Nigeria is a nation endowed with both abundant renewable and non-renewable energy resources. Despite its vast potential, Nigeria struggles with a consistent power supply due to various systemic issues, such as inadequate funding, infrastructural decay, corruption, technical skill shortages, and macroeconomic instability. These challenges hinder the effective harnessing and distribution of energy resources, particularly renewable ones like wind, solar, biomass, and hydropower. This study assesses the existing energy policies and their efficacy in promoting sustainable energy development towards achieving universal electricity access by 2030. It highlights the necessity for a just energy transition that integrates a substantial proportion of renewable energy into the national grid, aiming to meet up to 60% of the country’s energy demands with clean sources by 2050. This transition is critical not only for energy security and reducing the environmental impact but also for fostering socioeconomic equity. Recommendations include overhauling the legal and regulatory frameworks to support renewable energy growth, particularly in off-grid areas, to ensure clean, affordable, and secure energy access. Strategic investments, enhanced infrastructure, and robust public–private partnerships are essential to overcome the current barriers and realize Nigeria’s energy potential. This paper calls for a comprehensive approach that addresses both the technical and socioeconomic dimensions of the energy crisis, laying the groundwork for a sustainable and prosperous energy future for Nigeria.

We investigated the effect of reduced graphene oxide (rGO) doping on the birefringence of 5CB liquid crystal (LC). The characteristics of the synthesized rGO and LC-rGO composite with different rGO concentrations were analyzed by atomic force microscopy, X-ray photoelectron spectroscopy, white light polarized microscopy, voltage-dependent transmission measurement, and differential scanning calorimetry. We found that doping LC with an appropriate concentration of rGO enhances the birefringence of the LC. This is mainly due to the improved anisotropy of polarizability, which stems from the high shape anisotropy of rGO. However, the aggregation of rGO reduces the birefringence by decreasing the anisotropy of polarizability as well as the order parameter. Our study shows the promising potential of LC-rGO for developing various electro-optic devices that offer improved electro-optic effects.

This review explores the integration of graphene and liquid crystals to advance sensor technologies across multiple domains, with a focus on recent developments in thermal and infrared sensing, flexible actuators, chemical and biological detection, and environmental monitoring systems. The synergy between graphene’s exceptional electrical, optical, and thermal properties and the dynamic behavior of liquid crystals leads to sensors with significantly enhanced sensitivity, selectivity, and versatility. Notable contributions of this review include highlighting key advancements such as graphene-doped liquid crystal IR detectors, shape-memory polymers for flexible actuators, and composite hydrogels for environmental pollutant detection. Additionally, this review addresses ongoing challenges in scalability and integration, providing insights into current research efforts aimed at overcoming these obstacles. The potential for multi-modal sensing, self-powered devices, and AI integration is discussed, suggesting a transformative impact of these composite sensors on various sectors, including health, environmental monitoring, and technology. This review demonstrates how the fusion of graphene and liquid crystals is pushing the boundaries of sensor technology, offering more sensitive, adaptable, and innovative solutions to global challenges.

Increasing the use of renewable energy, particularly photovoltaic (PV) systems, is essential for mitigating climate change. However, the intermittent nature of PV power generation creates challenges in accurately forecasting and managing electricity supply within grid systems. This study proposes a hybrid deep learning (DL) model combining improved harmony search (IHS) optimization, convolutional neural networks (CNNs), and long short‐term memory (LSTM) networks (IHS–CNN–LSTM) for forecasting the 15‐min power output of grid‐connected monocrystalline and polycrystalline PV systems. The model uses 14 input features obtained from numerical weather prediction (NWP) and local measurement data (LMD), with data sourced from the Public Photovoltaic Output Dataset (PVOD) covering June 2018 to June 2019 in Hebei Province, China. Comparative evaluations against genetic algorithm‐based (GA–CNN–LSTM), differential evolution‐based (DE–CNN–LSTM), and conventional CNN–LSTM models showed that the IHS–CNN–LSTM provided superior forecasting accuracy. Specifically, the proposed model achieved reductions in root mean square error (RMSE) of 3.7% for polycrystalline and 1.8% for monocrystalline PV systems, and reductions in mean absolute error (MAE) of 2.6% and 1.2%, respectively, along with high R ² values of 98% and 99%. The results confirm the effectiveness and accuracy of the proposed hybrid approach for PV power output forecasting.