Sathyabama Institute of Science and Technology

Cesium-based cells have drawn plenty of attention from academics in recent years owing to their promising optoelectronic characteristics. The current study presents a comparison of the performance of ITO/WO3/CZTS/CIGS/ Mg–CuCrO2/Ag-based solar cells (SCs). The doubly CIGS/ CZTS - based SCs provide high performance parameters in compare to single-layered CIGS and CZTS-based SCs and limit their output. Optimising the thin flm materials is one of the best strategies to achieve high efciency, aside from physical characteristics like thickness, defect density, and series resistances. Additionally, SCAPS-1D software is used to optimise the optoelectronic parameters of J-V and QE in order to achieve high efciency. The optimum output parameters achieved in this simulation are: JSC of 31.72 mA/cm2, VOC of 1.10 V, FF of about 85.73%, and PCE of 29.96%, respectively

Multimodal data fusion is pivotal in artificial intelligence, with infrared and visible image fusion crucial for enhancing target detection. However, low-light visible images often lack vital details, impacting fusion quality and target detection accuracy, particularly in autonomous driving applications. To address these challenges, an innovative model is proposed that introduces a normalized depth-wise crested porcupine cross-attention model to extract complementary details from visible and infrared images. The incorporation of double cross-attention effectively enhances complementary information while minimizing redundant features. Subsequently, the Decomposed Non-subsampled Shearlet Structural Patch Transform (DNSSPT) model is employed to integrate features and generate the fused image. This process involves decomposing the extracted infrared and visible images into components such as signal intensity, mean intensity, and signal structure through structural patch decomposition. Next, a membership curve is applied to precisely determine the weight of the average intensity module, minimizing artifacts while preserving the importance of infrared targets. Additionally, sharpening operations are used to improve detail layer of both the visible and infrared images, leading to a fused image with higher contrast. Through subjective and objective evaluations, the proposed model outperforms existing fusion techniques, achieving minimum processing times of 0.0521, 0.0698, 0.0681, and 0.0832 s across the TNO, MSRS, VIFB, and VOT-RGBT datasets, respectively. Additionally, it attains high detection accuracies of 94% for VOT-RGBT, 92.71% for TNO, 96% for MSRS, and 90.89% for VIFB. Furthermore, object detection experiments using roadscene dataset confirm the DNSSPT model’s effectiveness in advancing computer vision tasks, achieving an accuracy of 94.1%.

This study aims to optimize the Wire‐Cut Electrical Discharge Machining (WEDM) parameters for Inconel 800, a high‐performance superalloy known for its remarkable mechanical properties and resistance to elevated temperatures. The research leverages Particle Swarm Optimization (PSO) to improve machining outcomes, including material removal rate, surface finish, and cost‐efficiency. A structured experimental approach, following Taguchi's L18 design, was used to evaluate the effects of key machining parameters such as pulse on‐time, pulse off‐time, peak current, and spark gap voltage. The results demonstrate that the PSO model significantly enhances machining performance by reducing surface roughness and increasing material removal rate (MRR), showcasing marked improvements in efficiency. With a mean prediction error of < 1%, the PSO model proves highly accurate and reliable. Additionally, the study examines the economic aspects of WEDM by calculating the total machining costs, which include power, wire, and dielectric fluid consumption. By filling a critical research gap in the machining of Inconel 800, this work offers valuable insights into optimizing WEDM processes for superalloys. The findings highlight the potential of PSO as a powerful tool for multi‐objective optimization in advanced manufacturing applications.

Predicting the rate of penetration (ROP) is critical for optimizing drilling performance, yet it remains a complex task due to the interplay of multiple geological and operational parameters. This study comprehensively evaluates machine learning models, utilizing a real-time, high-resolution dataset from drilling operations in southeast Iraq. Among the models tested, the Random Forest algorithm demonstrated outstanding performance, achieving an R2 of 0.955, a Mean Squared Error (MSE) of 0.119, and an Average Absolute Relative Error (AARE%) of 7.683, highlighting its reliability and robustness in predicting ROP. Sensitivity analysis and SHAP (Shapley Additive Explanations) also identified fracture pressure, kinematic viscosity, and rotary speed (RPM) as the most influential parameters affecting ROP. While alternative methods like Decision Tree and AdaBoost showed signs of overfitting, the results emphasize the Random Forest model’s superiority in balancing accuracy and generalizability. This research underscores the potential of advanced machine learning techniques in enhancing drilling performance, offering significant implications for real-world applications.

