Search Results (466)

Direct Air Capture (DAC) is gaining worldwide attention as a negative emissions strategy critical to meeting climate targets. Among emerging DAC materials, pyrolysis chars (PCs) and gasification chars (GCs) derived from biomass present a promising pathway due to their tunable porosity, surface chemistry, and low-cost feedstocks. This review critically examines the current state of research on the physicochemical properties of PCs and GCs relevant to CO₂ adsorption, including surface area, pore structure, surface functionality and aromaticity. Comparative analyses show that chemical activation, especially with KOH, can significantly improve CO₂ adsorption capacity, with some PCs achieving more than 308 mg/g (100 kPa CO₂, 25 °C). Additionally, nitrogen and sulfur doping further improves the affinity for CO₂ through increased surface basicity. GCs, although inherently more porous, often require additional modification to achieve a similar adsorption capacity. Importantly, the long-term stability and regeneration potential of these chars remain underexplored, but are essential for practical DAC applications and economic viability. The paper identifies critical research gaps related to material design and techno-economic feasibility. Future directions emphasize the need for integrated multiscale research that bridges material science, process optimization, and real-world DAC deployment. A synthesis of findings and a research outlook are provided to support the advancement of carbon-negative technologies using thermochemically derived biomass chars. Full article

The exponential proliferation of the Internet of Things (IoT) and optical IoT (O-IoT) has introduced substantial challenges concerning computational capacity and energy efficiency. IoT devices generate vast volumes of aggregated data and require intensive processing, often resulting in elevated latency and excessive energy consumption. Task offloading has emerged as a viable solution; however, many existing strategies fail to adequately optimize both latency and energy usage. This paper proposes a novel task-offloading approach based on deep Q-network (DQN) learning, designed to intelligently and dynamically balance these critical metrics. The proposed framework continuously refines real-time task offloading decisions by leveraging the adaptive learning capabilities of DQN, thereby substantially reducing latency and energy consumption. To further enhance system performance, the framework incorporates optical networks into the IoT–fog–cloud architecture, capitalizing on their high-bandwidth and low-latency characteristics. This integration facilitates more efficient distribution and processing of tasks, particularly in data-intensive IoT applications. Additionally, we present a comparative analysis between the proposed DQN algorithm and the optimal strategy. Through extensive simulations, we demonstrate the superior effectiveness of the proposed DQN framework across various IoT and O-IoT scenarios compared to the BAT and DJA approaches, achieving improvements in energy consumption and latency of 35%, 50%, 30%, and 40%, respectively. These findings underscore the significance of selecting an appropriate offloading strategy tailored to the specific requirements of IoT and O-IoT applications, particularly with regard to environmental stability and performance demands. Full article

An Analog-to-Digital Converter (ADC) is an indispensable part of image sensor systems. This paper presents a silicon-based 13-bit 100 kS/s two-step single-slope analog-to-digital converter (TS-SS ADC) for infrared image sensors with a frame rate of 100 Hz. For the charge leakage and offset voltage issues inherent in conventional TS-SS ADC, a four-terminal comparator was employed to resolve the fine ramp voltage offset caused by charge redistribution in storage and parasitic capacitors. In addition, a current-steering digital-to-analog converter (DAC) was adopted to calibrate the voltage reference of the dynamic comparator and mitigate differential nonlinearity (DNL)/integral nonlinearity (INL). To eliminate quantization dead zones, a 1-bit redundancy was incorporated into the fine quantization circuit. Finally, the quantization scheme consisted of 7-bit coarse quantization followed by 7-bit fine quantization. The ADC was implemented using an SMIC 55 nm processSemiconductor Manufacturing International Corporation, Shanghai, China. The post-simulation results show that when the power supply is 3.3 V, the ADC achieves a quantization range of 1.3 V–3 V. Operating at a 100 kS/s sampling rate, the proposed ADC exhibits an effective number of bits (ENOBs) of 11.86, a spurious-free dynamic range (SFDR) of 97.45 dB, and a signal-to-noise-and-distortion ratio (SNDR) of 73.13 dB. The power consumption of the ADC was 22.18 mW. Full article

