pJ Of Energy Research Articles

Single-beam low-frequency loss modulation technique for two-photon absorption measurement

We present a straightforward and reliable single-beam configuration designed for the assessment of two-photon absorption (2PA) through the utilization of the loss modulation technique. Mechanical rotation of a half-wave plate induces a sinusoidal modulation in the envelope of the probe pulse train. The elimination of spurious signals is achieved by determining the difference of the lock-in amplifier output at the focused and defocused positions of the sample. Our findings, based on measurements with dye solutions and crystalline silicon, demonstrate the feasibility of determining the 2PA coefficient, subject to preliminary calibration of the setup with reference samples. Remarkably, the acquisition of the 2PA signal in silicon at 1030 nm is accomplished with less than 200 pJ energy of the laser pulse, overcoming the challenge posed by concurrent linear absorption. Additionally, a signal of 3PA is revealed despite the presence of 2PA and linear absorption.

Towards Energy-Efficient Spiking Neural Networks: A Robust Hybrid CMOS-Memristive Accelerator

Spiking Neural Networks (SNNs) are energy-efficient artificial neural network models that can carry out data-intensive applications. Energy consumption, latency, and memory bottleneck are some of the major issues that arise in machine learning applications due to their data-demanding nature. Memristor-enabled Computing-In-Memory (CIM) architectures have been able to tackle the memory wall issue, eliminating the energy and time-consuming movement of data. In this work we develop a scalable CIM-based SNN architecture with our fabricated two-layer memristor crossbar array. In addition to having an enhanced heat dissipation capability, our memristor exhibits substantial enhancement of 10% to 66% in design area, power and latency compared to state-of-the-art memristors. This design incorporates an inter-spike interval (ISI) encoding scheme due to its high information density to convert the incoming input signals into spikes. Furthermore, we include a time-to-first-spike (TTFS) based output processing stage for its energy-efficiency to carry out the final classification. With the combination of ISI, CIM and TTFS, this network has a competitive inference speed of 2μs/image and can successfully classify handwritten digits with 2.9mW of power and 2.51pJ energy per spike. The proposed architecture with the ISI encoding scheme can achieve ∼10% higher accuracy than those of other encoding schemes in the MNIST dataset.

Assessment of the energy potential of agricultural residues in the Canary Islands: Promoting circular economy through bioenergy production

The Canary Islands, renowned for their tourism appeal, grapple with a pronounced energy challenge stemming from their geographic fragmentation and heavy reliance on external energy sources, particularly fossil fuels. Moreover, the region's history is marked by recurring energy crises, underscoring the imperative need for a swift and decisive transition towards renewable energy sources. This research examines the potential of waste biomass as a renewable energy source in the Canary Islands. This reliance on non-renewable energy sources leads to increased energy costs and greenhouse gas emissions, making the pursuit of a sustainable energy future a strategic priority in the region. This study proposes to assess the potential of residual biomass from the main crops of the Canary Islands, such as bananas, tomatoes, potatoes, and grapes, for bioenergy production. Statistical data were used to estimate the total availability of residues, while parameters like residue-to-product ratio, moisture content and calorific value were determined to calculate the energy potential of the residual biomass. The research findings reveal that the annual production of residual biomass from the primary crops in the Canary Islands is 234,744 tonnes (111,425 Tn/year from banana cultivation, 52,961 Tn/year from tomato cultivation, 40,102 Tn/year from potato cultivation, and 30,256 Tn/year from grape cultivation). This biomass has the potential to generate around 1.39 PJ of energy, equivalent to approximately 4.79% of the current energy consumption of the archipelago. Considering that renewable sources contribute to only around 20% of the energy production in the Canary Islands, the availability of this resource presents an exceptional opportunity for the region. By harnessing its natural resources, the Canary Islands can pursue sustainable economic growth while simultaneously mitigating negative environmental impacts. Definitely, the assessment of the potential of residual biomass plays a crucial role in evaluating sustainable energy resources and represents an important step towards a more sustainable energy future in the Canary Islands.

Toward integrated tantalum pentoxide optical parametric oscillators.

