, with efficacy ranging from 23.61% to 89.96% in different antioxidant assays. It showed antibacterial activity against both Gram-positive and Gram-negative potential pathogens. The biosurfactant induced substantial DNA leakage at a concentration of 10mg/mL and eradicated approximately 48.4% of pre-formed Staphylococcus aureus biofilm and showed anti-attachment behaviour to a polystyrene surface. Additionally, the biosurfactant precipitated up to 98.7% uranium from an aqueous solution, demonstrating its potential for bioremediation. These findings suggest that the biosurfactant produced by Bacillus sp. EIKU23 is multifunctional with promising applications in bioremediation, antibacterial activity, antibiofilm formation, and antioxidant defense, offering a novel solution for sustainable industrial practices and plastic waste management.

Detection of Military Aircraft Using YOLO and Transformer-Based Object Detection Models in Complex Environments

Computer vision and deep learning techniques are widely applied in object detection tasks across various domains, including defense technologies. Accurate and efficient detection of military aircraft plays a critical role in strengthening air defense systems and enabling effective strategic decision-making. This study evaluates the performance of YOLOv7, YOLOv8, and RT-DETR models in detecting military aircraft using a dataset consisting of 19.514 images spanning 43 aircraft models. The dataset incorporates images captured from various angles and diverse backgrounds, such as urban, rural, and coastal areas, ensuring realistic testing conditions. However, class imbalance is observed, with certain aircraft models, such as the F14 and F16, being more represented than others, which may affect model generalization. To address these challenges, hyperparameters were optimized, and performance metrics, including mean Average Precision (mAP) and recall, were analyzed. Experimental results show that YOLOv8 achieved 94% mAP and 88.1% recall, YOLOv7 reached 90.2% mAP and 82.7% recall, while RT-DETR demonstrated consistent performance with 92.7% mAP and 90.4% recall. These findings highlight the strengths and limitations of the evaluated models and provide inferences for improving detection systems in defense applications.

A Pixel-Based Machine Learning Atmospheric Correction for PeruSAT-1 Imagery

Atmospheric correction is essential in remote sensing, as it reduces the effects of light absorption and scattering by suspended particles and gases, enabling accurate surface reflectance computation from the observed Top-of-Atmosphere (TOA) reflectance. Each satellite sensor requires a customized atmospheric correction processor due to its unique system characteristics. Currently, PeruSAT-1, the first Peruvian remote sensing satellite, does not include this capability in its image processing pipeline, which poses challenges for its effectiveness in defense, security, and natural disaster management applications. This research investigated pixel-based machine learning methods for atmospheric correction of PeruSAT-1, using Sentinel-2 harmonized Bottom-of-Atmosphere (BOA) surface reflectance images as a benchmark, alongside additional atmospheric, terrain, and acquisition parameters. A robust dataset was developed to align data across temporal, spatial, geometric, and contextual conditions. Experimental results showed R2 values between 0.886 and 0.938, and RMSE values ranging from 0.009 to 0.025 compared to the benchmarks. Among the models tested, the Feedforward Neural Network (FFNN) using the Leaky ReLU activation function achieved the best overall performance. These findings confirm the robustness of this approach, offering a scalable methodology for satellites with similar characteristics and establishing a foundation for a customized atmospheric correction pipeline for PeruSAT-1. Future work will focus on diversifying the dataset across spectral and seasonal conditions and optimizing the modeling to address challenges in shorter wavelengths and high-reflectance surfaces.