Background Suicidal ideation is a global public health concern, highlighting the need to identify modifiable risk factors. Handgrip strength (HGS), an objective measure of muscular strength, has been linked to mental health outcomes. This review synthesizes evidence on HGS and suicidal ideation, exploring modifiers such as sex. Methods This systematic review and meta‐analysis, registered with PROSPERO and conducted in accordance with PRISMA guidelines, evaluated data retrieved from PubMed, Embase, and Web of Science up to November 30, 2024. The analysis focused on randomized controlled trials and observational studies—including case‐control, cohort, and cross‐sectional designs—that examined the relationship between HGS and suicidal ideation in human populations. A random‐effects model was employed to calculate pooled odds ratios (ORs) with 95% confidence intervals (CIs). Heterogeneity among studies was assessed using the I² statistic. Results Out of 294 studies, 9 met the inclusion criteria for the systematic review, and 6 were eligible for meta‐analysis, involving 81,035 participants. The pooled analysis showed a nonsignificant association between HGS and suicidal ideation. For males, the pooled OR per 1 kg increase in HGS was 0.939 (95% CI, 0.875–1.009), and for females, it was 0.851 (95% CI, 0.662–1.094), indicating a potential but nonsignificant protective effect. Conclusion This systematic review and meta‐analysis found no statistically significant association between handgrip strength and suicidal ideation in the pooled analysis. However, consistent trends observed in the qualitative synthesis suggest a potential relationship that warrants further investigation. Longitudinal studies are essential to elucidate the underlying mechanisms.

Climate change and anthropogenic activities are significantly impacting groundwater resources, resulting in depletion and posing challenges to water sustainability globally. These changes influence aquifers through altered groundwater recharge patterns, shifts in soil biogeochemical processes, and modifications in land use practices, both direct and indirect. This study addresses a critical gap in current literature by thoroughly investigating the impacts of climate change on groundwater quality and stability. The primary objective of this research is to comprehensively understand hydrogeological systems’ vulnerability to climate change across multiple scales. It seeks to bridge existing knowledge gaps by exploring the complex interactions between climate change events and dynamics of groundwater contamination. Through a meticulous review and synthesis of relevant literature on potential hazards associated with groundwater pollution, this study aims to provide robust assessments of climate change impacts on groundwater quality. Furthermore, the study examines how changes in land use patterns driven by climate change may exacerbate groundwater pollution issues. By conducting a detailed analysis of pertinent scholarly publications and offering scientifically informed policy recommendations, this research contributes to enhancing our understanding and management of water quality challenges in a changing climate.

Cashew nutshell liquid (CNSL), a highly versatile byproduct of the cashew processing industry, offers remarkable potential across a broad spectrum of industrial and chemical applications. Extracted from the soft, spongy honeycomb structure inside the hard outer shell of the cashew nut, this dark reddish-brown, viscous liquid is a rich, renewable source of unsaturated phenolic compounds. Among bio-based raw materials, CNSL stands out for its cost-effectiveness, high chemical reactivity, and wide applicability in the production of polymers, coatings, resins, and biofuels. This review presents a comprehensive analysis of various extraction techniques used to isolate CNSL, with a particular focus on chemical extraction methods. Three primary extraction approaches – thermal, mechanical, and chemical – are discussed, each uniquely influencing the yield and composition of the extracted CNSL. Special attention is given to chemical extraction methods such as solvent extraction, Soxhlet extraction, and supercritical carbon dioxide (SC–CO 2 ) extraction, evaluating their efficiency, selectivity, and scalability. He extracted CNSL typically contains four major constituents: anacardic acid, cardanol, cardol, and 2-methyl cardol, with their relative proportions depending heavily on the extraction technique and operational conditions. Beyond traditional methods, this review also highlights recent advancements in green chemical processes aimed at reducing environmental impacts while enhancing material performance. These innovations pave the way for sustainable production of industrial chemicals and biomaterials from cashew waste, supporting global initiatives toward a circular economy and the principles of green chemistry.