We present a hybrid waveguide-fiber optical parametric oscillator (OPO) exploiting degenerate four-wave mixing in tantalum pentoxide. The OPO, pumped with ultrashort pulses at 1.55 µm wavelength, generated tunable idler pulses with up to 4.1 pJ energy tunable center wavelength between 1.63 µm and 1.68 µm. An upper bound for the total tolerable cavity loss of 32 dB was found, rendering a chip-integrated OPO feasible as a compact and robust light source.

Renewable Energy Potential and CO2 Performance of Main Biomasses Used in Brazil

This review investigates the effects of the Brazilian agriculture production and forestry sector on carbon dioxide (CO2) emissions. Residual biomasses produced mainly in the agro-industrial and forestry sector as well as fast-growing plants were studied. Possibilities to minimize source-related emissions by sequestering part of carbon in soil and by producing biomass as a substitute for fossil fuel were extensively investigated. The lack of consistency among literature reports on residual biomass makes it difficult to compare CO2 emission reductions between studies and sectors. Data on chemical composition, heating value, proximate and ultimate analysis of the biomasses were collected. Then, the carbon sequestration potential of the biomasses as well as their usability in renewable energy practices were studied. Over 779.6 million tons of agricultural residues were generated in Brazil between 2021 and 2022. This implies a 12.1 million PJ energy potential, while 4.95 million tons of forestry residues was generated in 2019. An estimated carbon content of 276 Tg from these residues could lead to the production of approximately 1014.2 Tg of CO2. Brazilian biomasses, with a particular focus on agro-forest waste, can contribute to the development of sustainable alternative energy sources. Moreover, agro-waste can provide carbon credits for sustainable Brazilian agricultural development.

Availability of Biomass and Potential of Nanotechnologies for Bioenergy Production in Jordan

Jordan’s energy situation is in a critical state of dependency, with the country relying heavily on imports to satisfy its ever-increasing energy requirements. Renewable energy is a more competitive and consistent source of energy that can supply a large proportion of a country’s energy demand. It is environmentally friendly and minimizes atmospheric pollutant emissions. Thus, bioenergy has the potential to be a crucial alternative energy source in Jordan. Biomass is the principal source of bioenergy; it accounts for approximately 13% of the primary energy demand and is anticipated to supply half of the total primary energy demand by 2050. Nanotechnology has emerged as an important scientific research area with numerous applications, including biofuels. This review summarizes the application of nanoparticles to improve the properties and processes of biofuels. It presents the availability and viability of nanotechnology-supported bioenergy production in Jordan. Jordan generates up to 5.8 million tons of biomass each year and has access to abundant nonedible plant resources (such as Jojoba, Handal, and Jatropha). The theoretical energy potential of waste and residue available in Jordan was also assessed; it was discovered that the 1.28 million tons of dry crop residues (vegetables, fruits, and farming crops) could generate 6.8 PJ of energy per year and that biogas could be generated at a rate of 817 MCM/year

Design of Leaky Integrate and Fire Neuron for Spiking Neural Networks Using Trench Bipolar I-MOS

In this paper, using calibrated 2-D TCAD simulations we report a trench Bipolar I-MOS for the realization of a spiking neural network. We demonstrated that the proposed trench Bipolar I-MOS LIF neuron can emulate the biological neuronal nature and exhibits a low threshold voltage (-0.16 V), which is ∼ |400 mV| lower than the past reported LBIMOS LIF neuron. Moreover, the trench Bipolar I-MOS neuron consumes 0.35 pJ energy per spike, which is ∼ 100x lower in comparison to the PDSOI LIF neuron. Further, the proposed LIF neuron shows ∼ 10x reduction in energy per spike than the recently published Ge MOSFET and JLFET based LIF neurons. In addition, the proposed trench Bipolar I-MOS LIF neuron exhibits ∼ 6 orders high spiking frequency than the biological neuron. Also, the proposed device shows a ∼ 1.1x reduction in the breakdown voltage as compared to the conventional Bipolar I-MOS. This is due to the crowding of the electric field near the gate edges.