A Review on Securing Wireless Networks against IIRS Proliferation and Jamming Attacks

The internet of thins (IoT) framework has helped in design of high speed connected networks which include Wide Area Networks (WANs). Cognitive Radio can be considered to be one of the key enablers of high speed data transfer with evolving generation of wireless networks. Cognitive networks find their applications in military and defence as well with the advent of internet of things and its allied applications in military warfare. Cognitive Radio Networks often share common resources such as bandwidth or spectrum among several users or stations. Due to continued sharing of resources, cognitive networks often come under security attacks, most common of which are jamming and eavesdropping attacks. In this paper, the basics of cognitive IoT networks have been presented with the focus of security aware cognitive IoT networks. Previous work in the allied domain has been discussed along with their salient features. Keywords: Wide Area Network (WAN), Internet of Things (IoT), cognitive radio network (CRN), security aware spectrum assignment, cyber-attacks, false alarm, and throughput.

Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery.

Recognizing targets in infra-red images is an important problem for defense and security applications. A deployed network must not only recognize the known classes, but it must also reject any new or unknown objects without confusing them to be one of the known classes. Our goal is to enhance the ability of existing (or pretrained) classifiers to detect and reject unknown classes. Specifically, we do not alter the training strategy of the main classifier so that its performance on known classes remains unchanged. Instead, we introduce a second network (trained using regression) that uses the decision of the primary classifier to produce a class conditional score that indicates whether an input object is indeed a known object. This is performed in a Bayesian framework where the classification confidence of the primary network is combined with the class-conditional score of the secondary network to accurately separate the unknown objects from the known target classes. Most importantly, our method does not require any examples of OOD imagery to be used for training the second network. For illustrative purposes, we demonstrate the effectiveness of the proposed method using the CIFAR-10 dataset. Ultimately, our goal is to classify known targets in infra-red images while improving the ability to reject unknown classes. Towards this end, we train and test our method on a public domain medium-wave infra-red (MWIR) dataset provided by the US Army for the development of automatic target recognition (ATR) algorithms. The results of this experiment show that the proposed method outperforms other state-of-the-art methods in rejecting the unknown target types while accurately classifying the known ones.

Investigation of the Ocular Response and Corneal Damage Threshold of Exposure to 28 GHz Quasi-millimeter Wave Exposure.

Electromagnetic radiation energy at millimeter wave frequencies, typically 30 GHz to 300 GHz, is ubiquitously used in society in devices for telecommunications; radar and imaging systems for vehicle collision avoidance, security screening, and medical equipment; scientific research tools for spectroscopy; industrial applications for non-destructive testing and precise measurement; and military and defense applications. Understanding the biological effects of this technology is essential. We have been investigating ocular responses and damage thresholds comparing various frequencies using rabbit eyes and dedicated experimental apparatus. In this study we investigated the 28 GHz quasi-millimeter wave band (wavelength: 10.7 mm), a candidate for 5G communication. Similar to millimeter wave frequencies, ocular damage from exposure to 28 GHz for 6 min (400 mW cm-2) included corneal epithelial damage, corneal edema, and opacity. The incident power density threshold, indicating a 50% probability of ocular damage from exposure for 6 min, was found to be 359 mW cm-2 for 28 GHz. Comparing the ocular exposure area for various millimeter wave frequencies (40, 75, 95 GHz) and 28 GHz quasi-millimeter waves using a thermosensitive liquid crystal capsule, we found that for millimeter waves, even at identical incident power densities, the ocular exposure area decreases as the frequency increases (lens effect). However, this lens effect was not observed at 28 GHz, where the entire anterior segment area was exposed to radio waves.

Microstructural Assessment of Molybdenum Disulfide Coatings Using Nanoindentation Hardness.