In this study, we assumed the integrated vendor-buyer supply chain management along with probability distributions and Transportation cost. This research aims to determine the relevant elements that affect supply chain practices, enhance the effectiveness of stock control methods, and explore the influence of vendor coordination on SC improvement. Here, the mean and standard deviation are involved in the free distribution case. Moreover, the crashing cost of lead time is expressed as an exponential function. The defective items depend on free, uniform, triangular, and Rayleigh distributions. It is an easy way to minimize the total supply chain cost. Trade credit financing and Transportation cost with over declarations are the factors to elaborate this model. Finally, we obtain the minimum total expected cost, optimal solutions of the order quantity (Q), reorder point (R), lead time (L), and we compare these solutions with graphical representations.

Background COVID‐19 vaccination has raised concerns regarding its potential effects on women's reproductive health, particularly menstrual irregularities. This systematic review and meta‐analysis aimed to assess the impact of COVID‐19 vaccination on menstrual disturbances, bleeding patterns, and cycle duration among women of reproductive age. Methods A systematic search of PubMed, Embase, and Web of Science was conducted up to April 11, 2025. The study protocol was registered with the PROSPERO (CRD42024500832). Studies reporting menstrual changes postvaccination in women aged 13–50 were included. Data extraction and quality assessment were performed independently by two reviewers. Meta‐analyses using random‐effects models were conducted in R (version 4.3), with heterogeneity assessed using the I² statistic. Results Out of 586 records, 43 studies comprising 747,763 women met the inclusion criteria. The pooled RR for menstrual disturbances in vaccinated versus unvaccinated women was 1.03 (95% CI: 0.67–1.57; p = 0.88), indicating no significant association. Excluding one outlier increased the RR to 1.14 (95% CI: 0.97–1.34; p = 0.08). The overall pooled prevalence of menstrual disturbances postvaccination was 34% (95% CI: 26%–43%). Among vaccinated women, lighter bleeding was reported in 12.6%, heavier bleeding in 15.1%, irregular menstruation in 19.0%, and regular cycles in 56.6%. Shortened cycles occurred in 8.5%, longer cycles in 9.3%, amenorrhea (≥ 24 days) in 9.2%, and infrequent cycles (> 38 days) in 11.0%. All analyses showed high heterogeneity (I² = 98%–100%). Sensitivity analyses confirmed the robustness of findings, though Egger's test indicated potential publication bias (p = 0.0384). Conclusion COVID‐19 vaccination was not significantly associated with an increased risk of menstrual disturbances. Although minor changes such as altered bleeding patterns and cycle length were observed in some women, the overall impact on menstrual health was minimal.

Many technological advancements have been made because of the growth of IC. Everyday use of technology has had a profound impact on lives and existence, which would be unthinkable without them. As a result, the reliability issues associated with recent devices necessitate extraordinary, specialized effort. Accommodating several devices in a single and planar IC leads to various system-level damages to the IC, like the hot carrier effect, oxide breakdown, etc. This paper examines optimization strategies to improve the performance of nanomaterial-based liner materials in noise coupling sustainability. It also gives a complete defect analysis of those materials through electrical interventions. Active devices in one IC are integrated through another IC via vertical bonding. Through Silicon Vias (TSVs) and operational transistors are a major issue when implementing 3D IC, since it significantly lowers system efficiency. This study provides an innovative way to reduce electrical interference by utilizing several electrical interference designs, which include the TTSV framework, which also incorporates Thermal TSV while simulation, and the ETSV framework, which solely utilizes electrical signal carrying TSV. The study examined the electrical intervention of TSV-carrying signals to the substrate and other TSV. Additionally, using several suggested designs, this work shows further elevated frequency regimes up to 1 THz. Our simulation result suggests the proposed model has a marginal advantage in 3D IC developments with more than a 30% drop in electrical signal intervention from signal-carrying TSV to other TSV. Additionally, a guard ring was used to demonstrate electrical interference. When Teflon AF1600 liner material was used at the victim along with a P + protection ring, TSV demonstrated very little electrical interference. Additionally, the thermal effect was studied for the proposed TSV model.