Since its publication in the 1960s, the Kalman Filter (KF) has been a powerful tool in optimal state estimation. However, the KF and most of its variants have mainly focused on the state estimation of smooth systems. In this work, we propose a new algorithm called the Discrete Unilateral Constrained Extended Kalman Filter (DUCEKF) that expands the capabilities of the Extended Kalman Filter (EKF) to a class of hybrid mechanical systems known as systems with unilateral constraints. Such systems are non-smooth in position and discontinuous in velocity. Lyapunov stability theory is invoked to establish sufficient conditions for the estimation error stability of the proposed algorithm. A comparison of the proposed algorithm with the EKF is conducted in simulation through a case study to demonstrate the superiority of the DUCEKF for the state estimation tasks in this class of systems. Simulations and an experiment were developed in this case study to validate the performance of the proposed algorithm. The experiment was conducted using electronic hardware that consists of an Embedded System (ES) called “Mikromedia for dsPIC33EP” and an external DAC-12 Click board, which includes a Digital-to-Analog Converter (DAC) from Texas Instruments. Full article

Direct air capture (DAC), as a complementary strategy to carbon capture and storage (CCS), offers a scalable and sustainable pathway to remove CO₂ directly from the ambient air. This study presents a detailed evaluation of the amine-functionalised metal-organic framework (MOF) sorbent, mmen-Mg₂(dobpdc), for DAC using a temperature–vacuum swing adsorption (TVSA) process. While this sorbent has demonstrated promising performance in point-source CO₂ capture, this is the first dynamic simulation-based study to rigorously assess its effectiveness for low-concentration atmospheric CO₂ removal. A transient one-dimensional TVSA model was developed in Aspen Adsorption and validated against experimental breakthrough data to ensure accuracy in capturing both the sharp and gradual adsorption kinetics. To enhance process efficiency and sustainability, this work provides a comprehensive parametric analysis of key operational factors, including air flow rate, temperature, adsorption/desorption durations, vacuum pressure, and heat exchanger temperature, on process performance, including CO₂ purity, recovery, productivity, and specific energy consumption. Under optimal conditions for this sorbent (vacuum pressure lower than 0.15 bar and feed temperature below 15 °C), the TVSA process achieved ~98% CO₂ purity, recovery over 70%, and specific energy consumption of about 3.5 MJ/KgCO₂. These findings demonstrate that mmen-Mg₂(dobpdc) can achieve performance comparable to benchmark DAC sorbents in terms of CO₂ purity and recovery, underscoring its potential for scalable DAC applications. This work advances the development of energy-efficient carbon removal technologies and highlights the value of step-shape isotherm adsorbents in supporting global carbon-neutrality goals. Full article

Carbon capture technologies are largely considered to play a crucial role in meeting the climate change and global warming target set by Net Zero Emission (NZE) 2050. These technologies can contribute to clean energy transitions and emissions reduction by decarbonizing the power sector and other CO₂ intensive industries such as iron and steel production, natural gas processing oil refining and cement production where there is no obvious alternative to carbon capture technologies. While the progress of carbon capture technologies has fallen behind expectations in the past, in recent years there has been substantial growth in this area, with over 700 projects at various stages of development. Moreover, there are around 45 commercial carbon capture facilities already in operation around the world in different industrial processes, fuel transformation and power generation. Carbon capture technologies including pre/post-combustion, oxyfuel and chemical looping combustion have been widely exploited in the recent years at different Technology Readiness level (TRL). Although, a large number of review studies are available addressing different carbon capture strategies, however, studies related to the commercial status of the carbon capture technologies are yet to be conducted. In this review article, we summarize the state-of-the-art of different carbon capture technologies applied to different emission sources, focusing on emission reduction, net-zero emission, and negative emission. We also highlight the commercial status of the different carbon capture technologies including economics, opportunities, and challenges. Full article