MoS2 coatings are used extensively in aerospace and defense applications due to their ultralow friction and high wear resistance. Burnished and resin-bonded MoS2 coatings are commonly used in these applications due to simplicity in deposition and history of use, despite issues with consistency in coating properties and performance. Physical vapor deposition (PVD) of MoS2 thin films has emerged as a process alternative in the past 50 years, promising far greater control over film structure and composition but at a greater cost. Despite PVD's benefits, hesitance to adoption persists in high-consequence applications, not only due to increased costs but variability in resulting coating properties. These variations in properties and subsequent performance are in part due to the complexity of the PVD process and the sensitive interplay between coating process-structure-property relationships. This work aims to demystify the remaining uncertainties of the process-structure-property relationships in PVD MoS2. The microstructure and mechanical and tribological properties of 61 different PVD pure MoS2 coatings are examined herein. Emphasis has been placed on developing performance-based (i.e., hardness, modulus) metrics that can assess microstructural changes (density, orientation, and crystallinity) and be utilized to accelerate process development and coating optimization. Relationships established within suggest that nanoindentation hardness can be used to infer coating performance (i.e., wear rate) and properties (i.e., density, crystalline texture, and stoichiometry). Furthermore, this work demonstrates that PVD MoS2 coatings close to the theoretical density of MoS2 consistently have the best tribological performance and can be reliably identified by their hardness.

Review of Short-Wavelength Infrared Flip-Chip Bump Bonding Process Technology.

Short-wave infrared (SWIR) imaging has a wide range of applications in civil and military fields. Over the past two decades, significant efforts have been devoted to developing high-resolution, high-sensitivity, and cost-effective SWIR sensors covering the spectral range from 0.9 μm to 3 μm. These advancements stimulate new prospects across a wide array of fields including life sciences, medical diagnostics, defense, surveillance, security, free-space optics (FSO), thermography, agriculture, food inspection, and LiDAR applications. In this review, we begin by introducing monolithic SWIR image sensors and hybrid SWIR image sensors and indicate that flip-chip bump bonding technology remains the predominant integration method for hybrid SWIR image sensors owing to its outstanding performance, adaptable integration with innovative epitaxial SWIR materials, long-term stability, and long-term reliability. Subsequently, we comprehensively summarize recent advancements in epitaxial thin-film SWIR sensors, encompassing FPAs and flip-chip bump bonding technology for epitaxial InGaAs and Ge (Sn) thin-film SWIR sensors. Finally, a summary and outlook regarding the development of InGaAs and Ge (Sn) SWIR sensors are provided and discussed. The ongoing evolution of epitaxial thin-film SWIR sensors with flip-chip bump bonding technology is poised to foster new applications in both academic and industry fields.

A review of recent advancements in the impact response of fiber metal laminates.

Fiber metal laminates (FMLs) have garnered significant attention due to their exceptional impact resistance, making them attractive for various structural applications. This review presents recent advancements in understanding the impact behavior of FMLs under low- and high-velocity impact scenarios. Low-velocity impacts, commonly encountered during manufacturing, handling, and tool drops, are discussed, with a focus on damage mechanisms, energy absorption capabilities, and influential factors such as impactor geometry and boundary conditions. Additionally, this review delves into high-velocity impact events, simulating scenarios such as ballistic impacts, highlighting the role of FMLs in mitigating perforation and enhancing damage tolerance. The effects of various parameters on the impact response are critically analyzed. The findings presented herein contribute to the development of lightweight, impact-resistant FML components for aerospace, automotive, and defence applications.

High‐Performance Bionic Architectures via 3D Printing of Recycled Kevlar: A Study of Laser Ablation and Mechanical Properties

ABSTRACTThe study investigates the potential of 3D printing as an eco‐friendly and cost‐effective method to repurpose waste fabrics into valuable products. It explores the use of bio‐inspired structures in 3D printing to gain insights into sandwich structures' mechanical properties and laser ablation characteristics. By incorporating natural skeleton designs such as nacreous and foliated hierarchies and industrial waste material (KF), the research aims to enhance mechanical performance, laser ablation characteristics, and sustainable waste management. The study demonstrates the synergistic application of two distinct additive manufacturing techniques (FDM and DIW) at the micron scale to create nacre‐inspired sheet, columnar, and laminated structures, allowing for manipulation of rigid and pliant material phases within KF waste. The featured architectures are found in the Mollusk Seashell, which improves toughness and strength simultaneously for high‐end mechanical applications. During impact loading, the study highlights the enhanced energy dissipation and delamination properties of foliated structures compared to nacre‐like ones. The research emphasizes that the mass loss of NS + KF comprises the lowest value compared to NC + KF, then FL + KF, and pristine + KF at 375 mJ for 210 s. Ultimately, the findings hold promise for applications in aerospace, defense, automobile, thermal management, laser ablation, and structural components due to the demand for waste utilization, accessibility, environmental responsibility, and green manufacturing practices.

