Mixed-matrix membranes (MMMs), which combine porous materials with a polymeric matrix, have gained considerable research interest in the field of gas separation due to their complementary characteristics and cooperative activation. The tailorability and diversity of porous materials grant MMMs extendable functionalities and outstanding separation performance. To achieve the full potential of MMMs, researchers have focused on the rational matching of porous fillers with polymeric matrixes to achieve enhanced compatibility at the interfaces of these materials. In this review, we highlight state-of-the-art advances in combining metal–organic frameworks (MOFs) and metal–organic cages (MOCs) with polymeric matrixes to fabricate MMMs using different strategies. We further discuss the opportunities and challenges presented by the future development of MMMs, with the aim of boosting MMM fabrication with judicious material design and selection.
Developing efficient adsorbents with high uptake and selectivity for separation and recovery of C2H6 and C3H8 from natural gas is an important but challenging task. In this work, we demonstrate that high surface polarity and suitable pore diameter are two key factors that can synergistically enhance the separation performance, exemplified by metal–organic framework (MOF)-303 and Matériaux de l'Institut Lavoisier (MIL)-160, both possessing one-dimensional (1D) open channels with high density of heteroatoms and desired pore size (5–7 Å). Significantly, the uptake of MOF-303 for C3H8 is up to 3.38 mmol∙g−1 at 298 K and 5 kPa with a record-high C3H8/CH4 (5:85, v/v) ideal adsorbed solution theory (IAST) selectivity of 5114 among all reported MOFs. In addition, MOF-303 also displays high C2H6 uptake capacity (at 10 kPa) and C2H6/CH4 (10:85, v/v) selectivity, reaching 1.59 mmol∙g−1 and 26, respectively. Owing to the larger pore diameter and lower density of heteroatoms within its 1D channels, MIL-160 shows apparently lower uptake and selectivity compared to those of MOF-303, though the values exceed those of majority of reported MOFs. Density functional theory (DFT) calculations verify that the high surface polarity and the suitable pore diameter synergistically enhance the affinity of the frameworks toward C3H8 and C2H6, giving rise to the high loading capacity and selectivity for C3H8 and C2H6. Both MOFs feature remarkable moisture stability without structural change upon exposure to 95% relative humidity (RH) for a month. In addition, synthesis of both compounds can be readily scaled up through one-pot reactions to afford about 5 g samples with high crystallinity. Finally, the substantial potential of MOF-303 and MIL-160 as advanced adsorbents for efficient separation of C3H8/C2H6/CH4 has been demonstrated by ternary breakthrough experiments, regeneration tests, and cyclic evaluation. The excellent separation performance, high stability, low cost, and good scalability endow both MOFs promising adsorbents for natural gas purification and recovery of C2H6 and C3H8.
The adsorptive separation of CH4 from CO2 is a promising process for upgrading natural gas. However, thermodynamically selective adsorbents exhibit a strong affinity for CO2 and thus require a high energy compensation for regeneration. Instead, kinetic separation is preferred for a pressure swing adsorption process, although precise control of the aperture size to achieve a tremendous discrepancy in diffusion rates remains challenging. Here, we report a guest solvent-directed strategy for fine-tuning the pore size at a sub-angstrom precision to realize highly efficient kinetic separation. A series of metal–organic frameworks (MOFs) with isomeric pore surface chemistry were constructed from 4,4′-(hexafluoroisopropylidene)-bis(benzoic acid) and dicopper paddlewheel notes. The resultant CuFMOF·CH3OH (CuFMOF-c) exhibits an excellent kinetic separation performance thanks to a periodically expanding and contracting aperture with the ideal bottleneck size, which enables the effective trapping of CO2 and impedes the diffusion of CH4, offering an ultrahigh kinetic selectivity (273.5) and equilibrium-kinetic combined selectivity (64.2). Molecular dynamics calculations elucidate the separation mechanism, and breakthrough experiments validate the separation performance.
Achieving a water–oil interface imbalance has been identified as a critical factor in the demulsification of water-in-oil emulsions. However, conventional demulsifying membranes generally break the interface balance by depending on a relatively high transmembrane pressure. Here, we present a ″contact demulsification″ concept to naturally and quickly achieve disruption of the water–oil interface balance. For this purpose, a novel demulsifying membrane with a high flux of the organic component has been developed via the simple vacuum assembly of zeolitic imidazolate framework-8 (ZIF-8)@reduced graphene oxide (rGO) microspheres (ZGS) on a polytetrafluoroethylene (PTFE) support, followed by immobilization processing in a polydimethylsiloxane (PDMS) crosslinking solution. Due to the micro-nano hierarchies of the ZGS, the prepared ZIF-8@rGO@PDMS/PTFE (ZGPP) membranes feature a unique superhydrophobic surface, which results in a water–oil interface imbalance when a surfactant-stabilized water-in-oil emulsion comes into contact with the membrane surface. Under a low transmembrane pressure of 0.15 bar (15 kPa), such membranes show an excellent separation efficiency (~99.57%) and a high flux of 2254 L·m–2·h–1, even for surfactant-stabilized nanoscale water-in-toluene emulsions (with an average droplet size of 57 nm). This ″contact demulsification″ concept paves the way for developing next-generation demulsifying membranes for water-in-oil emulsion separation.
