Lithium production has become increasingly critical for sustainable development. The extraction of lithium from aqueous sources, particularly salt-lake brine, has become a trend in the lithium ...recovery industry because of its low cost and abundant reserves. Among various technologies applied for lithium recovery, membrane processes driven by pressure, electrical field, and thermal gradient have received considerable attention in the past few decades because of their high energy efficiency and low environmental impact. This paper presents a comprehensive review of the advantages and challenges of the current membrane-based technologies applied to the recovery of a water lithium resource. Here, we highlight that the combination of membrane processes (e.g. nanofiltration, selective electrodialysis, and membrane distillation crystallization) with a conventional lithium precipitation process will lead to higher performance efficiency and lower cost. Although the membrane-based separation technology is technically feasible, it is restricted by its high capital and operating costs. Therefore, the future development of membrane-based technologies should include efforts for the improvement of the separation efficiency, material stability, and some engineering aspects such as membrane fouling control, module design, and process optimisation.
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•Membrane-based technologies for lithium recovery from water resource are reviewed.•Technologies covered in review include NF, SLM, IIM, LISM, MDC, S-ED and PSMCDI.•The advantages and challenges of these membrane-based technologies are explained.•The techno-economic feasibility of these technologies is evaluated.•The diresssssctions for future research and development are identified.
Knowledge Tracing (KT) aims to trace the student’s state of evolutionary mastery for a particular knowledge or concept based on the student’s historical learning interactions with the corresponding ...exercises. Taking the “exercise-to-concept” relationships as input, several existing methods have been developed to trace and model students’ mastery states. However, these studies face two major shortcomings in KT: 1) they only consider “exercise-to-concept” relationships; 2) the multi-hot embeddings lack interpretability. In order to address the above issues, we propose a Joint graph convolutional network based deep Knowledge Tracing (JKT) framework to model the multi-dimensional relationships of “exercise-to-exercise”, and “concept-to-concept” into graph and fuse them with “exercise-to-concept” relationships. In JKT, it is not only possible to establish connections between exercises under cross-concepts, but also to help capture high-level semantic information and increase the model’s interpretability. In addition, sufficient experiments conducted on four real-world datasets have demonstrated that JKT performs better than the other baseline models. We further illustrate a case study to demonstrate its interpretability for learning analysis
Hydraulic fracturing is a key technology for improving the permeability of coal reservoirs. Understanding the variation of hydraulically induced fractures is crucial for improving coalbed methane ...production. In this study, we conducted a laboratory-simulation of hydraulic fracturing in coal and applied X-ray computer tomography (CT) and digital volume correlation (DVC) to quantify the spatial distribution, structural variation, and propagation of fractures with an aperture greater than 28.4 μm. Hydraulic fracturing increased the aperture, volume (by 5.3, 32.2, 2.2 and 2.8 times) and surface area (by 1.1, 9.9, 1.8 and 0.8 times), and simplified fracture morphology in the four tested samples. Moreover, the significant influence range in the axial direction of hydraulic fracturing on fracture is 4.2 cm, 4.4 cm, 1.9 cm and 2.9 cm, respectively, with fracture connectivity reaching 61.8%, 99.3%, 77.7%, and 91.1%. A low in-situ stress differential resulted in the formation of a complex network of many fractures with a small volume. A high in-situ stress differential resulted in the formation of large fractures with a simple morphology. The X-ray CT images also showed that new fractures originated in and propagated along the mineral–maceral interface. DVC shows that high volume displacement and strain occur in the fracture area induced by hydraulic fracturing, and it has a good application prospect in the investigation of the microevolution and microdamage of fractures in coal.
•Hydraulic fracturing increased fracture volume and surface area.•Low in-situ stress differential resulted in a complex fracture network•High in-situ stress differential resulted in large, simple fractures.•Vitrain bands with endogenous fractures were fractured preferentially.•DVC is applicable in the investigation of fractures in coal.
Membrane-based treatment for oil-in-water emulsion remains a significant challenge. The main difficulties are to solve membrane fouling and to destroy the emulsion system to achieve high-efficiency ...emulsion separation. Herein, chemical demulsification combined with membrane separation technology was proposed for oil/water separation from the emulsion. A hyperbranched phenol-amine resin block polyether demulsifier (AE2311) was grafted onto surface of styrene-co-maleic anhydride (SMA) blend polyvinylidene fluoride (PVDF) membrane by alcoholysis reaction. The successful preparation of the demulsifier modified PVDF membrane was characterized by X - rayphotoelectron spectroscopy (XPS), attenuated total reflectance-fourier transform infrared spectroscopy (ATR-FTIR), scanning electron microscopy (SEM) and atomic force microscope (AFM). The obtained AE2311@SMA/PVDF membrane with grafting time of 9 h exhibited superhydrophilic (water contact angle of 0°) and underwater superoleophobic properties (underwater oil contact angle over 150°). The modified membrane can break the O/W emulsion and allow water to pass through. The separation efficiency for dichloroethane-in-water, kerosene-in-water, toluene-in-water and petroleum ether-in-water emulsions were all recorded over 99.0%, which indicates that the modified membrane has excellent capability for oil-water separation. Moreover, the modified membrane can be reused and exhibited long-term operation stability owing to the excellent underwater anti-fouling performance. The intrinsic mechanism for the oil/water separation is the synergistic effect of the chemical demulsification of AE2311, the hydrophilic and underwater super hydrophobic properties of the membrane surface, and the sieving effect of the ultrafiltration membrane. The demulsifier functionalized membrane provides a new idea for the fabrication membrane of oil-water emulsion separation in the future.