Tackling climate change through circular economy in cities

Circular economy principles can lead to a series of opportunities to tackle climate change, particularly in cities of developing countries where the generation of waste, consumption and demand for energy is growing rapidly. This paper aims to advance the understanding of the expansion of circularity for tackling climate change using biomass for energy at the urban scale. In addition, little is known about how Circular Economy (CE) flows can be quantitatively integrated, what potential trade-offs exist in promoting Circular Economy using biomass, and how governance models can integrate these flows and contribute to a Circular Economy transition in cities. Based on an empirical study of a city in India, where the private sector participation through an innovative Public-Private Partnership (PPP) model enabled the use of urban wastewater in thermal power plants, the study presents the viability of Circular Economy models at the city scale. The feasibility of a further expansion of this circularity by co-firing biomass in local thermal power plants was analyzed, as a potential solution for reducing pollution and carbon emissions in a hard-to-abate sector. The result indicated an average 20.5 MJ kg−1 calorific value for torrefied mixed biomass pellets from crop residues, which, if applied to about 447 kt y−1 surplus biomass available in Nagpur would amount to 9.18 PJ of energy (per year). Overall, local power plants demanded about 19.85 Mt of coal in 2017–18, equivalent to 297.8 PJ in that fiscal year. Thus, the supply of torrefied pellets from peri-urban crop residues can meet between 2 and 3% of the demand from coal-fired power plants, resulting in a 0.3% decrease in the emissions from the power sector in India in 2017–18 (i.e., 922 MtCO2), benefiting different sectors and levels of government. The analysis provides a framework for understanding the technical and institutional possibilities for strengthening the relationship between circular economy and climate change in the cities of developing countries by connecting flows and systems within and beyond urban centers. Therefore, the expansion of Circular Economy in cities can occur by integrating urban sectors and policy coordination at different levels of governance. Partnerships between local governments, utilities, and civil society for co-managing energy, water, and waste in cities can reduce the financial burden on national governments, including meeting national emission targets.

Emulating low power nociceptive functionalities with a forming-free SiO2/VOx conductive bridge memory with Pt nanoparticles

The fabrication of low-power and scalable electronic devices that will have the ability to emulate the properties of the biological nociceptors is of great importance for the development of humanoid robots. Along these lines, in this work, an artificial nociceptive element composed of a SiO2/VOx-based bilayer configuration and a dense layer of Pt nanoparticles (NPs) as a bottom electrode is proposed. Interestingly, the device operates only under the threshold switching mode with the switching voltage as low as ∼220 mV and a huge switching ratio of 107. A systematic analysis of the impact of the bilayer configuration and the existence of the Pt NPs on the total memory performance is also provided, while a comprehensive numerical model is introduced to highlight the crucial role of the electrode material on the local temperature distribution and its influence on the memristive effect. On top of that, the proposed structure can imitate the normal, relaxation, and sensitization states of the nociceptors with about 0.3 pJ energy per spike. These enhanced properties are ascribed to the self-rupture of the Ag-based conducting filament, whereas valuable insights into the impact of the local temperature distribution on the switching dynamics are provided.

Sustainable utilization of bamboo through air-steam gasification in downdraft gasifier: Experimental and simulation approach

This study presents a detailed analysis to utilize bamboo which is a widely available feedstock in North-Eastern states of India, via air-steam gasification with an estimated potential to generate 23.92 PJ energy. The study was carried out in a downdraft gasifier (10 kWe) to produce hydrogen-rich syngas. The effect of the steam to biomass ratio (0.1–0.4) on the gas composition and performance parameters is studied. The maximum H2 yield (37.12%) is found in the steam to biomass ratio of 0.35. The lower heating value and cold gas efficiency are in the range of 4.72–5.71 MJ/Nm3 and 62.1–85.7%, respectively. The higher steam to biomass ratio of above 0.35 led to cooling of combustion and reduction zone and negatively affected the performance parameters of the gasifier. The experimental data of the present study is validated with model data and root mean square error is found in the range of 4.24–7.76 and 2.88–10.08 from experimental data from literature with the developed model. Results show that it is economical and viable to use small-scale gasifiers to produce syngas from bamboo to meet the local energy demand. This is of importance in small and isolated communities that are near bamboo forest.