Genome-Wide Identification of the ABC Gene Family in Rosaceae and Its Evolution and Expression in Response to Valsa Canker

The ATP-binding cassette (ABC) transporter family plays a critical role in plant growth, development, and disease resistance. However, the evolution and functional characteristics of the ABC gene family in Rosaceae species have not been fully studied. In this study, we performed the first whole-genome identification, as well as an evolutionary analysis and comparative analysis of ABC genes in Rosaceae plants. We identified 3037 ABC genes in 20 plant species, classifying them into eight subfamilies. Comparative analysis revealed significant variations in family size and expansion patterns among species, suggesting adaptive evolution. Tandem duplication (TD: where genes are duplicated in sequence) and whole-genome duplication (WGD: duplication of the entire genome) were identified as the primary drivers of ABC family expansion. In pears, gene pairs produced by WGD underwent purifying selection. Gene ontology (GO) enrichment analysis indicated the involvement of ABC proteins in transmembrane transport and signal transduction pathways. Under Valsa pyri infection, most ABC genes were upregulated in the early stages, highlighting the role of ABCG genes in pathogen response. A weighted gene co-expression network analysis (WGCNA) identified five key ABCG genes potentially involved in pathogen resistance regulation. Our findings provide insights into the evolutionary adaptability of the ABC gene family and their potential applications in plant disease defense.

High-velocity impact studies of honeycomb sandwich structures with Al/Al2O3 and Al/B4C functionally graded plasma sprayed faceplates

High-velocity impact response of honeycomb sandwich structures (HSS) with Al/Al2O3 and Al/B4C functionally graded plasma-sprayed (FGPS) faceplates are investigated in present work. FGPS structures improve the specific material properties and make the structure distinct from the substrate material. The metal and ceramic content was varied across the thickness of the FGPS coating in the present work. The HSS having honeycomb core sandwiched between two Al/Al2O3 FGPS faceplates were manufactured initially. Further, HSS having honeycomb core sandwiched between two Al/B4C FGPS faceplates were manufactured. Such HSS are repeatedly impacted with a spherical projectile using a single-stage gas gun at a constant impact energy of 260 J, and the results are quantified and compared. The central deflection and dent diameter of FGPS plates as well as HSS were determined, and they increased with the number of impacts. The HSS’s energy absorption was dissipated by top faceplate indentation and core compression. The incorporation of a core prevented FGPS coating delamination and top faceplate penetration. The Al/Al2O3 and Al/B4C FGPS faceplates had dent diameters that were 14.30% and 18.70% smaller than the non-coated Al 6061-T6 faceplate, respectively, which proves the enhancement of high-velocity impact resistance through FGPS coating. The central deflection and dent diameter of the Al/B4C FGPS HSS are 6.04% and 3% lesser than the Al/Al2O3 FGPS HSS, respectively. The energy absorption of the Al/B4C FGPS HSS is better than that of the Al/Al2O3 FGPS HSS. As a result, the present research enhances the knowledge on the impact energy absorption of two distinct FGPS coated plates and HSS, which is highly useful in aerospace and defence applications.