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•A polyether demulsifier graft PVDF blend styrene-co-maleic anhydride membrane was prepared.•The membrane exhibited superhydrophilicity and underwater superoleophobicity performance.•The membrane showed excellent anti-oil-fouling performance and the FRR was about 90%.•The oil removal rate of O/W emulsion was more than 99.0% by filtration coupling with demulsification.•The membrane can break the O/W emulsion even by membrane surface only contacting with the emulsion.
A positively charged polyamide composite nanofiltration (NF) hollow fiber membrane for lithium and magnesium separation was fabricated by the interfacial polymerization of ...1,4-Bis(3-aminopropyl)piperazine (DAPP) and trimesoyl chloride (TMC) on the polyacrylonitrile (PAN) ultrafiltration hollow fiber membrane. The chemical structure, morphology and surface charge of the composite membrane were characterized by using ATR-FTIR, XPS, SEM, AFM and zeta potential analyzer. The performance of the composite membrane was tested with various salts in aqueous solution (2000ppm) at the operating pressure of 0.3MPa. The results showed that the membrane performance was related to the changes of the monomer content in the aqueous phase rather than that in the organic phase. Furthermore, the salt rejection order of the membrane was MgCl2>MgSO4>NaCl≥LiCl. The difference between the rejections of MgCl2 and LiCl reached to 47.5%. The zeta potential measurements indicated that the surface of the resultant membrane was positively charged at pH values below 9.5. Moreover, the mass ratio of Mg2+/Li+ decreased from initial 20:1 in the feed of 2.0g/L MgCl2 and LiCl mixture to 7.7:1 in the permeate after the filtration by the composite membrane.
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•A positively charged NF membrane was prepared by interfacial polymerization.•The membrane performance was related to DAPP concentration rather than that of TMC.•The surface of resultant membrane was positively charged at pH<9.5.•The difference between the rejection rates of MgCl2 and LiCl reached to 47.5%.•The mass ratio of Mg2+/Li+ decreased from initial 20:1 to 7.7:1 after NF.
Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep ...learning. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021, focusing on models from traditional models to deep learning. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification. We then discuss each of these categories in detail, dealing with both the technical developments and benchmark datasets that support tests of predictions. A comprehensive comparison between different techniques, as well as identifying the pros and cons of various evaluation metrics are also provided in this survey. Finally, we conclude by summarizing key implications, future research directions, and the challenges facing the research area.
This study investigates the structural characteristics and hydration kinetics of modified basic oxygen furnace steel slag. The basic oxygen furnace steel slag (BOFS) was mixed with electric arc ...furnace steel slag (EAFS) in appropriate ratios and heated again at high temperature in the laboratory. The mineralogical and structural characteristics of both BOFS and modified steel slag (MSS) were characterized by X-ray diffraction, optical microscopy, scanning electron microscopy, Raman and Fourier transform infrared spectroscopies. The results show that modification increases alite content in MSS and decreases alite crystal size with the formation of C
6AF
2. One more obvious heat evolution peak appears in MSS's heat-flow rate curves in comparison to BOFS, becoming similar to that of typical Portland cement paste. As a result, its cementitious activity is much improved.
An nanoporous NiMnFeMo alloy is exploited for over water splitting in basic solutions. The free-standing 3D nanoporous electrode exhibits both outstanding HER and OER catalytic activities even at ...high current density in basic electrolyte, only 1.54V for full water splitting at 10 mA cm−2.
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•A free-standing nanoporous NiMnFeMo alloy with ultrahigh catalytic activity is synthesized.•The electrode can stably deliver 1000 mA cm−2 at only 290 mV for HER and 570 mV for OER.•The synergetic reaction of Ni with Mn, Fe and Mo results in good HER preformance.
Although significant progresses have been achieved recently in developing efficient catalysts for electrochemical water splitting, high performance catalysts toward hydrogen evolution and oxygen evolution in alkaline electrolyte at high current density (≧1000 mA cm−2) have been seldom realized. Herein, we report a flexible and free-standing nanoporous NiMnFeMo alloy (np-NiMnFeMo) with ultrahigh catalytic activity as both anode and cathode even at high current density. The nanoporous NiMnFeMo alloy can deliver as high as 1000 mA cm−2 at an overpotential of only 290 mV for hydrogen evolution reaction and 570 mV for oxygen evolution reaction. DFT calculations indicate that the ultrahigh HER activity of the catalyst is originated from the synergetic effect of the solid solution elements, where Ni atoms act as water dissociation center in the np-NiMnFeMo and the other metals (Mn, Fe and Mo) regulate the electronic structure and provide superior adsorption properties towards hydrogen. More importantly, the electrolyzer, assembled using the np-alloys as both cathode and anode for full water splitting, shows excellent stability.