Purpose of Review This study investigates the expanding range of mosquito-borne diseases in India, particularly in coastal regions, and explores the role of climate change in driving this trend. Coastal areas, which are densely populated and ecologically significant, are increasingly vulnerable to environmental changes such as sea level rise, increased precipitation, and higher temperatures. The review compares disease burdens between coastal and inland regions and links these patterns to changing climatic conditions. Recent Findings From 2014 to 2023, coastal states and union territories contributed significantly to mosquito-borne disease cases: 68.29% of chikungunya (157,783 cases), 48.52% of dengue (716,643 cases), and 49.21% of malaria (2,816,914 cases). During this period, coastal zones experienced rising temperatures and humidity, creating favourable conditions for mosquito breeding and disease transmission. These findings suggest that climate change is a key driver of increased disease susceptibility in coastal populations. Coastal ecosystems, already stressed by sea level rise and extreme weather, are becoming hotspots for mosquito proliferation, further exacerbating public health risks. Summary The study highlights the growing threat of mosquito-borne diseases in India’s coastal regions due to climate change. Coastal populations are disproportionately affected as environmental changes create ideal conditions for mosquito breeding and disease spread. The findings underscore the need for targeted public health interventions, including enhanced surveillance, vector control, and community awareness programs. Long-term strategies to mitigate climate change impacts on coastal ecosystems are also essential to reduce future disease risks. This review provides critical insights and recommendations for managing mosquito-borne diseases in a changing climate.

The fluid flow between two parallel disks subjected to a low‐oscillating magnetic field has important applications in magnetohydrodynamic systems, where controlling the flow of electrically conducting fluid is essential. This setup is used in cooling systems for advanced electronics and nuclear reactors, where precise thermal management is required. In view of this, the current investigation explores the effect of a low‐oscillating magnetic field on the liquid flow with bio‐convection and moving motile microorganisms between two parallel disks. It is expected that the lower disk is rotating, while the upper disk is stretching. Additionally, the consequence of non‐uniform heat source/sink, non‐linear thermal radiation, and chemical reaction on the fluid flow is considered in the analysis. The current issue's governing partial differential equations (PDEs) are transformed into dimensionless ordinary differential equations (ODEs) using appropriate similarity variables. The resultant ODEs are numerically solved using Runge Kutta Fehlberg's fourth‐fifth order (RKF‐45) approach. The significance of several parameters on the various profiles is depicted with graphic illustrations. The results indicate that higher solid volume fraction and effective magnetization parameter enhance radial velocity while reducing tangential velocity. The thermal profile intensifies with the improvement of radiation and heat source/sink parameters. The microorganism profile drops with the increase in values of Lewis and Peclet numbers.

The present article investigates the domain of metric spaces, going beyond traditional bounds by presenting hexagonal suprametric spaces with the aim of extending upon the idea of hexagonal metric spaces (Tiwari and Sharma in Ann. Math. Comput. Sci. 6:35–48, 2022) and Branciari suprametric spaces (Tasneem Zubair in Results Nonlinear Anal. 7(3):80–93, 2024). By means of meticulous analysis and clarification, we illuminate the nuances of this recently established metric space and its elongated counterparts. The newly introduced metric is demonstrated through several illustrations, and its topology is examined. Through the application of well-known fixed point theorems to the framework of theorems about hexagonal suprametric spaces, we reveal a corollary that leads to symmetry requirements, which are required for the existence and uniqueness of fixed points with respect to self-operators in such a space. Eventually, by applying the theoretically proven fixed point theorems, this research examines the existence of solutions for the following nonlinear fractional differential equations with antiperiodic boundary conditions of order $\mathfrak{z} \in (3, 4]$: $\left \{ \begin{aligned} & ^{\mathfrak{c}}\mathsf{D}^{\mathfrak{z}}\varkappa (\mathfrak{t}) = \varrho (\mathfrak{t},\varkappa (\mathfrak{t})),\;\mathfrak{t}\in \mathsf{[0,T]}, \mathsf{T}>0, \\ &\varkappa (0) = -\varkappa (\mathsf{T}),\ \varkappa '(0) = - \varkappa '(\mathsf{T}),\ \varkappa ''(0) = -\varkappa ''(\mathsf{T}), \ \varkappa '''(0) = -\varkappa '''(\mathsf{T}), \end{aligned} \right .$ in which ϱ is a specified continuous function and ${}^{\mathfrak{c}}\mathsf{D}^{\mathfrak{z}}$ is the Caputo fractional derivative of order $\mathfrak{z}$. The theoretical result is demonstrated through an illustrative example presented in the concluding section of the article.