A Voltage-Controlled, Oscillation-Based ADC Design for Computation-in-Memory Architectures Using Emerging ReRAMs

Conventional von Neumann architectures cannot successfully meet the demands of emerging computation and data-intensive applications. These shortcomings can be improved by embracing new architectural paradigms using emerging technologies. In particular, Computation-In-Memory (CiM) using emerging technologies such as Resistive Random Access Memory (ReRAM) is a promising approach to meet the computational demands of data-intensive applications such as neural networks and database queries. In CiM, computation is done in an analog manner; digitization of the results is costly in several aspects, such as area, energy, and performance, which hinders the potential of CiM. In this article, we propose an efficient Voltage-Controlled-Oscillator (VCO)–based analog-to-digital converter (ADC) design to improve the performance and energy efficiency of the CiM architecture. Due to its efficiency, the proposed ADC can be assigned in a per-column manner instead of sharing one ADC among multiple columns. This will boost the parallel execution and overall efficiency of the CiM crossbar array. The proposed ADC is evaluated using a Multiplication and Accumulation (MAC) operation implemented in ReRAM-based CiM crossbar arrays. Simulations results show that our proposed ADC can distinguish up to 32 levels within 10 ns while consuming less than 5.2 pJ of energy. In addition, our proposed ADC can tolerate ≈30% variability with a negligible impact on the performance of the ADC.

Truncation and Rounding-Based Scalable Approximate Multiplier Design for Computer Imaging Applications

Advanced technology used for arithmetic computing application, comprises greater number of approximate multipliers and approximate adders. Truncation and Rounding-based Scalable Approximate Multiplier (TRSAM) distinguish a variety of modes based on height (h) and truncation (t) as TRSAM (h, t) in the architecture. This TRSAM operation produces higher absolute error in Least Significant Bit (LSB) data shift unit. A new scalable approximate multiplier approach that uses truncation and rounding TRSAM (3, 7) is proposed to increase the multiplier accuracy. With the help of foremost one bit architecture, the proposed scalable approximate multiplier approach reduces the partial products. The proposed approximate TRSAM multiplier architecture gives better results in terms of area, delay, and power. The accuracy of 95.2% and the energy utilization of 24.6 nJ is observed in the proposed multiplier design. The proposed approach shows 0.11%, 0.23%, and 0.24% less Mean Absolute Relative Error (MARE) when compared with the existing approach for the input of 8-bit, 16-bit, and 32-bit respectively. It also shows 0.13%, 0.19%, and 0.2% less Variance of Absolute Relative Error (VARE) when compared with the existing approach for the input of 8-bit, 16-bit, and 32-bit respectively. The proposed approach is implemented with Field-Programmable Gate Array (FPGA) and shows the delay of 3.640, 6.481, 12.505, 22.572, and 36.893 ns for the input of 8-bit, 16-bit, 32-bit, 64-bit, and 128-bit respectively. The proposed approach is applied in digital filters design which shows the Peak-Signal-to-Noise Ratio (PSNR) of 25.05 dB and Structural Similarity Index Measure (SSIM) of 0.98 with 393 pJ energy consumptions when used in image application. The proposed approach is simulated with Xilinx and MATLAB and implemented with FPGA.

Low-Carbon Clean Technology for Waste Energy Recovery in Power Plants Based on Green Environmental Protection

With the development of biomass power generation technology, biomasswaste has a more excellent recycling value. The article establishes a biomasswaste inventory model based on the material flow analysis method andpredicts raw material waste’s energy utilization potential. The results showthat the amount of biomass waste generated from 2016 to 2020 is on therise. In 2020, biomass waste’s energy utilization can reach 107,802,300 tons,equivalent to 1,955.28PJ of energy. Through biomass energy analysis andemission analysis, the results show that the biomass waste can generate182.02 billion kW·h in 2020, which can replace 35.9% of the region’s totalpower consumption, which is compared with the traditional power generationmethod under the same power generation capacity. Power generation can reduce SO2 emissions by 250,400 tons, NOx emissions by 399,300 tons,and PM10 emissions by 49,700 tons. Reduce direct economic losses by712 million yuan. Therefore, Chinese promotion of the recycling of biomasswaste and the acceleration of the biomass energy industry’s development isof great significance for reducing pollutant emissions and alleviating energypressure.