Evidence of the effects of long-term fine particulate matter (PM2.5) exposure on cardiovascular diseases (CVDs) is rare for populations exposed to high levels of PM2.5 in China and in other countries ...with similarly high levels.
The aim of this study was to assess the CVD risks associated with long-term exposure to PM2.5 in China.
A nationwide cohort study, China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China), was used, with 116,972 adults without CVD in 2000 being included. Participants were followed until 2015. Satellite-based PM2.5 concentrations at 1-km spatial resolution during the study period were used for exposure assessment. A Cox proportional hazards model with time-varying exposures was used to estimate the CVD risks associated with PM2.5 exposure, adjusting for individual risk factors.
Annual mean concentrations of PM2.5 at the China-PAR sites ranged from 25.5 to 114.0 μg/m3. For each 10 μg/m3 increase in PM2.5 exposures, the multivariate-adjusted hazard ratio was 1.251 (95% confidence interval: 1.220 to 1.283) for CVD incidence and 1.164 (95% confidence interval: 1.117 to 1.213) for CVD mortality. The slopes of concentration-response functions of PM2.5 exposure and CVD risks were steeper at high PM2.5 levels. In addition, older residents, rural residents, and never smokers were more prone to adverse effects of PM2.5 exposure.
This study provides evidence that elevated long-term PM2.5 exposures lead to increased CVD risk in China. The effects are more pronounced at higher PM2.5 levels. These findings expand the current knowledge on adverse health effects of severe air pollution and highlight the potential cardiovascular benefits of air quality improvement in China and other low- and middle-income countries.
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The incidence of thyroid cancer is rising steadily because of overdiagnosis and overtreatment conferred by widespread use of sensitive imaging techniques for screening. This overall incidence growth ...is especially driven by increased diagnosis of indolent and well-differentiated papillary subtype and early-stage thyroid cancer, whereas the incidence of advanced-stage thyroid cancer has increased marginally. Thyroid ultrasound is frequently used to diagnose thyroid cancer. The aim of this study was to use deep convolutional neural network (DCNN) models to improve the diagnostic accuracy of thyroid cancer by analysing sonographic imaging data from clinical ultrasounds.
We did a retrospective, multicohort, diagnostic study using ultrasound images sets from three hospitals in China. We developed and trained the DCNN model on the training set, 131 731 ultrasound images from 17 627 patients with thyroid cancer and 180 668 images from 25 325 controls from the thyroid imaging database at Tianjin Cancer Hospital. Clinical diagnosis of the training set was made by 16 radiologists from Tianjin Cancer Hospital. Images from anatomical sites that were judged as not having cancer were excluded from the training set and only individuals with suspected thyroid cancer underwent pathological examination to confirm diagnosis. The model's diagnostic performance was validated in an internal validation set from Tianjin Cancer Hospital (8606 images from 1118 patients) and two external datasets in China (the Integrated Traditional Chinese and Western Medicine Hospital, Jilin, 741 images from 154 patients; and the Weihai Municipal Hospital, Shandong, 11 039 images from 1420 patients). All individuals with suspected thyroid cancer after clinical examination in the validation sets had pathological examination. We also compared the specificity and sensitivity of the DCNN model with the performance of six skilled thyroid ultrasound radiologists on the three validation sets.
Between Jan 1, 2012, and March 28, 2018, ultrasound images for the four study cohorts were obtained. The model achieved high performance in identifying thyroid cancer patients in the validation sets tested, with area under the curve values of 0·947 (95% CI 0·935–0·959) for the Tianjin internal validation set, 0·912 (95% CI 0·865–0·958) for the Jilin external validation set, and 0·908 (95% CI 0·891–0·925) for the Weihai external validation set. The DCNN model also showed improved performance in identifying thyroid cancer patients versus skilled radiologists. For the Tianjin internal validation set, sensitivity was 93·4% (95% CI 89·6–96·1) versus 96·9% (93·9–98·6; p=0·003) and specificity was 86·1% (81·1–90·2) versus 59·4% (53·0–65·6; p<0·0001). For the Jilin external validation set, sensitivity was 84·3% (95% CI 73·6–91·9) versus 92·9% (84·1–97·6; p=0·048) and specificity was 86·9% (95% CI 77·8–93·3) versus 57·1% (45·9–67·9; p<0·0001). For the Weihai external validation set, sensitivity was 84·7% (95% CI 77·0–90·7) versus 89·0% (81·9–94·0; p=0·25) and specificity was 87·8% (95% CI 81·6–92·5) versus 68·6% (60·7–75·8; p<0·0001).
The DCNN model showed similar sensitivity and improved specificity in identifying patients with thyroid cancer compared with a group of skilled radiologists. The improved technical performance of the DCNN model warrants further investigation as part of randomised clinical trials.
The Program for Changjiang Scholars and Innovative Research Team in University in China, and National Natural Science Foundation of China.
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