Direct Air Capture (DAC) is emerging as a critical climate change mitigation strategy, offering a pathway to actively remove atmospheric CO₂. This comprehensive review synthesizes advancements in DAC technologies, with a particular emphasis on the pivotal role of nanostructured solid sorbent materials. The work critically evaluates the characteristics, performance, and limitations of key nanomaterial classes, including metal–organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, amine-functionalized polymers, porous carbons, and layered double hydroxides (LDHs), alongside solid-supported ionic liquids, highlighting their varied CO₂ uptake capacities, regeneration energy requirements, and crucial water sensitivities. Beyond traditional temperature/pressure swing adsorption, the review delves into innovative DAC methodologies such as Moisture Swing Adsorption (MSA), Electro Swing Adsorption (ESA), Passive DAC, and CO₂-Binding Organic Liquids (CO₂ BOLs), detailing their unique mechanisms and potential for reduced energy footprints. Despite significant progress, the widespread deployment of DAC faces formidable challenges, notably high capital and operational costs (currently USD 300–USD 1000/tCO₂), substantial energy demands (1500–2400 kWh/tCO₂), water interference, scalability hurdles, and sorbent degradation. Furthermore, this review comprehensively examines the burgeoning global DAC market, its diverse applications, and the critical socio-economic barriers to adoption, particularly in developing countries. A comparative analysis of DAC within the broader carbon removal landscape (e.g., CCS, BECCS, afforestation) is also provided, alongside an address to the essential, often overlooked, environmental considerations for the sustainable production, regeneration, and disposal of spent nanomaterials, including insights from Life Cycle Assessments. The nuanced techno-economic landscape has been thoroughly summarized, highlighting that commercial viability is a multi-faceted challenge involving material performance, synthesis cost, regeneration energy, scalability, and long-term stability. It has been reiterated that no single ‘best’ material exists, but rather a portfolio of technologies will be necessary, with the ultimate success dependent on system-level integration and the availability of low-carbon energy. The review paper contributes to a holistic understanding of cutting-edge DAC technologies, bridging material science innovations with real-world implementation challenges and opportunities, thereby identifying critical knowledge gaps and pathways toward a net-zero carbon future. Full article

Dopaminergic amacrine cells (DACs) are a subclass of amacrine cells that modulate retinal processing and light adaptation by releasing dopamine. Although the role of dopamine is largely conserved, their retinal distribution across mammals remains incompletely characterized. In mice, rats, thirteen-lined ground squirrels (TLGSs), and macaques, we systematically compared the localization, number, and topography of DACs by their expression of tyrosine hydroxylase (TH), a crucial enzyme in the biosynthesis of dopamine. In all species examined, TH+ cells were primarily located in the inner nuclear layer; however, there was a species-dependent influence on their number and distribution. Mice exhibited the highest density of TH+cells but completely lacked displaced TH+cells (dTH+cells) in the ganglion cell layer. Despite interspecies variation in the total number of TH+cells in the retina, the overall density in rats, TLGSs, and macaques was similar. Most species displayed a higher density of DACs toward central retinal regions. However, rats exhibited a distinctive dorsal concentration, particularly among dTH+cells. Although most species examined exhibited a similar ratio of TH+cells to Brn3a+ retinal ganglion cells, TLGSs showed a marked reduction, indicating a potentially diminished dopaminergic modulatory role. Species-specific DAC topographies aligned with specialized visual regions, such as the visual streak in TLGS and the macula in macaques. These results reveal both conserved and divergent features of retinal dopamine circuitry, reflecting evolutionary adaptations to visual processing demands. Full article

We present a 1024 × 512, 1000 fps, high-dynamic-range global shutter CMOS image sensor. The pixel is based on a voltage domain global shutter architecture, featuring a pitch of 24 μm × 24 μm. Both high-gain and low-gain signals can be captured within a single frame. The combined dynamic range is 95 dB, and the full well capacity is 620 ke-. In this paper, we analyze the pixel noise performance as well as the non-linearity and image lag that arise from parasitic capacitance in the pixel. The ramp generator is based on a 12-bit full thermometer code current-steering DAC with high driving capability. We discuss the design considerations for the ramp generator, particularly addressing the phenomenon of non-linear response. Finally, the comparator design and the column readout noise are analyzed. Full article