Mathematical model for enhancing midwave infrared transmission using phoxonic crystals

Abstract This paper presents a novel mathematical model for designing a highly efficient on-chip optical waveguide operating in the Mid-Wave Infrared (MWIR) spectrum, specifically covering a range from 3-5μm. The proposed waveguide (called Phoxonic waveguide) architecture achieves exceptional transmission rates of up to 99.8% throughout this broad range of MWIR. The simultaneous control of photon and phonon transmission in the proposed waveguide structure gives its name Phoxonic crystal waveguide. The exceptional performance in the proposed waveguide structure has been achieved due to the innovative use of a mirror-symmetric architecture, which effectively suppresses losses caused by the interaction between photons and phonons. To validate the proposed mathematical model's effectiveness, extensive numerical simulations were conducted using the Qutip platform. This research opens up promising avenues for the development of MWIR waveguides with wide-ranging applications in communication, defense, medicine, and technology.

A novel dual-band absorber for millimeter-wave RADAR applications

Abstract A linearly polarized dual-resonant millimeter-wave absorber for Radio Detection And Ranging (RADAR)applications is presented in this paper. The frequency-selective absorber (FSA) is composed of solitarily using the distributed elements. The proposed FSA achieves a dual-band resonance characteristic utilizing the mutual coupling between concentric square loops, the second harmonic mode of the Jerusalem cross, and the corrugated cross grids. The proposed dual-band FSA operates from 25.5 to 26.5 GHz (1 GHz) (fL) and 31.8 GHz–32.5 GHz (0.7 GHz) (fH) with minimum absorptivity of 96% and 92%, respectively. The desired frequency response of the proposed unit cell is demonstrated by an equivalent circuit model. The FSA prototype is fabricated and the simulated results are validated using experimental measurements. The proposed FSA is a suitable candidate for stealth application in defense and military systems.

Optimal input excitations for suppressing nonlinear instabilities in multimode fibers

Wavefront shaping has become a powerful tool for manipulating light propagation in various complex media undergoing linear scattering. Controlling nonlinear optical interactions with spatial degrees of freedom is a relatively recent but fast growing area of research. A wavefront-shaping-based approach can be used to suppress nonlinear stimulated Brillouin scattering (SBS) and transverse mode instability (TMI), which are the two main limitations to power scaling in high-power narrowband fiber amplifiers. Here we formulate both SBS and TMI suppression as optimization problems with respect to coherent multimode input excitation in a given multimode fiber. We develop an efficient method using linear programming for finding the globally optimal input excitation for minimizing SBS and TMI individually or jointly. The theory shows that optimally exciting a standard multimode fiber leads to roughly an order of magnitude enhancement in instability-free output power compared to fundamental-mode-only excitation. We find that the optimal mode content is robust to small perturbations and our approach works even in the presence of mode-dependent loss and gain. When such optimal mode content is excited in real experiments using spatial light modulators, the stable range of ultrahigh-power fiber lasers can be substantially increased, enabling applications in gravitation wave detection, advanced manufacturing, and defense.

Mechanical and Wear Behavior of Aluminium Metal Matrix Composites Reinforced Ceramics Materials for Light Structures

Aluminium Alloy based Metal Matrix Composites (AAMMCs) has widely used in defense, aircraft and automobile applications because of their enhanced engineering properties with light weight metals. Nano sized silicon nitride (80 μm) is used as a reinforcement in this study, whereas aluminium alloy 8011 is selected as the matrix material. Using the stir casting method, metal matrix composites made of aluminium alloy 8011 with varying weight percentages of Si3N4 (0, 4, 8, 12, and 16) are created. The stir casted AL 8011/Si3N4 composites further heated under T6 condition. The AL 8011/Si3N4 – T6 composites are further subjected to Energy Dispersive X ray Analysis (EDAX) and Scanning Electron Microscope (SEM) to identify by the presence of elements and study the microstructure characterization, respectively. The density, microhardness and wear test are conducted by employing Archimedes principle, Vickers hardness tested and pin on disc equipment, respectively. The wear test is done at different sliding distances like (500, 1000, 1500 and 2000 m), applied load like (10, 20, 30 and 40 N) and kept sliding at a speed of 1 m/s. The increasing weight percentage of silicon nitride expands the increasing of density and Vickers hardness up to 12 wt % of silicon nitride and decreasing by 16 wt % addition. The wear resistances of AL 8011/12 wt % Si3N4 –T6 composite exhibits higher wear resistance than other Al8011 based composites.