A Highly Scalable Junctionless FET Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

In this article, a highly scalable and CMOS compatible double-gate junctionless field-effect transistor (DG-JLFET)-based leaky integrate-and-fire (LIF) neuron is presented for the sub-20 nm gate length which is the smallest reported until now. Using well-calibrated 2-D TCAD simulations, we demonstrated that DG-JLFET LIF is able to mimic biological neuronal behavior. The DG-JLFET LIF neuron shows a low threshold voltage ( -0.31 V) for firing a spike and requires 1.14 pJ of energy per spike which is ~ 32× less than partially depleted silicon-on-insulator (PD-SOI) MOSFET LIF neuron. The proposed neuron needs only 0.4 V of supply voltage that is ~ 7×, 7.5×, 5×, and 2× lower as compared to its counterpart PD-SOI MOSFET, FinFET, L-shaped bipolar impact ionization MOS (LBIMOS), and Si NIPIN Diode-based LIF neurons, respectively. In addition, at a gate length of 10 nm the DG-JLFET LIF requires 0.07 pJ of energy per spike, which is ~ 500×, ~ 642×, and ~ 86× lower than that of the PD-SOI MOSFET, single MOSFET and Biristor based LIF neurons, respectively. Moreover, the DG-JLFET LIF neuron shows spiking frequency in the range of megahertz, which is ~5 orders high compared to the biological neuron. The absence of metallurgical junctions in DG-JLFET eases the fabrication complexity and cuts down the thermal budget requirement.

A Dual-Mode In-Memory Computing Unit Using Spin Hall-Assisted MRAM for Data-Intensive Applications

The emerging big data application poses many non-traditional challenges to a conventional computing system. The processing of a large amount of data in a conventional computing system involves frequent data movement between the processor and the memory. As a result, power consumption and latency of the system increases, most commonly known as the von Neumann bottleneck. To address this von Neumann bottleneck, processing in memory is considered one of the most promising solutions for energy-efficient computing in data-intensive applications. Therefore, in this work, we propose an in-memory computing unit based on emerging non-volatile magnetic random access memory (MRAM), which can perform computations directly within the memory. We call the proposed memory unit computational MRAM (C-MRAM). The proposed array works in two modes: 1) memory mode, where it acts as standard memory with the additional advantage of non-volatility, and 2) logic mode, where it performs the computation inside the memory array. To demonstrate the benefits of the proposed C-MRAM array, we realize an approximate adder (Ax-ADD) for energy-constraint applications in which further power saving is achieved by relaxing the demand for computational accuracy. Furthermore, we propose to use the C-MRAM array in hybrid multi-bank architecture, which offers the flexibility of reconfiguring the proposed C-MRAM as a memory array or logic array to meet the requirement of a wide range of applications. The simulation result shows that the proposed array can efficiently perform memory, logic, and arithmetic operation in 2, 4, and 6 ns while consuming 0.2, 0.47, and 0.7 pJ of energy.

Reducing Dynamism in DRAM With Bistable MEMS Switch as Sleep Transistor

We demonstrate the bistable microelectromechanical system (MEMS) sleep-transistor for CMOS integrated circuits (ICs). The sleep transistor has infinite sleep mode resistance and ~2.5-Ω resistance in active mode. The hold bias in active mode is zero for the bistable MEMS sleep transistor. The proposed bistable MEMS switch is an ideal candidate for gating the power-hungry digital circuits. The dynamic random access memory (DRAM) supply rails gated with bistable MEMS sleep transistors offer ~6 order reduction in refresh rate. This article also reports on the design guidelines for the CMOS compatible fabrication of bistable MEMS sleep transistor. Only ~0.31 pJ of energy is consumed, in toggling between the modes of bistable MEMS sleep transistor. The MEMS gate can drive large area CMOS circuit blocks with fuse current density of ~0.5 mA/μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Furthermore, the fabrication of the bistable MEMS sleep transistor is critical in the aspect that its design is a play between restoring and adhesion forces.

