Electrochemical extraction of lithium from seawater/brine has attracted more attentions, since its environmental friendliness and energy-efficient feature. Lithium recovery capacity and energy ...efficiency has been improved significantly with fast growth of researches on nanomaterial and novel energy storage techniques. This review summarizes electrochemical lithium extracting research from seawater/brine with an emphasis on electrode material design and improving lithium extraction performance. Subsequently, electrochemical extraction of other rare elements from seawater/brine has also been briefly introduced, for reference. It has been concluded that electrochemical extraction technology shows competitiveness and perspectives on utilization of seawater/brine chemical resources. Further, future challenges are also discussed.
•Electrochemical extraction of lithium from seawater/brine was reviewed systematically.•An emphasis on electrode material design thought has been analyzed.•The improved lithium extraction performance has been summarized and compared.•Future challenge on this technology has been clarified.
Geoscientists mainly identify subsurface geologic features using exploration-derived seismic data. Classification or segmentation of 2D/3D seismic images commonly relies on conventional deep learning ...methods for image recognition. However, complex reflections of seismic waves tend to form high-dimensional and multi-scale signals, making traditional convolutional neural networks (CNNs) computationally costly. Here we propose a highly efficient and resource-saving CNN architecture (SeismicPatchNet) with topological modules and multi-scale-feature fusion units for classifying seismic data, which was discovered by an automated data-driven search strategy. The storage volume of the architecture parameters (0.73 M) is only ~2.7 MB, ~0.5% of the well-known VGG-16 architecture. SeismicPatchNet predicts nearly 18 times faster than ResNet-50 and shows an overwhelming advantage in identifying Bottom Simulating Reflection (BSR), an indicator of marine gas-hydrate resources. Saliency mapping demonstrated that our architecture captured key features well. These results suggest the prospect of end-to-end interpretation of multiple seismic datasets at extremely low computational cost.
Capacitive deionization (CDI) is an emerging technology for energy-efficient water desalination, and attracts more and more attention in recent years. It has been concluded that CDI technology shows ...competitiveness and perspectives on seawater desalination and wastewater treatment. The ionic adsorption mechanism can be clarified by electric double-layer capacitive adsorption and pseudocapacitive adsorption. The performance of CDI depends on both device and materials. The adsorption capacity and energy efficiency was improved significantly with fast growth of researches on material and novel energy storage techniques. This review summarizes researches on CDI technologies with an emphasis on electrode material design and improved adsorption performance.
This review outlines the ion storage mechanisms and electrode materials of capacitive deionization. Display omitted
•Electrochemical extraction of ions from seawater/brine was reviewed systematically.•An emphasis on electrode material design thought has been analyzed.•The performance of various extraction devices has been summarized and compared.•Future challenge on this technology has been clarified.
Poly(allylamine)-stabilized spherical- and rod-shaped copper nanoparticles are synthesized by a simple chemical reaction. The synthesis is performed by the reduction of copper(II) salt with hydrazine ...in aqueous solution under atmospheric air in the presence of poly(allylamine) (PAAm) capping agent. Noteworthy of the advantages of the synthetic method includes its production of water dispersible copper nanoparticles at room temperature under no inert atmosphere, making the synthesis more environmentally friendly. The resulting copper nanoparticles are investigated by UV−vis spectroscopy and transmission electron microscopy (TEM). The results demonstrate that the amount of NaOH used is important for the formation of the copper nanoparticles while the reaction time and concentration of PAAm play key roles in controlling the size and shape of the nanoparticles, respectively. The resulting colloidal copper nanoparticles exhibit large surface-enhanced Raman scattering (SERS) signals.
Compared to traditional extraction of lithium using solar evaporation, the electrochemical extraction is advantageous in many aspects, including elevated efficiency, superior selectivity, and low ...environmental pollution. In this study, LiNi0·038Mo0·012Mn1·95O4 (LNMMO) with good lattice structure and nano morphology was successfully prepared and confirmed by XRD (X-ray diffraction), SEM-Mapping (scanning electron microscope mapping), SEM (scanning electron microscope), and EDX (Energy-dispersive X-ray spectroscopy) analyses. The pairing of LNMMO with activated carbon (AC) yielded obvious cost-effective and environmentally friendly composite when compared to conventional Noble metals used for battery anodes, such as Ag and Pt. A hybrid supercapacitor was assembled using delithiated LNMMO cathode (NMMO) and AC anode to form NMMO/AC. The cell showed high capacity, high rate performance, and excellent cyclic stability. A continuous flow NMMO/AC hybrid supercapacitor (CF-NMMO/AC) was developed for selective capture of Li+ in aqueous solution by combining NMMO/AC hybrid supercapacitor with self-designed continuous flow control system. The device delivered high Li+ extraction efficiencies reaching 14.4 mg/g per one cycle in simulated brine by consuming only 7.91 W h/mol Li+. The overall process produced 97.2％ Li+ in simulated brine at optimized operating conditions. Overall, CF-NMMO/AC system provided higher efficiency, superior selectivity, and moderate energy utilization for Li+ recovery from brine.
