Recent increases in marine traffic in the Arctic have amplified the demand for reliable ice and marine environmental predictions. This article presents the verification of ice forecast skill from a new system implemented recently at the Canadian Meteorological Centre called the Global Ice Ocean Prediction System (GIOPS). GIOPS provides daily global ice and ocean analyses and 10-day forecasts on a 1/4• -resolution grid. GIOPS includes a multivariate ocean data assimilation system that combines satellite observations of sealevel anomaly and sea-surface temperature (SST) together with in situ observations of temperature and salinity. Ice analyses are produced using a 3D-Var method that assimilates satellite observations from SSM/I and SSMIS together with manual analyses from the Canadian Ice Service. Analyses of total ice concentration are projected onto the thickness categories used in the ice model using spatially and temporally varying weighting functions derived from ice model tendencies. This method may reduce deleterious impacts on the ice thickness distribution when assimilating ice concentration, as it can directly modulate (and reverse) nonlinear processes such as ice deformation. An objective verification of sea ice forecasts is made using two methods: analysis-based error assessment focusing on the marginal ice zone, and a contingency table approach to evaluate ice extent as compared to an independent analysis. Together the methods demonstrate a consistent picture of skilful medium-range forecasts in both the Northern and Southern Hemispheres as compared to persistence. Using the contingency table approach, GIOPS forecasts show a significant open-water bias during spring and summer. However, this bias depends on the choice of threshold used. Ice forecast skill is found to be highly sensitive to the assimilation of SST near the ice edge. Improved observational coverage in these areas (including salinity) would be extremely valuable for further improvement in ice forecast skill.

Porous liquids (PLs), an emerging type of flowing liquid materials that combine the merits of porous solids and flowing liquids, have garnered immense attention since the concept of PLs was proposed in 2007. Meanwhile, PLs have witnessed growing success in versatile synthesis strategies and emerging applications, especially since 2017. Given the lack of a timely comprehensive review, developing a prompt summary with a comprehensive understanding is undoubtedly urgent. Thus, this critical review offers a comprehensive summary of the progress in fundamental chemistry, developmental history, synthetic strategies, and emerging applications of PLs. First, the fundamental chemistry and developmental history are reviewed. Then, the synthesis strategies of PLs are highlighted. Additionally, crosscutting studies of pure theoretical simulations are reviewed. Meanwhile, the daunting characterization issues of PLs are analyzed. Next, the state-of-the-art of PLs applications is reviewed in detail. In the end, perspectives regarding the remaining challenges and future directions for PLs are presented. It is speculated that this critical comprehensive review of PLs could inspire scientific communities who focus on the taskspecific materials for various applications, such as gas sorption, membrane separation, catalytic conversion, chiral separation, thermal management and electrolyte, and so on.

Porous liquids (PLs), an emerging kind of liquid materials with permanent porosity, have attracted increasing attention in gas capture. However, directly turning metal−organic frameworks (MOFs) into PLs via a covalent linkage surface engineering strategy has not been reported. Additionally, challenges including reducing the cost and simplifying the preparation process are daunting. Herein, we proposed a general method to transform Universitetet i Oslo (UiO)-66-OH MOFs into PLs by surface engineering with organosilane (OS) and oligomer species via covalent bonding linkage. The oligomer species endow UiO-66-OH with superior fluidity at room temperature. Meanwhile, the resulting PLs showed great potential in both CO 2 adsorption and CO 2 /N 2 selective separation. The residual porosity of PLs was verified by diverse characterizations and molecular simulations. Besides, CO 2 selective capture sites were determined by grand canonical Monte Carlo (GCMC) simulation. Furthermore, the universality of the covalent linkage surface engineering strategy was confirmed using different classes of oligomer species and another MOF (ZIF-8-bearing amino groups). Notably, this strategy can be extended to construct other PLs by taking advantages of the rich library of oligomer species, thus making PLs promising candidates for further applications in energy and environment-related fields, such as gas capture, separation, and catalysis.

Torealize intelligent and personalized medicine, it is a huge challenge to develop a hydrogel dressing that can be used as a sensor to monitor human health in real-time while promoting wound healing. Herein, an injectable, self-healing, and conductive chitosan-based (CPT) hydrogel with pH responsiveness and intrinsic antibacterial properties was fabricated via a Schiff base linkage and a hydrogen bond. Due to the introduction of Schiff base bonds, the injectable CPT hydrogel exhibits various excellent properties, such as pH responsiveness to sol–gel transition, self-healing properties, and broad-spectrum antibacterial properties even without additional antibacterial agents. In vitro experiments verify the excellent biocompatibility of the as-prepared hydrogel. An in vivo experiment in a mouse full-thickness skin-wound model was performed to confirm the outstanding effect on wound healing. Meanwhile, as epidermal sensors, the conductive hydrogel that perceives various human activities in real-time could provide the real-time analysis of the patient’s healthcare information. Based on these excellent properties, the CPT hydrogel, as a biological dressing with a sensing function, lays a solid foundation for the further realization of personalized medicine.

No abstract

No abstract

To investigate the influence of urea–formaldehyde resin (UF resin) adhesive on the thermal utilization of wood waste, the pyrolysis of particleboard and its main components (poplar and UF resin) are studied in this paper.

The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of an LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation's alertness and numerical stability so as to achieve an accurate online adaption of model parameters.Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of an LIB. Simulation and experimental studies are performed to verify the feasibility of the proposed data-model fusion method. The proposed method is shown to effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high stability, and high accuracy.