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With the acceleration of global industrialization, the sharp increase in carbon dioxide (CO2) emissions has emerged as a pivotal factor exacerbating the greenhouse effect and global warming, necessitating urgent measures for emission reduction and resource utilization. Consequently, this area has become a focal point of international research efforts. Among the promising materials under investigation, metal-organic frameworks (MOFs) stand out due to their unique structural attributes, which confer remarkable potential in photocatalytic and electrocatalytic CO2 reduction. These frameworks offer high surface area, tunable porosity, and versatile chemical functionalities, making them ideal candidates for CO2 capture and conversion. This paper delves into the recent research progress in MOFs for photocatalytic and electrocatalytic CO2 reduction, offering comparative analyses of reaction mechanisms, product distributions, and differences in catalytic processes. Furthermore, it explores the factors influencing catalyst stability, aiming to elucidate strategies for enhancing the efficiency and durability of MOF-based systems, thereby not only providing innovative pathways for the sustainable conversion and utilization of CO2 but also advancing the cause of green chemistry.

Polar petroleum components enter marine environments through oil spills and natural seepages each year. Lately, they are receiving increased attention due to their potential toxicity to marine organisms and persistence in the environment. We conducted a laboratory experiment and employed state-of-the-art Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) to characterize the polar petroleum components within two operationally-defined seawater fractions: the water-soluble fraction (WSF), which includes only water-soluble molecules, and the water-accommodated fraction (WAF), which includes WSF and microscopic oil droplets. Our results show that compounds with higher heteroatom (N, S, O) to carbon ratios (NSO:C) than the parent oil were selectively partitioned into seawater in both fractions, reflecting the influence of polarity on aqueous solubility. WAF and WSF were compositionally distinct, with unique distributions of compounds across a range of hydrophobicity. These compositional differences will likely result in disparate impacts on environmental health and organismal toxicity, and thus highlight the need to distinguish between these often-interchangeable terminologies in toxicology studies. We use an empirical model to estimate hydrophobicity character for individual molecules within these complex mixtures and provide an estimate of the potential environmental impacts of different crude oil components.

Resistive random access memory (RRAM) devices are receiving increasing extensive attention due to their enhanced properties such as fast operation speed, simple device structure, low power consumption, good scalability potential and so on, and are currently considered to be one of the next-generation alternatives to traditional memory. In this review, an overview of RRAM devices is demonstrated in terms of thin film materials investigation on electrode and function layer, switching mechanisms and artificial intelligence applications. Compared with the well-developed application of inorganic thin film materials (oxides, solid electrolyte and two-dimensional (2D) materials) in RRAM devices, organic thin film materials (biological and polymer materials) application is considered to be the candidate with significant potential. The performance of RRAM devices is closely related to the investigation of switching mechanisms in this review, including thermal-chemical mechanism (TCM), valance change mechanism (VCM) and electrochemical metallization (ECM). Finally, the bionic synaptic application of RRAM devices is under intensive consideration, its main characteristics such as potentiation/depression response, short-/long-term plasticity (STP/LTP), transition from short-term memory to long-term memory (STM to LTM) and spike-time-dependent plasticity (STDP) reveal the great potential of RRAM devices in the field of neuromorphic application.

Sepsis-induced immunosuppression leads to poor prognosis. Circulating lymphocyte count (LC), as an easily accessible clinical marker, closely reflects the immune status of sepsis. The study aims to perform immune phenotyping of sepsis patients using dynamic LC for early identification of high-risk individuals. A latent class trajectory model (LCTM) was used to analyze the dynamic trajectories of lymphocyte count (LC) based on repeated measurements obtained within at least two measurements of lymphocyte count (LC) within the first 24 h after sepsis diagnosis, followed by two more between day 2 and day 7. Survival differences among subphenotypes were assessed using Kaplan–Meier curves and Cox regression. Feature selection was conducted via the Boruta algorithm, and a high-precision machine learning model was developed to predict the target trajectory. Model interpretability was ensured through SHapley Additive exPlanations (SHAP). The predictive performance of the model for ICU mortality was assessed using the receiver operating characteristic (ROC) curve. The derivation cohort included 2085 sepsis patients from the China Multicenter Sepsis database, and the external validation cohort of 1299 sepsis patients. We identified four trajectory patterns of LC dynamics, among which the persistent lymphopenia (PL) subgroup exhibited the highest disease severity and poorest prognosis. The trajectory model demonstrated consistent patterns in external validation. Six machine learning models were utilized to determine the best model to identify the PL subphenotype, and an online prediction tool was developed for clinical application. Incorporating the PL trajectory subphenotype significantly improved the predictive performance for ICU mortality. Dynamic LC trajectories effectively capture immunological heterogeneity in sepsis, encompassing immunocompromised and immunocompetent hosts. These findings underscore the importance of early identification of patients with persistent lymphopenia to better target populations for future sepsis immunotherapy.