Achieving carbon neutrality requires not only reducing CO₂ emissions but also capturing atmospheric CO₂. Direct air capture (DAC) using amine-based adsorbents has emerged as a promising approach. In this study, we developed amine-epoxy/poly(vinyl alcohol) (AE/PVA) nanofibers via electrospinning and in situ thermal polymerization. PVA was incorporated to enhance spinnability, and B-staging of AE enabled fiber formation without inline heating. We systematically investigated the effects of electrospinning parameters, epoxy-to-amine ratios (E/A), and the degree of PVA saponification on CO₂ adsorption performance. Thinner fibers, obtained by adjusting spinning conditions, exhibited faster adsorption kinetics due to increased surface area. Varying the E/A revealed a trade-off between adsorption capacity and low-temperature desorption efficiency, with secondary amines offering a balanced performance. Additionally, highly saponified PVA improved thermal durability by minimizing side reactions with amines. These findings highlight the importance of optimizing fiber morphology, chemical composition, and polymer properties to enhance the performance and stability of AE/PVA nanofibers for DAC applications. Full article

Reinforcement learning (RL), a branch of artificial intelligence (AI), is becoming more popular in a variety of application fields such as games, workplaces, and behavioral analysis, due to its ability to model complex decision-making through interaction and feedback. Traditional systems for personality and behavior assessment often rely on self-reported questionnaires, which are prone to bias and manipulation. RL offers a compelling alternative by generating diverse, objective behavioral data through agent–environment interactions. In this paper, we propose a Reinforcement Learning-based framework in a game environment, where agents simulate personality-driven behavior using context-aware policies and exhibit a wide range of realistic actions. Our method, which is based on the OCEAN Five personality model—openness, conscientiousness, extroversion, agreeableness, and neuroticism—relates psychological profiles to in-game decision-making patterns. The agents are allowed to operate in numerous environments, observe behaviors that were modeled using another simulation system (HiDAC) and develop the skills needed to navigate and complete tasks. As a result, we are able to identify the personality types and team configurations that have the greatest effects on task performance and collaboration effectiveness. Using interaction data derived from self-play, we investigate the relationships between behaviors motivated by the personalities of the agents, communication styles, and team outcomes. The results demonstrate that in addition to having an effect on performance, personality-aware agents provide a solid methodology for producing realistic behavioral data, developing adaptive NPCs, and evaluating team-based scenarios in challenging settings. Full article

Background/Objectives: The fat mass and obesity-associated (FTO) protein demethylates nuclear N6-Methyladenosine (m6A) on mRNA, facilitates tumor growth, and contributes to therapeutic resistance in multiple cancer types. Recent evidence demonstrates several roles of FTO in tumorigenesis. In this study, we seek to explore the role of FTO in non-small cell lung cancer (NSCLC) tumorigenicity and its relationship with epidermal growth factor receptor (EGFR) tyrosine kinase resistance. Methods: We performed qPCR, immunoblotting, viability assays, migration assays, and ATP assays to investigate the functions of FTO in EGFR tyrosine kinase inhibitor (TKI) resistance, specifically to erlotinib, in three NSCLC cell lines harboring either wild-type or mutant EGFR. We also performed immunohistochemistry on lung tumor tissues from patients diagnosed at different stages of NSCLC. Results: Our study found an upregulation of FTO in erlotinib-resistant (ER) cell lines at both the gene and protein levels. FTO inhibition and knockdown significantly reduced cell viability of erlotinib-resistant H2170 and PC9 cells by over 30% when treated with 0.8 µM of Dac51 and about 20% when treated with siFTO. FTO inhibition also slowed down the migration of ER cells, and the effect was even more pronounced when combined with erlotinib. Furthermore, FTO was found to be overexpressed in late-stage NSCLC tumor tissues compared to early-stage tumors, and it was upregulated in patients who smoked. Conclusions: These findings suggest FTO might mediate resistance and tumor growth by augmenting cell proliferation. In addition, FTO can be a potential prognostic marker in NSCLC patients. Full article