Freeze-tolerant hydrogels can regulate the freezing behavior of the water inside them at subzero temperatures, thus maintaining their exceptional properties (e.g., intelligent responsiveness and liquid transporting) and extending their applications under cold conditions. Herein, a series of aggregation-induced emission (AIE)-active freeze-tolerant hydrogels are developed, which enable information encryption and decryption at subzero temperatures. The hydrogels possess varied freezing temperatures (Tf) depending on their betaine concentration. Above/below Tf, the information in the hydrogels that is encoded by means of AIE luminogens presents turn-off/-on fluorescence, thereby enabling the use of these hydrogels for information encryption and decryption. Moreover, by tuning the cooling procedures or introducing photothermal copper sulfide nanoparticles into the hydrogels via an in situ sulfidation process, together with certain irradiation conditions, multistage information readouts can be obtained, significantly enhancing the information security. Finally, because the decrypted information in the hydrogels is irreversibly sensitive to temperature fluctuation, external energy-free cryogenic anticounterfeiting labels built with the hydrogels are demonstrated, which can realize the visual and real-time viability monitoring of cryopreserved biosamples (e.g., mesenchymal stem cells and red blood cells) during cold-chain transportation (–80 °C).
Congenital heart disease (CHD) is the leading cause of infant death. An artificial intelligence (AI)-based CHD diagnosis network (CHDNet) is an echocardiogram video-based binary classification model that judges whether echocardiogram videos contain heart defects. Existing CHDNets have shown performances comparable to or even better than medical experts, but their unreliability on cases outside of the training set has become the main bottleneck for their deployment. This is a common problem for most AI-based diagnostic approaches. Here, to overcome this challenge, we present two essential mechanisms— Bayesian inference and dynamic neural feedback—to respectively measure and improve the diagnostic reliability of AI. The former easily makes the neural network output its reliability instead of a single prediction result, while the latter is a computational neural feedback cell that allows the neural network to feed knowledge from the output layer back to the shallow layers and enables the neural network to selectively activate relevant neurons. To evaluate the effectiveness of these two mechanisms, we trained CHDNets on 4151 echocardiogram videos containing three common CHD defects and tested them on an internal test set of 1037 echocardiogram videos and an external set of 692 videos that were newly collected from other cardiovascular imaging devices. Each echocardiogram video corresponds to a unique patient and a unique visit. We demonstrate on various neural network architectures how the reliability obtained by Bayesian inference interprets and quantifies the significant performance difference between internal and external test sets of neural networks, and how the devised feedback cell helps the neural networks to maintain high accuracy and reliability, despite the input being corrupted by noise or when using an external test set.
Ovulation induction is a first-line medical treatment for infertility in polycystic ovary syndrome (PCOS). Poor ovulation responses are assumed to be due to insulin resistance and hyperandrogenism. In a prospective cohort (PCOSAct) of 1000 infertile patients with PCOS, whole-exome plus targeted singlenucleotide polymorphism (SNP) sequencing and comprehensive metabolomic profiling were conducted. Significant genome-wide common variants and rare mutations associated with anovulation were identified, and a prediction model was built using machine learning. Common variants in zinc-finger protein 438 gene (ZNF438) indexed by rs2994652 (p = 2.47 × 10–8) and a rare functional mutation in REC114 (rs182542888, p = 5.79 × 10–6) were significantly associated with failure of ovulation induction. Women carrying the A allele of rs2994652 and REC114 p.Val101Leu (rs182542888) had lower ovulation (odds ratio (OR) = 1.96, 95% confidence interval (95%CI) = 1.55–2.49; OR = 11.52, 95%CI = 3.08–43.05, respectively) and prolonged time to ovulation (mean = 56.7 versus (vs) 49.0 days, p < 0.001; 78.1 vs 68.6 days, p = 0.014, respectively). L-phenylalanine was found to be increased and correlated with the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) index (r = 0.22, p = 0.050) and fasting glucose (r = 0.33, p = 0.003) for rs2994652, while arachidonic acid metabolism was found to be decreased and associated with increased anti-Müllerian hormone (AMH; r = –0.51, p = 0.01) and total testosterone (TT; r = –0.71, p = 0.02) for rs182542888. A combined model of genetic variants, metabolites, and clinical features increased the prediction of ovulation (area under the curve (AUC) = 76.7%). Common variants in ZNF438 and rare functional mutations in REC114, associated with phenylalanine and arachidonic acid metabolites, contributed to the failure of infertility treatment in women with PCOS.
