The ever-increasing global energy demand necessitates the development of efficient, sustainable, and high-performance energy storage systems. Nanotechnology, through the manipulation of materials at the nanoscale, offers significant potential for enhancing the performance of energy storage devices due to unique properties such as increased surface area and improved conductivity. This review paper investigates the crucial role of nanotechnology in advancing energy storage technologies, with a specific focus on capacitors and batteries, including lithium-ion, sodium–sulfur, and redox flow. We explore the diverse applications of nanomaterials in batteries, encompassing electrode materials (e.g., carbon nanotubes, metal oxides), electrolytes, and separators. To address challenges like interfacial side reactions, advanced nanostructured materials are being developed. We also delve into various manufacturing methods for nanomaterials, including top–down (e.g., ball milling), bottom–up (e.g., chemical vapor deposition), and hybrid approaches, highlighting their scalability considerations. While challenges such as cost-effectiveness and environmental concerns persist, the outlook for nanotechnology in energy storage remains promising, with emerging trends including solid-state batteries and the integration of nanomaterials with artificial intelligence for optimized energy storage.

Lithium-ion batteries (LIBs) have become the leading energy storage technology because of their high specific energy, excellent efficiency, and longer lifespan. This review offers a comprehensive overview of the lithium battery industry, covering lithium materials and the global supply chain, as well as examining traditional and sustainable extraction methods. The discussion includes the technical and environmental challenges of each extraction method, with particular emphasis on feedstock selection, which greatly influences lithium recovery efficiency, yield, and ecological impact, and capital and operating expenditures. It also investigates the growth of the global battery recycling industry and provides an outlook on innovations in this area. These innovations include recycling systems, a case study on Biomass Energy Systems Inc., and the battery chemistries needed to support a circular economy. The review highlights industrial applications of LIBs in sectors such as automotive, consumer electronics, and aerospace. Finally, it addresses supply chain and recycling challenges related to LIBs, positioning cost, environmental footprint, and regulatory compliance as central considerations for the future of the battery industry.

Rail trespassing remains a persistent safety challenge at the system level in the United States, with a 24% increase in incidents within the last decade (2016–2025). Identifying hotspots proactively is difficult due to limited incident data and strong spatial dependencies within the built environment. This study thus creates a ZIP-code–level geospatial analytics framework to identify current and emerging trespassing hotspots across North Carolina by combining land-use composition, rail exposure metrics, and historical Federal Railroad Administration (FRA) trespassing records. Geospatial layers were integrated within a GIS workflow to derive attributes such as rail miles, grade crossings, population density, and land-use types. Exploratory spatial analysis showed significant clustering of trespassing incidents, with Global Moran’s I indicating positive spatial autocorrelation across multiple neighborhood sizes. Permutation z-scores confirmed non-random hotspot formation along major rail corridors. A k-means clustering method also identified four structural risk environments, and a Composite Risk Index (CRI) was developed from weighted, standardized exposure and land-use variables to quantify latent risk, independent of raw casualty counts. Results indicate that clusters characterized by higher rail infrastructure exposure and mixed land-use environments exhibit the highest CRI values and elevated hotspot probabilities. In contrast, clusters with limited rail infrastructure, including predominantly commercial and rural ZIP codes, show substantially lower risk levels. The findings highlight that trespassing risk is more strongly associated with structural exposure conditions than with isolated historical incident counts. The resulting risk surfaces and hotspots provide an interpretable and scalable framework for statewide safety planning, early hotspot detection, and targeted interventions by transportation agencies.

Rail trespassing remains a persistent safety challenge at the system level in the United States. However, identifying hot spots proactively is difficult due to limited incident data and strong spatial dependencies within the built environment. This study creates a ZIP-code–level geospatial analytics framework to identify current and emerging trespassing hot spots across North Carolina by combining land-use composition, rail exposure metrics, and historical Federal Railroad Administration (FRA) trespassing records. Geospatial layers were integrated within a GIS workflow to derive predictors such as rail miles, grade crossings, crossings per mile, population density, and land-use types encoded as one-hot vectors. Exploratory spatial analysis showed significant clustering of trespassing incidents, with Global Moran’s I indicating positive spatial autocorrelation across multiple neighborhood sizes. Permutation z-scores confirmed non-random hotspot formation along major rail corridors. A k-means clustering method identified four structural risk environments, and a Cluster Risk Index (CRI) was developed from weighted, standardized exposure and land-use variables to quantify latent risk, independent of raw casualty counts. Results demonstrate that dense urban–industrial rail corridors have the highest CRI values and exhibit the strongest spatial autocorrelation. In contrast, rural ZIP codes with long rail lines show increased exposure-based risk despite fewer historical casualties. The resulting risk surfaces and hotspot classifications provide an interpretable and scalable framework for statewide safety planning, early hotspot detection, and targeted interventions by transportation agencies.