Spectrum‐sensing algorithm based on graph feature fusion

AbstractGraph‐based spectrum sensing in noisy environments has major implications for civilian and military signal processing applications. However, existing algorithms suffer from high computational complexity and performance deterioration at low signal‐to‐noise ratios (SNRs). Therefore, a spectrum‐sensing algorithm based on graph feature fusion using a quadratic form derived from self‐loop weights and the graph Laplacian matrix is proposed in this study. The sum of the first and second block maxima of the power spectrum of the observed signal is selected as the input to the graph converter. Self‐loop weights are combined with the Laplacian matrix to construct the graph quadratic form, which serves as the test statistic for decision‐making. By applying majorisation and the extreme value theory, it is demonstrated that the proposed algorithm outperforms existing methods. The simulation results confirm the robust spectrum‐sensing performance across various signal modulation types and pulse shapes. Thus, compared to existing algorithms, except block range‐ and energy‐detection‐based methods, the proposed algorithm demonstrates the best spectrum‐sensing performance under low SNRs and channel‐fading conditions, while achieving the lowest computational complexity. The proposed approach enables more efficient and accurate spectrum sensing, fostering advancements in communication technologies and defence applications.

Experimental investigation of the friction stir welding process of AA5059 and AA7079 and prediction using fuzzy approach

Aluminum alloys AA 5059 and AA7079 have high strength, excellent weldability, and corrosion resistance, widely used in train and truck bodies, armored vehicles, and defense applications. Recent materials engineering tasks call for minimizing material weight and to achieve this objective, the solid-state joining approach of Friction stir welding (FSW) is utilized for material joining. FSW has produced sound welds with high joint strength and good ductility. This research investigates the weldability of AA5059 and AA7079 dissimilar aluminum alloys. In this FSW process, a threaded tapered cylindrical tool pin profile was selected, and welding trials were conducted at three different speeds of 500, 750, and 1000 rpm, with the 75 mm/min constant feed rate. The Taguchi L9 orthogonal array was selected to design the process parameters, that is, tool traversing speed, rotational speed, axial force, feed rate, and effect on dissimilar aluminum weld joints. The Mamdani-type fuzzy logic model predicted welded specimens’ ultimate tensile strength and yield strength. Microstructural analysis revealed that the grains in the stirring zone had undergone a full transformation into homogenous, evenly distributed, refined grains, which had a positive impact on the remarkable mechanical properties.

A Fluorinated Ether Co-solvent Enables High Operating Temperature Li-ion Batteries

Abstract Li-ion batteries are commonly used as electrochemical energy storage systems due to their high energy density. However, few Li-ion batteries can reliably function at elevated temperatures, which is necessary for space and defense applications. In this study, Li-ion electrolytes were prepared and investigated for use at 100°C. The previously developed baseline electrolyte, 1.0 M lithium hexafluorophosphate (LiPF6) in 1:1 ethylene carbonate (EC):ethyl-methyl carbonate (v/v) with 2 wt. % vinylene carbonate (VC), was altered to observe the effects of the lithium salts lithium difluoro(oxalato)borate (LiDFOB) and lithium difluorophosphate (LiDFP), and the fluorinated co-solvent 1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropyl ether (TTE). The resulting formulations showed significantly improved capacity retention at 100°C in multiple cell configurations. X-ray photoelectron spectroscopy characterization of the electrodes following cycling at high temperatures with the improved electrolyte revealed the cathode-electrolyte-interface to be boron-rich, while the graphite anodes were found to have little boron but were more fluorine rich compared to the baseline anodes. Raman spectroscopy determined notable changes in solvation structure upon addition of the TTE diluent. Overall, the use of various Li salts as well as the TTE co-solvent improved specific capacity retention at 100°C in Li-ion cells.