IoT‐enabled Wireless Sensor Networks (WSNs) and Vehicular Ad Hoc Networks (VANETs) utilize the Butterfly Optimization Algorithm (BOA) with K‐Means++ clustering to enhance data transmission, energy management, and real‐time communication. Signal processing in WSNs and VANETs faces challenges such as uneven energy distribution, suboptimal clustering, high latency, and reduced network lifetime, which are further complicated by scalability and dynamic topology in IoT environments. The methodology begins with initializing sensor and vehicular nodes, followed by K‐Means++ clustering to form energy‐efficient clusters, minimizing intra‐cluster distances and optimizing data aggregation. Cluster Heads (CHs) are selected based on residual energy, mobility, and proximity to ensure efficient data relay. BOA optimizes signal processing by mimicking butterfly behaviors through global and local searches, iteratively refining configurations to balance energy efficiency, latency, and signal quality. This hybrid approach enhances network performance by minimizing energy consumption, extending network lifetime, and improving real‐time data transmission. By leveraging BOA's optimization and K‐Means++'s effective cluster formation, the proposed model outperforms existing methods. Results indicate improved energy efficiency, reduced latency, superior signal quality, and enhanced vehicular communication stability in dynamic environments.

Two important performance measures in wireless communication are data rate and bandwidth. It is necessary to adjust wireless technology to achieve the highest possible data rate. Terahertz (THz) frequency is increasingly being used in a variety of applications because to its non-ionizing properties. THz technology can achieve high data rates of up to Tera bits per second (Tbps). The proposed antenna incorporates THz technology as well as a photonic crystal (PhC) structure to improve antenna performance. This work introduces a unique Double Pie Drilled Patch antenna (DPDPA) and investigates its characteristics using the antenna tool. This study optimizes the suggested antennas by adjusting the PhC air hole shapes, lattice value, and hole dimensions. The optimized DPDPA based on the rectangular air hole PhC model, produces −78.41 dB return loss (RL), 1.0002 VSWR, and 23.5 dBi gain at 2.10 THz frequency. Delivering a rapid data rate in wireless connections and other diverse streams is aided by the suggested patch antenna.

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and the fourth leading cause of cancer-related mortality. Despite the development of several therapeutic strategies to treat HCC, the highly refractory nature of this disease limits therapeutic outcomes and patient survival. Oncolytic viruses (OVs) are a type of natural virus that can specifically infect and kill human tumor cells in HCC and have been extensively investigated as attractive live therapeutic agents for the treatment of HCC. Engineered OVs are capable of inhibiting HCC progression through direct tumor lysis, perpetuation of apoptosis and infection-destruction cycle, activation of antitumor innate immunity and autoimmunity, disruption of tumor vasculature, induction of immunogenic cell death, and regulation of tumor microenvironment. However, HCC is a heterogeneous tumor and further preclinical or clinical studies are needed to focus on optimizing the route of administration and combination of OVs with other conventional therapies and, more importantly, to develop targeted delivery systems for better treatment of HCC while avoiding liver toxicity, as reviewed in this study.