Energy collection ways using solar energy, wave, wind, or mechanical energy have attracted widespread attention for small self-powered electronic devices with low power consumption, such as sensors, wearable devices, electronic skin, and implantable devices. Among them, triboelectric nanogenerator (TENG) operated by coupling effect of triboelectrification and electrostatic induction has gradually gained prominence due to its advantages such as low cost, lightweight, high degree of freedom in material selection, large power, and high applicability. The device with a single energy exchange mechanism is limited by its conversion efficiency and work environment and cannot achieve the maximum conversion of energy. Thus, this article reviews the research status of different types of hybrid generators based on TENG in recent years. Hybrid energy generators will improve the output performance though the integration of different energy exchange methods, which have an excellent application prospect. From the perspective of energy complementation, it can be divided into harvesting mechanical energy by various principles, combining with harvesters of other clean energy, and converting mechanical energy or various energy sources into hydrogen energy. For integrating multitype energy harvesters, mechanism of single device and structural design of integrated units for different application scenarios are summarized. The expanding energy harvesting efficiency of the hybrid TENG makes the scheme of self-charging unit to power intelligent mobile electronic feasible and has practical significance for the development of self-powered sensor network.

Highlights Owing to the great robustness, continuous conductivity, and geometric construction of a steel wire electrode, the FST–TENGs demonstrate high stability, stretchability, and even tailorability. By knitting several FST–TENGs to be a fabric or a bracelet worn on the human body, it enables to harvest human motion energy. The FST–TENGs can also be woven on dorsum of glove to monitor the movements of gesture. Continuous deforming always leads to the performance degradation of a flexible triboelectric nanogenerator due to the Young’s modulus mismatch of different functional layers. In this work, we fabricated a fiber-shaped stretchable and tailorable triboelectric nanogenerator (FST–TENG) based on the geometric construction of a steel wire as electrode and ingenious selection of silicone rubber as triboelectric layer. Owing to the great robustness and continuous conductivity, the FST–TENGs demonstrate high stability, stretchability, and even tailorability. For a single device with ~ 6 cm in length and ~ 3 mm in diameter, the open-circuit voltage of ~ 59.7 V, transferred charge of ~ 23.7 nC, short-circuit current of ~ 2.67 μA and average power of ~ 2.13 μW can be obtained at 2.5 Hz. By knitting several FST–TENGs to be a fabric or a bracelet, it enables to harvest human motion energy and then to drive a wearable electronic device. Finally, it can also be woven on dorsum of glove to monitor the movements of gesture, which can recognize every single finger, different bending angle, and numbers of bent finger by analyzing voltage signals.

In the face of complex pressure stimuli, pressure sensor is required to sense the magnitude of static force and sensitive to transient mechanical stimuli. However, an individual sensing mechanism has difficulty meeting practical needs simultaneously. In this work, an MXene/cellulose nanofiber (CNF) aerogel@PDMS‐based dual‐modal pressure sensor is reported for complex stimuli monitoring. The aerogel‐based sensing material is fabricated through MXene nanosheets and CNFs. Aerogel ice crystals sublimate and then form a 3D porous structure during vacuum freeze‐drying. After attaching PDMS dilution, aerogels achieve >200 reversible compressions, and hysteresis energy is reduced by 57.8%. By utilizing both triboelectric and piezoresistive properties of MXene/CNF aerogel@PDMS, a dual‐modal pressure sensor is achieved. The triboelectric effect acquires high sensitivity of 26.95 kPa−1 under low pressure (3.46 Pa–3.32 kPa) and responds to vibrations up to 1000 Hz. On the basis of variable resistances of aerogels, the piezoresistive effect can be used to identify static pressures stably (167 kPa−1, 1.56–26.64 kPa). Combining two effects broadens the lower limit of high‐sensitivity monitoring, realizing static‐dynamic detection simultaneously and breaking the frequency limit of piezoresistive materials. Finally, the dual‐modal pressure sensor is demonstrated to monitor complex physiological and physical signals, such as pronunciation, gestures, and tone recognition. A triboelectric‐piezoresistive dual‐modal pressure sensor based on superelastic and ultrasoft MXene/cellulose nanofiber (CNF) aerogel@PDMS is fabricated. The sensor exhibits high sensitivity and stable sensing performance via a dual mechanism. Through the effective combination of these two mechanisms, comprehensive monitoring of complex stimuli can be achieved, overcoming the response frequency limit of traditional materials.

