ACS Engineering Au最新文献

Ocean liming is attracting ever-increasing attention as one of the most suitable and convenient ways of removing carbon dioxide from the atmosphere and combating global warming and the acidification of the oceans at the same time. However, the short-term consequences of Ca(OH)₂ [slaked lime] dissolution in seawater have been scarcely studied. In this work, we investigate in detail what happens in the initial stages after the dissolution of slaked lime, analyzing the kinetics of the process and the effects on the physicochemical parameters of seawater. A series of experiments, carried out by varying the seawater conditions (like temperature and salinity) or the liming conditions (like the dispersion in the form of slurry or powder and the concentration) allow us to draw conclusions on the ideal conditions for a more efficient and environmentally friendly liming process.

Multimaterial additive manufacturing incorporates multiple species within a single 3D-printed object to enhance its material properties and functionality. This technology could play a key role in distributed manufacturing. However, conventional layer-by-layer construction methods must operate at low volumetric throughputs to maintain fine feature resolution. One approach to overcome this challenge and increase production capacity is to structure multimaterial components in the printhead prior to deposition. Here we survey four classes of multimaterial nozzle innovations, nozzle arrays, coextruders, static mixers, and advective assemblers, designed for this purpose. Additionally, each design offers unique capabilities that provide benefits associated with accessible architectures, interfacial adhesion, material properties, and even living-cell viability. Accessing these benefits requires trade-offs, which may be mitigated with future investigation. Leveraging decades of research and development of multiphase extrusion equipment can help us engineer the next generation of 3D-printing nozzles and expand the capabilities and practical reach of multimaterial additive manufacturing.

The ability to find optimal molecular structures with desired properties is a popular challenge, with applications in areas such as drug discovery. Genetic algorithms are a common approach to global minima molecular searches due to their ability to search large regions of the energy landscape and decrease computational time via parallelization. In order to decrease the amount of unstable intermediate structures being produced and increase the overall efficiency of an evolutionary algorithm, clustering was introduced in multiple instances. However, there is little literature detailing the effects of differentiating the selection frequencies between clusters. In order to find a balance between exploration and exploitation in our genetic algorithm, we propose a system of clustering the starting population and choosing clusters for an evolutionary algorithm run via a dynamic probability that is dependent on the fitness of molecules generated by each cluster. We define four parameters, MFavOvrAll-A, MFavClus-B, NoNewFavClus-C, and Select-D, that correspond to a reward for producing the best structure overall, a reward for producing the best structure in its own cluster, a penalty for not producing the best structure, and a penalty based on the selection ratio of the cluster, respectively. A reward increases the probability of a cluster’s future selection, while a penalty decreases it. In order to optimize these four parameters, we used a Gaussian distribution to approximate the evolutionary algorithm performance of each cluster and performed a grid search for different parameter combinations. Results show parameter MFavOvrAll-A (rewarding clusters for producing the best structure overall) and parameter Select-D (appearance penalty) have a significantly larger effect than parameters MFavClus-B and NoNewFavClus-C. In order to produce the most successful models, a balance between MFavOvrAll-A and Select-D must be made that reflects the exploitation vs exploration trade-off often seen in reinforcement learning algorithms. Results show that our reinforcement-learning-based method for selecting clusters outperforms an unclustered evolutionary algorithm for quinoline-like structure searches.

Platinum on oxide catalysts are established for the loading and unloading of liquid organic hydrogen carriers (LOHCs). These catalysts have been optimized so far to provide high reaction rates and consequently high power densities in the loading and unloading reactor units. However, high temperatures are required for catalytic dehydrogenation (hydrogen release), which can result in low energy efficiency. Another challenge is to avoid the formation of the undesired side product methylfluorene. In this work, the optimized S–Pt/TiO₂ catalyst was successfully applied in the hydrogenation and dehydrogenation of the commercially attractive LOHC system benzyltoluene/perhydro benzyltoluene (H0-BT/H12-BT). Methylfluorene was not detected using S–Pt/TiO₂, while utilizing the S–Pt/Al₂O₃ state-of-the-art catalyst caused methylfluorene formation. The S–Pt/TiO₂ catalyst combines the prevention of this side reaction with a competitive hydrogen release rate. Hence, the application of S–Pt/TiO₂ in the LOHC cycle was further studied. It was shown that the catalytic hydrogen release can be accelerated by increasing the temperature, but low reaction temperatures are desired to increase the energy efficiency of the process by enabling heat integration between the hydrogen release and waste heat generation from energetic hydrogen use cases. Accordingly, the potential for low-temperature hydrogen release at reduced pressure was demonstrated by a systematic investigation of pressure influence. With pressure reduction, the hydrogen release productivity continuously increased. Finally, the hydrogenation and dehydrogenation productivity obtained in this work was compared to results reported in the literature to demonstrate the implementation potential of the optimized S–Pt/TiO₂ catalyst.

Addressing climate change constitutes one of the major scientific challenges of this century, and it is widely acknowledged that anthropogenic CO₂ emissions largely contribute to this issue. To achieve the “net-zero” target and keep the rise in global average temperature below 1.5 °C, negative emission technologies must be developed and deployed at a large scale. This study investigates the feasibility of using membranes as direct air capture (DAC) technology to extract CO₂ from atmospheric air to produce low-purity CO₂. In this work, a two-stage hollow fiber membrane module process is designed and modeled using the AVEVA Process Simulation platform to produce a low-purity (≈5%) CO₂ permeate stream. Such low-purity CO₂ streams could have several possible applications such as algae growth, catalytic oxidation, and enhanced oil recovery. An operability analysis is performed by mapping a feasible range of input parameters, which include membrane surface area and membrane performance metrics, to an output set, which consists of CO₂ purity, recovery, and net energy consumption. The base case for this simulation study is generated considering a facilitated transport membrane with high CO₂/N₂ separation performance (CO₂ permeance = 2100 GPU and CO₂/N₂ selectivity = 1100), when tested under DAC conditions. With a constant membrane area, both membranes’ intrinsic performances are found to have a considerable impact on the purity, recovery, and energy consumption. The area of the first module plays a dominant role in determining the recovery, purity, and energy demands, and in fact, increasing the area of the second membrane has a negative impact on the overall energy consumption, without improving the overall purities. The CO₂ capture capacity of DAC units is important for implementation and scale-up. In this context, the performed analysis showed that the m-DAC process could be appropriate as a small-capacity system (0.1–1 Mt/year of air), with reasonable recoveries and overall purity. Finally, a preliminary CO₂ emissions analysis is carried out for the membrane-based DAC process, which leads to the conclusion that the overall energy grid must be powered by renewable sources for the technology to qualify within the negative emissions category.