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•LiNi0·03Mo0·01Mn1·96O4 (LNMMO) was synthesized as cathode material.•It delivered Li+ extraction efficiencies reaching 14.4 mg/g.•A continuous flow controlled hybrid supercapacitor (CF-LNMMO/AC) was developed.•The coulombic efficiency appeared stable after 30 charge/discharge cycles.•CF-LNMMO/AC produced 97.2％ Li+ using 7.91 W h/mol Li+.
Wellbore instability is a major safety and environmental concern in both onshore and offshore drilling. Geological drilling risk assessment for wellbore collapse is critical for the optimization of ...well plans, and to reduce potential costs before drilling. In this work, we propose a physics-guided deep learning approach to predict wellbore instability using seismic attributes data. We first trained an auto-encoder to extract latent representation (five principle features) from 17 typical seismic attributes, and then we introduced a regularization term, based on geomechanics in the objective function to train a neural network. As long as there is no significant over-pressure in the formation, the physics-based regularization term indicating wellbore instability risk is a function of neutron porosity, and of the true vertical depth obtained from well logging data. In this way, we combined drill log data for five wells, as prior information, with latent seismic attribute representations, to train the neural network. After training, our approach needed only seismic data to predict wellbore instability risk in new locations, and our case study showed that the physics-based regularizer, with an appropriate weight, prevented overfitting to training data and enhanced the generalization accuracy of the neural network (by ~4%) in two new test wells. We argue that statistical correlations between seismic attributes and rock properties are algorithm dependent, and have to be treated cautiously in the absence of a base of petrophysical reasoning. The physics-guided deep learning method presented here has potential application for the quantification of geology-based wellbore instability risk before drilling.
•Physics-guided deep learning improved geological risk prediction before drilling.•Logging data is used as prior information to constrain deep learning physically.•Auto-encoder lowers the interpretation risk of manually selecting data features.
SUMMARY
Estimating subsurface attenuation distribution is essential to compensate the amplitude and phase distortions in seismic imaging and characterize attenuative reservoirs. Full-waveform ...inversion (FWI) methods represent promising techniques to invert for both velocity and attenuation models with arbitrary spatial distributions. However, simultaneously determining velocity and attenuation properties introduces the problem of interparameter trade-off in viscoelastic FWI. Ignoring attenuation effects can result in inaccurate velocity estimations. Velocity errors may produce significant parameter crosstalk artefacts in the inverted attenuation models. An appropriate misfit function measuring specific seismic attribute is essential to capture the influence of attenuation on the seismic data and thus is expected to reduce the influences of velocity errors for attenuation estimation. In this study, we evaluate the performances of different misfit functions for attenuation estimation in viscoelastic FWI accompanied with a two-stage sequential inversion strategy. Synthetic examples with different acquisition surveys are given to show that in the presence of strong velocity errors, the amplitude-based misfit functions, including envelope-difference, root-mean-square amplitude-ratio and spectral amplitude-ratio, can invert for the attenuation models more reliably, compared to the waveform-difference and instantaneous phase misfit functions. With the two-stage inversion approach, more reliable velocity and attenuation models can be obtained using viscoelastic FWI.
Detection and imaging of multiple moving targets is a challenging task, particularly with real synthetic aperture radar (SAR) data. In this paper, moving targets with different motion parameters are ...classified based on the relative locations of their spectra to that of the clutter. Based on the classification, a novel moving target processing strategy with real SAR data is proposed, including a two-step ground moving target indication (GMTI) algorithm and a practical ground moving target imaging (GMTIm) algorithm with motion error compensation. The two-step GMTI algorithm has the ability of indicating multiple moving targets, particularly those submerged by the clutter; the practical GMTIm algorithm shows a robust performance in real data moving target imaging since the motion errors are estimated and compensated. Both simulated and real data processing results are provided to demonstrate the effectiveness of the proposed strategy.
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