Despite the consensus that keeping global temperature rise within 1.5 °C above pre-industrial level by 2100 reduces the chance for climate change to reach the point of no return, the newest Intergovernmental Panel on Climate Change (IPCC) report warns that the existing commitment of greenhouse gas emission reduction is only enough to contain the warming to 3–4 °C by 2100. The harsh reality not only calls for speedier deployment of existing CO₂ reduction technologies but demands development of more cost-efficient carbon removal strategies. Here we report an ocean alkalinity-based CO₂ sequestration scheme, taking advantage of proton consumption during nitrate assimilation by marine photosynthetic microbes, and the ensuing enhancement of seawater CO₂ absorption. Benchtop experiments using a native marine phytoplankton community confirmed pH elevation from ~8.2 to ~10.2 in seawater, within 3–5 days of microbial culture in nitrate-containing media. The alkaline condition was able to sustain at continued nutrient supply but reverted to normalcy (pH ~8.2–8.4) once the biomass was removed. Measurements of δ¹³C in the dissolved inorganic carbon revealed a significant atmospheric CO₂ contribution to the carbonate alkalinity in the experimental seawater, confirming the occurrence of direct carbon dioxide capture from the air. Thermodynamic calculation shows a theoretical carbon removal rate of ~0.13 mol CO₂/L seawater, if the seawater pH is allowed to decrease from 10.2 to 8.2. A cost analysis (using a standard bioreactor wastewater treatment plant as a template for CO₂ trapping, and a modified moving-bed biofilm reactor for nitrate recycling) indicated that a 1 Mt CO₂/year operation is able to perform at a cost of ~$40/tCO₂, 2.5–5.5 times cheaper than that offered by any of the currently available direct air capture technologies, and more in line with the price of $25–30/tCO₂ suggested for rapid deployment of large-scale CCS systems.

The residual shear strength of cohesive soils is an important index in study and designing of geotechnic engineering projects, a neural network model that has been developed to estimate the residual shear strength with the physical property parameters of cohesive soils. It has been studied with the model that relation of the physical property parameters, which present the properties and conditions of cohesive soils, and its residual shear strength, based on the further analysis of residual shear strength mechanism and its influence factors. The results of calculation and analysis show that the model is more precise than the empirical formulas are. The better self-learning, and calculation capacity and mistake tolerance make the neural network models more exact and useful.