The demand for portable and rapid identification of liquids has challenged traditional laboratory methods. Here, we propose a high-accuracy liquid identification strategy that integrates water droplet mechanics and solid–liquid interface contact electrification. By applying non-Hookean mechanical properties of droplets, we fabricate a lotus leaf-inspired ZnO–Polydimethylsiloxane (PDMS) superhydrophobic solid–liquid sensor. Based on the special mechanical–electric coupling interface, it achieves the highest droplet pressure sensitivity of 281 mV/Pa. We have made a breakthrough in detecting diverse solution composition with a high monitoring resolution of 5 nM metal ions and 0.1% of alcohol concentration. Through the design of double-stacked devices, triboelectric signals are able to be decoupled into mechanical and contact electrification dual-mode signals. With the integration of a gated recurrent unit (GRU) model, intelligent identification of ten liquids has reached an ultrahigh accuracy of 99%, opening up a pathway for portable liquid monitoring. This work introduces an accurate liquid identification strategy by integrating droplet mechanics and solid–liquid interface contact electrification in a ZnO–PDMS superhydrophobic solid–liquid sensor.

HighlightsThe porous-reinforcement microstructure is constructed by the self-assembly of silicone rubber adhering to the porous framework of the PU sponge.With the excellent performance both for tiny pressure and large mechanical stimuli, the flexible triboelectric patch can be used to monitor pulse wave and plantar pressure.A remote healthcare system for real-time physiological signal monitoring is proposed.Realizing real-time monitoring of physiological signals is vital for preventing and treating chronic diseases in elderly individuals. However, wearable sensors with low power consumption and high sensitivity to both weak physiological signals and large mechanical stimuli remain challenges. Here, a flexible triboelectric patch (FTEP) based on porous-reinforcement microstructures for remote health monitoring has been reported. The porous-reinforcement microstructure is constructed by the self-assembly of silicone rubber adhering to the porous framework of the PU sponge. The mechanical properties of the FTEP can be regulated by the concentrations of silicone rubber dilution. For pressure sensing, its sensitivity can be effectively improved fivefold compared to the device with a solid dielectric layer, reaching 5.93 kPa−1 under a pressure range of 0–5 kPa. In addition, the FTEP has a wide detection range up to 50 kPa with a sensitivity of 0.21 kPa−1. The porous microstructure makes the FTEP ultra-sensitive to external pressure, and the reinforcements endow the device with a greater deformation limit in a wide detection range. Finally, a novel concept of the wearable Internet of Healthcare (IoH) system for real-time physiological signal monitoring has been proposed, which could provide real-time physiological information for ambulatory personalized healthcare monitoring.

This paper presents an effective numerical simulation method for production prediction of in situ recovery of oil shale reservoirs with steam injection. In this method, finite volume-based discretization schemes of heat and mass transfer equations of the thermal compositional model are derived and used. The embedded discrete fracture model is used to accurately handle the fractured vertical well. A smooth non-linear solver is proposed to solve the global equations, then cell pressure, temperature, saturation, component mole fractions, and well production rates can be obtained. Compared with the existing commercial software, this new method can have a smoother non-linear solution and handle the complex fracture geometry theoretically. A numerical example is used to test this presented method and can realize accurate calculation results compared with CMG. Another numerical case with a hydraulic fracture and an open thermal boundary condition is implemented to validate the presented method and can effectively handle the actual situation of steam injection-assisted in situ recovery of oil shale, which was difficult to handle using previous methods.