Feeding is vital for animal growth and the maintenance of health. However, the underlying mechanisms that mediate dietary performance have long been a so-called black box. It is only during recent years that studies have demonstrated that nutrients act as signals that can be sensed by cells and organisms and that play vital roles in gene expression and metabolism. Multiple signaling pathways have been identified as being responsible for the sensing of discrete nutrients. While successes have been achieved in the exploitation of nutrient-sensing signals in drug discovery and disease control, applications based on the sensing and metabolic control of major nutrients (proteins, lipids, carbohydrates, etc.) in aquaculture and land-farmed animals remain in their infancy. We thus provide a tentative perspective on future research topics and applications of nutrient sensing in animal nutrition.
Cuticular wax plays a major role in the growth and storage of plant fruits. The cuticular wax coating, which covers the outermost layer of a fruit's epidermal cells, is insoluble in water. Cuticular wax is mainly composed of very long-chain fatty acids (VLCFAs); their derivatives, including esters, primary alcohols, secondary alcohols, aldehydes, and ketones; and triterpenoids. This complex mixture of lipids is probably biosynthesized in the epidermal cells of most plants and exuded onto the surface. Cuticular wax not only makes the fruit less susceptible to microbial infection but also reduces mechanical damage to the fruit, thereby maintaining the fruit's commodity value. To date, research has mostly focused on the changes, function, and regulation of fruit wax before harvest, while ignoring the changes and functions of wax in fruit storage. This paper reviews on the composition, structure, and metabolic regulation of cuticular wax in fruits. It also focuses on postharvest factors affecting wax composition, such as storage temperature, relative humidity (RH), gas atmosphere, and as exogenous hormones; and the effects of wax on fruit postharvest quality, including water dispersion, fruit softening, physiological disorders, and disease resistance. These summaries may be of assistance in better understanding the changes in cuticular wax in postharvest fruit and the resulting effects on fruit quality.
Overhead transmission lines (OTLs) have always been the major means of power delivery. With the significant increase of transmission voltage and transmission capacity, the dimensions of transmission towers are increasing accordingly, resulting in extensive occupation of land resources. Towers with composite cross arms are a promising solution to this problem, considering the remarkable performance of composite line insulators. In this research, a full-scale alternating current (AC) 500 kV model of a transmission tower with composite cross arms is manufactured and applied under a lightning overvoltage of different polarities. The developing process of streamer-leader discharge is recorded with a high-speed camera, and the major path of the flashover is identified. The flashover voltages are measured and corrected to standard conditions while considering the air humidity and air density, and clearly confirm the polarity effect. The tower's lightning-withstand level is calculated based on the tower structure and the flashover characteristics. Based on the results obtained from full-scale experiments, the feasibility of composite cross arms is confirmed, and a structural optimization is proposed.
The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a national initiative aimed at building a world-class city cluster in China and whose trends, socioeconomic drivers of CO2 emissions, and mitigation pathways are of great significance to the high-quality regional economic development. This study compiled the CO2 emission inventories of the GBA from 2000 to 2019 and explored the key drivers of CO2 emissions using the logarithmic mean Divisia index method. The results showed that CO2 emissions in GBA slowed significantly after 2017 and have already been decoupled from gross domestic product (GDP) growth. Economic growth and energy intensity are the major factors driving and inhibiting the increase in GBA's CO2 emissions, respectively. The energy production and heavy manufacturing sectors have reduced their roles in driving the growth of GBA's CO2 emissions, with the service sector becoming the main driver. GBA achieved remarkable results in low-carbon development through industrial restructuring and upgrading. Industrial upgrades in Shenzhen and Hong Kong and technological advances in Shenzhen, Guangzhou, and Foshan have significantly curbed the growth in the GBA's CO2 emissions. The heterogeneity of cities in the GBA greatly increases the complexity of formalizing the allocation of emission reduction tasks and developing a roadmap for regional carbon neutrality. Graded emission reduction strategies and carbon peaking and neutrality policy recommendations for GBA cities are proposed. This study provides a scientific basis for the development of an action program for carbon peaking and neutrality in GBA cities and low-carbon development plans for other cities and regions.
