Related papers: Path to Low-Cost Direct Air Capture

The dual challenge of decarbonizing the economy and meeting rising global energy demand underscores the need for scalable and cost-effective carbon dioxide removal technologies. Direct air capture (DAC) is among the most promising…

Current decarbonization efforts are falling short of meeting the net-zero greenhouse gas (GHG) emission target, highlighting the need for substantial carbon dioxide removal methods such as direct air capture (DAC). However, integrating DACs…

Direct air carbon capture and storage (DACCS) is a promising CO2 removal technology, but its deployment at scale remains speculative. Yet, its technological, economic, and policy-related uncertainties have often been overlooked in…

Direct Air Carbon Capture and Storage (DACCS) can mitigate hard-to-abate emissions, e.g. from transport or industry. However, there is a wide variety of cost estimates for DACCS, driven, to a significant extent, by differences in…

Building a carbon-neutral world needs to remove the excess CO$_2$ that has already been dumped into the atmosphere. The sea, soil, vegetation, and rocks on Earth all naturally uptake CO$_2$ from the atmosphere. Human beings can accelerate…

Understanding the similarities and differences of the long term impact of different carbon dioxide removal (CDR) techniques is essential in determining the most effective and sustainable strategies to mitigate climate change. In particular,…

A moisture-driven air capture (DAC) system was designed and demonstrated. A laboratory-scale system delivering ~1 g CO2 per day was demonstrated in a laminar flow hood and a small pilot-scale system that could deliver ~100 g CO2 daily was…

The international community aims to limit global warming to 1.5{\deg}C, but little progress has been made towards a global, cost-efficient, and fair climate mitigation plan to deploy carbon dioxide removal (CDR) at the Paris Agreement's…

Hydrogen production from water electrolysis, direct air capture (DAC), and synthetic kerosene derived from hydrogen and CO2 (`e-kerosene') are expected to play an important role in global decarbonization efforts. So far, the economics of…

AI data centers (AIDCs) are rapidly increasing electricity demand and associated CO2 emissions, yet they also generate continuous low-grade waste heat. Here, we assess whether this heat can be upgraded by heat pumps to drive direct air…

The carbon-capturing process with the aid of CO2 removal technology (CDRT) has been recognised as an alternative and a prominent approach to deep decarbonisation. However, the main hindrance is the enormous energy demand and the economic…

We present new metrics to evaluate solid sorbent materials for Direct Air Capture (DAC). These new metrics provide a theoretical upper bound on CO2 captured per energy as well as a theoretical upper limit on the purity of the captured CO2.…

Achieving climate neutrality in Europe requires rapid electrification alongside carbon management strategies for residual emissions. Existing analyses of the European energy system often focus on collocated carbon capture and geological…

In the global pursuit of sustainable energy solutions, mitigating carbon dioxide (CO2) emissions stands as a pivotal challenge. With escalating atmospheric CO2 levels, the imperative of direct air capture (DAC) systems becomes evident.…

The electrification of energy demand across sectors, powered by solar and wind generation, is the best strategy for achieving carbon neutrality. Carbon dioxide removal (CDR) strategies are also expected to play a crucial role by providing…

Early-exit deep neural networks enable adaptive inference by terminating computation when sufficient confidence is achieved, reducing cost for edge AI accelerators in resource-constrained settings. Existing methods, however, rely on…

Change-point detection (CPD) is crucial for identifying abrupt shifts in data, which influence decision-making and efficient resource allocation across various domains. To address the challenges posed by the costly and time-intensive data…

Accurate day-ahead individual residential load forecasting is of great importance to various applications of smart grid on day-ahead market. Deep learning, as a powerful machine learning technology, has shown great advantages and promising…

This research addresses the significant challenges of energy consumption and environmental impact in laser cutting by proposing novel deep learning (DL) methodologies to achieve energy reduction. Recognizing the current lack of adaptive…

Deploying a deep learning model on mobile/IoT devices is a challenging task. The difficulty lies in the trade-off between computation speed and accuracy. A complex deep learning model with high accuracy runs slowly on resource-limited…