Prospects of Bioenergy Production for Sustainable Rural Development in Ghana

Biomass supplies about 80% of the energy needs for cooking and heating in rural Ghana. It is predominantly used in traditional and inefficient forms (firewood and charcoal), which presents environmental and health concerns. In order to better the living standard in rural Ghana, efforts must be made to provide modern energy services. Most rural communities in Ghana are so remote that an extension of the national grid is uneconomical, hence biomass electricity provides a viable alternative. Biomass is pivotal to the socio-economic development of rural Ghana due to its easy accessibility and enormous potential in the production of varied energy forms. In this paper, a comprehensive review of biomass resources, biomass energy conversion technologies and bioenergy production potential for rural development in Ghana is provided. The most important feedstock from an energy perspective was found to be crop residues. Based on 2017 statistics, Ghana has a theoretical potential of 623.84 PJ of energy from agricultural crop residues and 64.27 PJ of energy from livestock production. Evidence from literature suggests that biomass gasification is the best conversion technology to expand electricity access rate for rural households in Ghana. The paper concludes that although ample biomass resources exist, cocoa pod husks (CPH) which is very common in rural Ghana can be pelletized and used as feedstock for rural power generation systems.

Trust-Based Opportunistic Network Offloaders for Smart Agriculture

With the enormous use of internet of things-based devices for enabling smart agriculture, there is a significant need for efficient systems in order to improve agricultural practices. It can help efficiently to develop optimal web-based information system using the data of field monitoring. But, the collection of such data in the presence of connectivity disruptions poses new challenges for users. This paper targets to determine such offloaders with less infrastructural costs to enable smart agriculture based on network heuristics. Although, few works contribute to the trust established, most of them are applicable only for static networks. This paper explores a trust-based solution for mobile data offloading. This paper identifies the need and impact of trust determination using the trust model algorithm. The proposed algorithm outperforms the hybrid trust-based mobility aware clustering algorithm for trust-based offloaders with up to 13% better offloading potential saving a minimum of 8 pJ energy per user with just 25% contributors with 50% lesser time delay.

A Dual-Bit Spin-Based Analog-to-Digital Converter With Double-Check Estimation

This letter presents a three-bit magnetic analog-to-digital converter (MADC) based on the spin Hall effect, which exploits the spin current flowing at both sides of a heavy metal (HM) layer. The shape of the HM can be adjusted to produce multiple switching thresholds. After the input signal is injected, the magnetization of both free layers in the top and bottom magnetic tunnel junctions (MTJs) can be switched simultaneously. The converted results could be quantified by the states of the corresponding MTJs. In order to improve the accuracy and reliability of quantization, a multilayer structure with a double-check estimation method inspired by or gate operation is designed. This dual-bit MADC can reach up to 667 MHz sampling rate with only 0.17 pJ energy consumption, which is well suited for nonvolatile computing based on magnetic devices.

Land–water–energy nexus in agricultural management for greenhouse gas mitigation

Agriculture plays an important role in global climate change. The interaction and efficiency of use of land, water, and energy in agricultural activities are the principal factors affecting greenhouse gas (GHG) emissions and food production. However, comprehensive analysis exploring the mechanism of the land–water–energy system in agricultural production remains lacking. This study developed such a framework based on regional agricultural GHG emissions by combining top-down analysis that considered cross-sectoral interactions with bottom-up analysis that addressed the context-specific conditions of resources and technology. We employed the proposed framework to analyze the interaction of land–water–energy and factors influencing agricultural GHG emissions and to explore mitigation measures based on a case study of the Sanjiang Plain (China). Results showed cropland on the Sanjiang Plain produced 1.8 million tonnes of protein and released 10.9 million tonnes of CO2eq in 2015 using 3.0 million ha of arable land, 12.1 billion m3 of water, and 100.4 PJ of energy. Owing to their high input of resources and flooded cultivation, rice fields produced 29% of total crop protein but consumed 51% of total crop water use, 43% of total crop energy use, and emitted 54% of total crop GHG (CO2eq). Structural adjustment through conversion of half the paddy fields into dryland crops (e.g., wheat) could mitigate GHG emissions by 18.8% in 2020 compared with the baseline scenario. However, such change would be almost impossible given the Sanjiang Plain is one of China’s most important rice-producing areas. If integrated technology improvements were adopted, e.g., advanced crop–soil nutrition management, groundwater protection measures, water-saving irrigation technology, and low-carbon energy technology, GHG emissions could be reduced by 23.9% without sacrificing food production. This study used the nexus approach to analyze agricultural GHG emissions, providing a framework for sustainable agricultural management and a reference for understanding the land–water–energy nexus.