Single-atom catalysts characterized by their novel electronic configurations and exceptional atomic utilization efficiency have emerged as potential alternatives to costly noble metal catalysts, garnering extensive research attention in various electrocatalytic fields. However, the inherent characteristics of single metal centers constrain their further application in catalyzing multi-electron reactions. In contrast, multi-atom catalysts (MACs), particularly dual-atom catalysts (DACs), possess multiple active metal sites that can significantly enhance catalytic performance through synergistic effects. This review summarizes recent developments in multi-atom catalysts, focusing on synthesis methods, design strategies, and the correlation between interatomic synergy and catalytic efficiency. Furthermore, we discuss their applications in key electrochemical reactions, including the hydrogen evolution reaction, oxygen reduction reaction, and oxygen evolution reaction. Finally, we outline the opportunities and challenges in this field to guide the development of high-efficiency catalysts for sustainable energy conversion applications. Full article

Multi-fingered dexterous hands provide superior dexterity in complex manipulation tasks due to their high degrees of freedom (DOFs) and biomimetic structures. Inspired by the anatomical structure of human tendons and muscles, numerous robotic hands powered by pneumatic artificial muscles (PAMs) have been created to replicate the compliant and adaptable features of biological hands. Nonetheless, PAMs have inherent nonlinear and hysteresis behaviors that create considerable challenges to achieving real-time control accuracy and stability in dexterous hands. In order to address these challenges, this paper proposes a hierarchical control architecture that employs a fuzzy PID strategy to optimize the nonlinear control of pneumatic artificial muscles (PAMs). The FPGA-based hardware integrates a multi-channel digital-to-analog converter (DAC) and a multiplexed acquisition module, facilitating the independent actuation of 20 PAMs and the real-time monitoring of 20 joints. The software implements a fuzzy PID algorithm that dynamically adjusts PID parameters based on both the error and the error rate, thereby effectively managing the nonlinear behaviors of the hand. Experimental results demonstrate that the designed control system achieves high precision in controlling the angle of a single finger joint, with errors maintained within ±1°. In scenarios involving multi-finger cooperative grasping and biomimetic motion demonstrations, the system exhibits excellent synchronization and real-time performance. These results validate the efficacy of the fuzzy PID control strategy and confirm that the proposed system fulfills the precision and stability requirements for complex operational tasks, providing robust support for the application of PAM-driven multi-fingered dexterous hands. Full article

Membrane-based direct air capture of CO₂ (m-DAC) is a promising solution for atmospheric decarbonization. Despite growing interest, the impact of relative air humidity on the performance of m-DAC systems is often neglected in the literature. This study presents detailed parametric analyses that take into account humidity variability and several hypothetical scenarios regarding membrane selectivity toward water vapor. Specifically, cases were considered where the permeance of H₂O relative to CO₂ was assumed to be 0.5, 2, and 5 times higher, which allowed for a systematic assessment of the impact of relative humidity on process performance. The calculations were carried out both for membranes with assumed separation parameters and for the PolyActive^TM membrane, enabling a realistic evaluation of the influence of atmospheric conditions on the process. The results show that an increase in humidity in the analyzed range from 0 to 80% can lead to a rise in the energy intensity of the process by up to approximately 34%, and an increase in total power demand by around 29%. As humidity increases, key process parameters such as CO₂ purity in the permeate and recovery rate decrease. The water vapor content in the permeate in a single-stage membrane separation process can reach up to 60%. It is recommended to use gas drying systems and to develop membranes with low H₂O permeance in order to reduce the energy cost of the process. The potential location of m-DAC systems should preferably be in regions with low air humidity. The study highlights the necessity of considering local climate conditions and the need for further research on membrane selectivity. Full article