In this paper, an integrated thermoelectric (TE) and photovoltaic (PV) hybrid energy harvesting system (HEHS) is proposed for self-powered internet of thing (IoT)-enabled wireless sensor networks (WSNs). The proposed system can run at a minimum of 0.8 V input voltage under indoor light illumination of at least 50 lux and a minimum temperature difference, ∆T = 5 °C. At the lowest illumination and temperature difference, the device can deliver 0.14 W of power. At the highest illumination of 200 lux and ∆T = 13 °C, the device can deliver 2.13 W. The developed HEHS can charge a 0.47 F, 5.5 V supercapacitor (SC) up to 4.12 V at the combined input voltage of 3.2 V within 17 s. In the absence of any energy sources, the designed device can back up the complete system for 92 s. The sensors can successfully send 39 data string to the webserver within this time at a two-second data transmission interval. A message queuing telemetry transport (MQTT) based IoT framework with a customised smartphone application ‘MQTT dashboard’ is developed and integrated with an ESP32 Wi-Fi module to transmit, store, and monitor the sensors data over time. This research, therefore, opens up new prospects for self-powered autonomous IoT sensor systems under fluctuating environments and energy harvesting regimes, however, utilising available atmospheric light and thermal energy.

Conventional wireless sensor networks (WSNs) in smart home-building (SHB) are typically driven by batteries, limiting their lifespan and the maximum number of deployable units. To satisfy the energy demand for the next generation of SHB which can interconnect WSNs to make the internet of smart home-building (IoSHB), this study introduces the design and implementation of a 250 mW to 2.3 W energy harvesting device. The proposed device is dynamically autonomous owing to the integration of embedded solar photovoltaic (PV) modules and power storage through a supercapacitor (SC; 5 V, 0.47 F) capable of powering WSNs for 95 s (up to 4.11 V). The deployed device can harvest indoor and outdoor ambient light at a minimum illumination of 50 lux and a maximum illumination of 200 lux. Moreover, the proposed system supports wireless fidelity (Wi-Fi) and Bluetooth Low Energy (BLE) to do data transfer to a webserver as a complete internet of things (IoT) device. A customized android dashboard is further developed for data monitoring on a smartphone. All in all, this self-powered WSN node can interface with the users of the SHBs for displaying ambient data, which demonstrates its promising applicability and stability.

Solar photovoltaic (PV) panels are the best solution to reduce greenhouse gas emissions by fossil fuel combustion, with global capability now exceeding 714 GW due to rapid technological advances in solar panels (SPs). However, SPs’ efficiency and lifespan remain limited due to the absence of advanced fault-detection systems, and they are prone to short circuits (SC), open circuits (OC), and power degradation. Therefore, this large-scale production requires reliable, real-time fault diagnosis to maintain panel performance. However, traditional diagnostic methods implemented using MPPT, neural networks, or microcontroller-based systems often rely on complex computational algorithms and are not cost-effective. So, this paper proposes a diagnostic system composed of six functional blocks to address this issue. The proposed system was initially verified using an Intel DE-10 Lite FPGA board. Once its functionality was confirmed, an ASIC design was proposed for mass production, offering a significantly lower implementation cost and reduced hardware complexity than prior methods. Different circuit designs were developed for each of the six blocks. All designs were created using Cadence software and TSMC 180 nm technology files. The basic components used in these designs include PMOS transistors with 300 nm channel length and 2 µm width, NMOS transistors with 350 nm channel length and 2 µm width, as well as resistors and capacitors. Differential amplifiers with a gain of 40 dB were used for voltage and current sensing from the SP. The chip activation signal generator circuit was designed with an adjustable frequency and generated 120 MHz and 100 MHz signals in this work. The decision-making block, Logic Driver Circuit, was innovatively implemented using a reduced number of transistors. A custom memory block with a reset switch was also implemented to store the fault value detected at the SP. Finally, the proposed ASIC was implemented for fabrication, which is highly cost-effective in mass production and does not require complex computational stages.

Several carbon sequestration technologies have been proposed to utilize carbon dioxide (CO₂) to produce energy and chemical compounds. However, feasible technologies have not been adopted due to the low efficiency conversion rate and high-energy requirements. Process intensification increases the process productivity and efficiency by combining chemical reactions and separation operations. In this work, we present a model of a chemical-electrochemical cyclical process that can capture carbon dioxide as a bicarbonate salt. The proposed process also produces hydrogen and electrical energy. Carbon capture is enhanced by the reaction at the cathode that displaces the equilibrium into bicarbonate production. Literature data show that the cyclic process can produce stable operation for long times by preserving ionic balance using a suitable ionic membrane that regulates ionic flows between the two half-cells. Numerical simulations have validated the proof of concept. The proposed process could serve as a novel CO₂ sequestration technology while producing electrical energy and hydrogen.

The greenhouse effect, responsible for trapping heat in Earth’s atmosphere, has a parallel thermal phenomenon at the indoor scale known as the Room-Level Greenhouse Effect (RGHE), where solar radiation elevates room temperatures and increases energy consumption. The RGHE contributes to indoor temperature increases of 4–10 °C and elevates energy demands by 15–30% in high solar exposure zones, the effect being even worse in tropical zones. To address this problem, an innovative analog microarchitecture is proposed for real-time RGHE detection by sensing the sunlight intensity radiation factor (SIR). A compact analog system is introduced, comprising three stages: a Sensing Circuit Stage (SCS) that isolates the dynamic sunlight signal f (r) from static room condition factors (RCFs), an Amplification Stage (AS) that shifts and boosts the signal, and a Stabilized Peak Detection Stage (SPDS) that captures the peak solar intensity. The microsystem was tested across fixed f (m) levels of 0.75 V, 1.0 V, and 1.5 V, and varying f (r) values of 3 mV, 4 mV, and 5 mV. It successfully detects peak voltages ranging from 1.69 V to 1.92 V, with stabilization achieved within 60 µs, enabling accurate detection of the f (r) signal. The proposed microarchitecture offers a scalable approach to localized thermal monitoring in smart building environments using fully analog circuitry, designed and simulated in Cadence Virtuoso using the TSMC 180 nm technology library.