Hollow spherical SiO2 micro-container encapsulation of LiCl for high-performance simultaneous heat reallocation and seawater desalination

Energy & fresh water have both become scarce resources in the modern era of human society. Sorption-based technology is environmentally friendly and energy-efficient and can be driven by low-grade energy to transfer energy and produce fresh water. Here, we report a solid sorbent fabricated by encapsulating a hygroscopic salt, lithium chloride (LiCl), inside micro-sized hollow-structured SiO2. This composite sorbent (LiCl@HS) exhibits 6 times faster water vapor sorption kinetics than pure LiCl and a water vapor sorption capacity of 1.7 kg kg-1 at a relative humidity (RH) of 50%, which is the highest ever reported for any solid sorbent in the literature. The low regeneration temperature (<80 °C) and good cycling stability ensure the feasibility of the composite sorbent for use in practical applications. The thermodynamic calculations reveal that the sorbent is able to continuously supply 20 °C temperature lift with a maximum coefficient of performance (COP) for cooling of 0.97 and COP for heating of 1.89 while simultaneously producing 9.05 kg potable water per kilogram sorbent daily using seawater as the source water and solar energy as the sole energy source. A homemade system is developed and its practical performance in providing seasonally switchable heating and cooling along with clean water production from source water with an impaired quality is successfully verified, indicating its great potential

Polycaprolactone/multi-walled carbon nanotube nerve guidance conduits with tunable channels fabricated via novel extrusion-stretching method for peripheral nerve repair

Multi-channeled nerve guidance conduit is a prospective way to repair peripheral nerve injury, which is still difficult to be fabricated. A novel extrusion-stretching method was utilized in this study to produce multi-walled carbon nanotubes (MWCNTs) loaded multi-channeled nerve conduits with improved flexibility and versatility. The channels and geography of the conduits were tunable. The results showed that the mechanical properties of the multi-channeled nerve conduits were suitable for peripheral nerve restoration. MWCNTs increased the biocompatibility of the multi-channeled nerve conduits. This study proved that the MWCNTs loaded multi-channeled produced by extrusion-stretching method have great potential to repair peripheral nerve injury

Genome Sequence Variation in the Constricta Strain Dramatically Alters the Protein Interaction and Localization Map of \u3cem\u3ePotato Yellow Dwarf Virus\u3c/em\u3e

The genome sequence of the constricta strain of Potato yellow dwarf virus (CYDV) was determined to be 12 792 nt long and organized into seven ORFs with the gene order 3′-N-X-P-Y-M-G-L-5′, which encodes the nucleocapsid, phospho, movement, matrix, glyco, and RNA-dependent RNA polymerase proteins, respectively, except for X, which is of unknown function. Cloned ORFs for each gene, except L, were used to construct a protein interaction and localization map (PILM) for this virus, which shares greater than 80 % amino acid similarity in all ORFs except X and P with the sanguinolenta strain of this species (SYDV). Protein localization patterns and interactions unique to each viral strain were identified, resulting in strain-specific PILMs. Localization of CYDV and SYDV proteins in virus-infected cells mapped subcellular loci likely to be sites of replication, morphogenesis and movement

The Altered Reconfiguration Pattern of Brain Modular Architecture Regulates Cognitive Function in Cerebral Small Vessel Disease

Background: Cerebral small vessel disease (SVD) is a common cause of cognitive dysfunction. However, little is known whether the altered reconfiguration pattern of brain modular architecture regulates cognitive dysfunction in SVD.Methods: We recruited 25 cases of SVD without cognitive impairment (SVD-NCI) and 24 cases of SVD with mild cognitive impairment (SVD-MCI). According to the Framingham Stroke Risk Profile, healthy controls (HC) were divided into 17 subjects (HC-low risk) and 19 subjects (HC-high risk). All individuals underwent resting-state functional magnetic resonance imaging and cognitive assessments. Graph-theoretical analysis was used to explore alterations in the modular organization of functional brain networks. Multiple regression and mediation analyses were performed to investigate the relationship between MRI markers, network metrics and cognitive performance.Results: We identified four modules corresponding to the default mode network (DMN), executive control network (ECN), sensorimotor network and visual network. With increasing vascular risk factors, the inter- and intranetwork compensation of the ECN and a relatively reserved DMN itself were observed in individuals at high risk for SVD. With declining cognitive ability, SVD-MCI showed a disrupted ECN intranetwork and increased DMN connection. Furthermore, the intermodule connectivity of the right inferior frontal gyrus of the ECN mediated the relationship between periventricular white matter hyperintensities and visuospatial processing in SVD-MCI.Conclusions: The reconfiguration pattern of the modular architecture within/between the DMN and ECN advances our understanding of the neural underpinning in response to vascular risk and SVD burden. These observations may provide novel insight into the underlying neural mechanism of SVD-related cognitive impairment and may serve as a potential non-invasive biomarker to predict and monitor disease progression

A Theoretical Model to Predict Both Horizontal Displacement and Vertical Displacement for Electromagnetic Induction-Based Deep Displacement Sensors

Deep displacement observation is one basic means of landslide dynamic study and early warning monitoring and a key part of engineering geological investigation. In our previous work, we proposed a novel electromagnetic induction-based deep displacement sensor (I-type) to predict deep horizontal displacement and a theoretical model called equation-based equivalent loop approach (EELA) to describe its sensing characters. However in many landslide and related geological engineering cases, both horizontal displacement and vertical displacement vary apparently and dynamically so both may require monitoring. In this study, a II-type deep displacement sensor is designed by revising our I-type sensor to simultaneously monitor the deep horizontal displacement and vertical displacement variations at different depths within a sliding mass. Meanwhile, a new theoretical modeling called the numerical integration-based equivalent loop approach (NIELA) has been proposed to quantitatively depict II-type sensors’ mutual inductance properties with respect to predicted horizontal displacements and vertical displacements. After detailed examinations and comparative studies between measured mutual inductance voltage, NIELA-based mutual inductance and EELA-based mutual inductance, NIELA has verified to be an effective and quite accurate analytic model for characterization of II-type sensors. The NIELA model is widely applicable for II-type sensors’ monitoring on all kinds of landslides and other related geohazards with satisfactory estimation accuracy and calculation efficiency