Nanotechnology presents innovative solutions in advanced water treatment; however, its application is limited by the challenging large-scale production of ultrasmall (< 5 nm) nanoparticles (NPs) with extraordinary decontamination reactivity and the difficulty of handling such tiny NPs in engineering. To address these challenges, we propose a straightforward route for synthesizing ultrasmall NPs using the commercial gel-type anion exchange resin N201 as the host. N201 is a millimeter-scale poly(styrene-co-divinylbenzene) bead modified with quaternary ammonium groups. Nanoparticles of hydrated ferric oxide (HFO), hydrated manganese oxide (HMO), cadmium sulfide (CdS), and zero-valent iron (ZVI) were obtained through simple impregnation-precipitation in N201, and all of the NPs possessed an ultrasmall size of sub-5 nm. A pilot-scale production assay indicated that the synthetic system could be enlarged proportionally to prepare massive sub-5 nm HFO. Regarding the underlying mechanism, each N201 bead contained a continuous water phase, allowing the rapid diffusion of the reactants (7 s for diffusion from the bead surface to the center), resulting in burst nucleation to produce ultrasmall NPs with a narrow size distribution. Moreover, the crosslinked polymer chains provided a confined space (< 5 nm diameter) to prevent the excessive growth of the formed NPs. Owing to the millimetric N201 host, the resultant nanocomposite can be applied in flow-through systems. The batch and column adsorption assays demonstrate the dramatically enhanced adsorption performance of the ultrasmall HFO toward As(III/V) than the ∼17 nm analogs. This study can advance the widespread use of nanotechnology in practical water treatment.
This paper investigates two novel polarization- and pattern-diversity glass dielectric resonator antennas (DRAs), both of which are for tri-band (2.4, 5.2, and 5.8 GHz) wireless fidelity (WiFi) applications. It also investigates what type of diversity antenna is most suitable for WiFi router applications by comparing the two DRAs, along with a new space-diversity glass DRA. These three diversity glass DRAs are also compared with a commercial space-diversity monopole pair to benchmark the performance of the glass DRA in WiFi router applications. In our polarization-diversity antenna, a double-port feeding scheme is developed to excite different DRA modes. The frequencies of the DRA modes are tuned by using a stepped DRA. For the pattern-diversity design, a stacked DRA is introduced to broaden the bandwidth for both the conical and broadside radiation modes. All three of the new diversity antennas were fabricated and measured to verify the simulations. In our experiment, the bit error rate (BER) of the three diversity glass antennas and the reference space-diversity monopole antenna was also measured, and the results are compared and discussed. It is found that the polarization-diversity omnidirectional DRA has the most stable BER among the three.
The unique motion styles of flagella and cilia (i.e., planar/helical waveform propulsion of flagella and twodimensional (2D)/three-dimensional (3D) asymmetric ciliary beating), play a key role in many biological activities and inspire lots of bionic designs, especially miniature robotic systems. However, quite different to the fact in nature that microorganisms can evolve diverse motions from the homologous bio-structure (9 + 2 axoneme structure), current bionics can still not find an effective engineering solution to achieve such wisdom. Herein, by investigating the inner structure of flagella/cilia and their intrinsic driven mechanisms, we derive a unified physical model to describe the microtubules' bending and the constructed external motions. Then, we propose a three-channel based tubular actuation concept and correspondingly fabricate an actuator via a rod-embedded casting process. By sequencing the actuation of each channel, our design can not only reproduce the diverse 2D/3D flagellar/ciliary motility in nature, but also extrapolate a variety of symmetry-breaking ciliary beating modes for effective propulsion at low Reynolds number. This study deepens our understanding of the propulsion mechanism of microorganisms and provides new inspirations for the design of biomimetic systems, which may find significant applications in a wide spectrum of engineering fields.
Molten pool characteristics have a significant effect on printing quality in laser powder bed fusion (PBF), and quantitative predictions of printing parameters and molten pool dimensions are critical to the intelligent control of the complex processes in PBF. Thus far, bidirectional predictions of printing parameters and molten pool dimensions have been challenging due to the highly nonlinear correlations involved. To
address this issue, we integrate an experiment on molten pool characteristics, a mechanistic model, and deep learning to achieve both forward and inverse predictions of key parameters and molten pool characteristics during laser PBF. The experiment provides fundamental data, the mechanistic model significantly augments the dataset, and the multilayer perceptron (MLP) deep learning model predicts the molten pool dimensions and process parameters based on the dataset built from the experiment and the mechanistic model. The results show that bidirectional predictions of the molten pool dimensions and process parameters can be realized, with the highest prediction accuracies approaching 99.9% and mean prediction accuracies of over 90.0%. Moreover, the prediction accuracy of the MLP model is closely related to the characteristics of the dataset—that is, the learnability of the dataset has a crucial impact on the prediction accuracy. The highest prediction accuracy is 97.3% with enhancement of the dataset via the mechanistic model, while the highest prediction accuracy is 68.3% when using only the experimental dataset. The prediction accuracy of the MLP model largely depends on the quality of the dataset as well. The research results demonstrate that bidirectional predictions of complex correlations using MLP are feasible for laser PBF, and offer a novel and useful framework for the determination of process conditions and outcomes for intelligent additive manufacturing.