The main aim of the current assay was to fabricate bioactive/interactive nanofibrous wound dressings with cytocompatibility, antibacterial, and antioxidant properties. Phytosynthesized silver nanoparticles (AgNPs) mediated via Momordica charantia extract (as the reducing and capping agent) were applied into polycaprolactone (PCL) electrospun nanofibers. Using scanning electron microscopy (SEM), transmission electron microscope (TEM), DLS, Zeta potential, energy‐dispersive X‐ray spectrometer (EDX), and X‐ray diffraction (XRD), we confirmed the phytosynthesis of AgNPs. Using SEM, we observed the morphology of PCL and PCL/AgNPs nanofibers, and the presence of AgNPs was confirmed. Characterization techniques showed that the fabricated nanofibrous wound dressings have a porous microstructure (with porosity in the range of 50%–60%), and PCL/AgNPs nanofibers exhibited acceptable hydrophilicity (with water contact angel degree of 53.0° ± 2.5°). The mean diameter of the unaltered polyurethane fibers was found to be 252 ± 55 nm, while the PCL nanofibers including AgNPs had an average diameter of 305 ± 29 nm. The biological evaluations showed that the fabricated nanofibrous wound dressings were cytocompatible (with toxicity of less than 10%) and exhibited antioxidant (dose dependent) and antibacterial activities. The WVTR for the control group (open container) was 46.0 ± 5.6 mg/cm² h, while the WVTR values for PCL nanofibers and PCL/AgNPs nanofibers were 8.3 ± 2.5 mg/cm² h and 8.6 ± 3.5 mg/cm² h, respectively. The hemolysis assay showed that the PCL nanofibers exhibited a hemolysis value of 14.3% ± 2.5%, and PCL/AgNPs exhibited a hemolysis value of 8.0% ± 3.1%. In vivo wound healing in streptozotocin (STZ)‐induced diabetes in the rat showed that applying the fabricated PCL/AgNPs nanofibers accelerated wound healing and induced a fully repaired wound. In contrast, the wounds of diabetic rats that received treatment with pure mats did not exhibit complete healing. Moreover, the PCL/AgNPs nanofibers increased hexosamine, collagen, and hydroxyproline contents and subsequently elevated HIF‐1α, TGF‐β1, and VEGF (angiogenesis factors contents). Thus, it can be inferred that utilizing the nanofibrous mat incorporating AgNPs may be deemed an appropriate intervention for wound dressing. This intervention can potentially expedite the healing duration and mitigate the risk of infection along the wound healing trajectory.

For improved efficiency and lower emissions in biodiesel fueled engine operation, either fuel properties or engine design parameters are adjusted. However, improving biodiesel characteristics is often difficult and expensive. In contrast, engine modifications are typically most cost effective and therefore more commonly used. One of the most important modifications is altering the geometry of the combustion chamber (CC), which has a vital influence on engine performance and emissions. Even slight changes in CC geometry have been shown to significantly improvement in the behaviour of biodiesel fueled engines. This review explores several CC geometries, such as shallow depth, trapezoidal, re-entrant, and toroidal etc. It addresses research gaps in prior studies by examining how these CC geometries affect the performance, emissions, and combustion characteristics of biodiesel powered engines. The review finds that the re-entrant bowl piston design is particularly effective for enhancing swirl and turbulent kinetic energy during the compression stroke. Toroidal combustion chambers (TCCs) are considered one of the most promising alternatives for biodiesel operation, as they reduce ignition delay by at least 10%. Compared to other geometries, TCCs enhance combustion, increase thermal efficiency, and lower fuel consumption, resulting in improved engine output. Shallow depth CC are less frequently investigated in research but may be suitable for large vehicles operating at low speeds. Since biodiesel-fueled engines are well-suited for heavy-duty, low-speed applications, shallow depth CC could offer an appropriate design solution.

This study aims to systematically investigate how friction stir processing (FSP) parameters—specifically tool geometry, rotational speed, and feed rate—influence the corrosion resistance of WE43/TiC surface composites. A methodical experimental approach was employed wherein WE43 magnesium (Mg) alloy plates were processed with incorporated TiC nanoparticles using various combinations of processing parameters, followed by comprehensive electrochemical testing in 3.5 wt% NaCl solution and detailed microstructural characterization. Results revealed that WE43/TiC surface composites exhibited significantly enhanced corrosion resistance compared to both processed and as-received WE43 substrates. The optimal combination for surface composites was achieved using a square tool at 1700 rev/min rotational speed and 60 mm/min feed rate, resulting in a 69.5% improvement in corrosion protection efficiency and the lowest corrosion current density (1.8 × 10⁻⁵ A/cm²). For processed WE43 substrates without reinforcement, the best corrosion resistance was observed with a square tool at 800 rev/min and 60 mm/min. Microstructural analysis demonstrated that enhanced corrosion resistance was primarily attributed to grain refinement and homogeneous distribution of TiC particles. A clear correlation was established between processing parameters, heat input, resultant microstructure, and corrosion behavior: controlled heat input led to finer grain structure, which subsequently improved corrosion resistance. This research provides critical insights into optimizing FSP parameters for WE43/TiC surface composites, establishing a robust approach for the electrochemical tailoring of Mg-based materials for corrosion-resistant applications. It presents a novel approach to customizing these materials for applications in corrosive environments by leveraging microstructural refinement through FSP. The findings emphasize the importance of carefully selecting process parameters to optimize corrosion resistance in WE43/TiC surface composites and their matrix substrates.