Search

[Display omitted] • Hydrophobic eutectogel is facilely produced through the polymerization of hydrophobic monomers in a hydrophobic DES. • The hydrophobic eutectogel shows outstanding water resistance (water contact angle over 110°) and extraordinary durability (over 1000 testing cycles) underwater. • Hydrophobic eutectogel can be utilized as strain and pressure sensors underwater. • Hydrophobic eutectogel sensors can transmit information through Morse code, performing as a underwater communicator. Eutectogels derived from deep eutectic solvents (DESs) exhibit great potential for the fabrication of flexible sensors. However, the hygroscopicity of eutectogels hinders their applications in underwater sensing. In this work, a hydrophobic eutectogel with exceptional long-term underwater stability is produced through one-step polymerization of lauryl methacrylate and glycidyl methacrylate in a hydrophobic DES. The hydrophobic gel network and hydrophobic DES fulfill the eutectogel with outstanding water resistance (water contact angle > 110°) and excellent mechanical properties in an aqueous environment, thus leading to extraordinary durability (over 1000 testing cycles). Additionally, based on this eutectogel, underwater strain and pressure sensors with high sensitivity, rapid responsiveness, and superior durability were fabricated for accurate real-time monitoring of human activity. Furthermore, it has been demonstrated that the eutectogel sensor can transmit information through Morse code, performing as a wearable underwater communicator. This research provides an exemplary way for a demonstration method of hydrophobic eutectogel for durable underwater applications. [ABSTRACT FROM AUTHOR]

[Display omitted] Excessive CO 2 emissions have contributed to global environmental issues, driving the development of CO 2 capture adsorbents. Among various candidates, metal–organic frameworks (MOFs) are considered the most promising due to their unique microporous structure. Herein, a series of partially interpenetrated MOFs named UPC-XX were built to investigate the continuous enhancement in CO 2 capture performance via synergistic effects from functional group, pore size, and steric-hindrance using theoretical calculations. It's showed that the introduction of functional groups improved the structure polarity and created more adsorption sites, thus, enhanced CO 2 capture capacity. The pore size modification augments the exposure of adsorption sites to mitigate the negative impact of pore space and surface area reduction caused by the introduction of functional groups, thereby further increasing the CO 2 capture capacity. The steric-hindrance effect optimized the adsorption sites distribution, which hasn't been considered in the previous two regulation strategies, thus, further increased the CO 2 capture capacity. The results underscore UPC-MOFs as outstanding adsorbent materials, among the UPC-MOFs, UPC-OSO 3 -steric exhibited the highest CO 2 capture capacity of 12.69 mmol/g with selectivities of 1142.41 (CO 2 over N 2) and 507.42 (CO 2 over CH 4) at 1.0 bar, 298 K. And the synergistic effect mechanisms of functional group, structure size, and steric hindrance were elucidated through theoretical calculations analyzing pore characteristics, gas distribution, isosteric heat, and van der Waals/Coulomb interactions. [ABSTRACT FROM AUTHOR]

• A novel hybrid residual manifold convolutional neural network is proposed for sleep staging. • Mitigating the insufficient data problem by leveraging semi-supervised scheme. • Applying the channel attention mechanism for feature extraction. Analysis of electroencephalogram (EEG) is a crucial diagnostic criterion for many sleep disorders, of which sleep staging is an important component. Manual stage classification is a labor-intensive process and usually suffered from many subjective factors. Recently, more and more computer-aided techniques have been applied to this task, among which deep convolutional neural network has been performing well as an effective automatic classification model. Despite some comprehensive models have been developed to improve classification results, the accuracy for clinical applications has not been reached due to the lack of sufficient labeled data and the limitation of extracting latent discriminative EEG features. Therefore, we propose a novel hybrid manifold-deep convolutional neural network with hyperbolic attention. To overcome the shortage of labeled data, we update the semi-supervised training scheme as an optimal solution. In order to extract the latent feature representation, we introduce the manifold learning module and the hyperbolic module to extract more discriminative information. Eight subjects from the public dataset are utilized to evaluate our pipeline, and the model achieved 89% accuracy, 70% precision, 80% sensitivity, 72% f1-score and kappa coefficient of 78%, respectively. The proposed model demonstrates powerful ability in extracting feature representation and achieves promising results by using semi-supervised training scheme. Therefore, our approach shows strong potential for future clinical development. [ABSTRACT FROM AUTHOR]

Circular RNAs are covalently closed and non-coding in eukaryotes, which have tissue- specific and temporal-specific expression patterns whose biogenesis is regulated by transcription and splicing. Most circular RNAs are localized in the cytoplasm. The sequences and protein-binding elements of circular RNAs facilitate circular RNAs in exerting biological functions through complementary base pairing, regulating protein function or self-translation. Recent studies have revealed that N6-Methyladenosine (m6A), a prevalent post-transcriptional modification, can affect the translation, localization, and degradation of circular RNAs. Cutting-edge research into circular RNAs have benefitted from the development of high-throughput sequencing technology. Furthermore, the expansion of novel research methods has promoted progress into circular RNA research. [ABSTRACT FROM AUTHOR]

The construction of C–C bonds and degradation of chemical warfare agents (CWAs) are two central organic conversions for future sustainable development. Herein, two remarkable silver-containing polyoxomolybdates-based hybrids (Ag-POMos), [Ag(L)(H 2 O) 2 ] 2 [HPMo 12 O 40 ] (1) and [Ag 2 (L) 2 ] 2 [HPMo 10 V 2 O 40 ]·4DMSO (2) (L ​= ​4,6-bis(2-pyridyl)-2-aminopyrimidine) were designed and prepared by using silver cation, polyoxomolybdates and multiple N-donors ligand (L). The structure of compound 1 is constructed from an independent Ag center and a keggin-type POMs; compound 2 is composed of two independent Ag centers and a keggin-type POMs, which displays intriguing POM-incorporated 1-D chain structure with hydrogen bonding. Additionally, Ag-POMos catalysts can be severed as the efficient bifunctional catalysts for the three-component Mannich reaction to achieve the construction of C–C bonds and decontamination of sulfur mustard 2-chloroethyl ethyl sulfide (CEES). As bifunctional heterogeneous catalyst, particularly compound 2 , exhibits satisfying catalytic performances in the Mannich reaction (conv. 99%; sele. 99%) and the oxidation of CEES (conv. 96%; sele. 96%). Notedly, after three runs, compound 2 maintains excellent catalytic properties, demonstrating its remarkable cycling stability. 1. Two novel inorganic-organic silver-polyoxomolybdates hybrids were synthesized for the first time and routinely characterized.2. The compounds can be severed as efficient bifunctional catalysts (functions of Lewis acid catalysis and oxidation catalysis) for the construction of the C–C bond and decontamination of sulfur mustard 2-chloroethyl ethyl sulfide (CEES).3. The molecular compound 2 has good cycling stability, after three continuous runs, the conversion and selectivity have no obvious decrease. [Display omitted] • Designed and synthesis of four- and five-coordinate silver-polyoxomolybdate clusters with bifunctional catalytic properties. • These bifunctional catalysts can efficiently catalyze both Mannich reaction and degradation of sulfur mustard simulant under mild conditions. • The bifunctional catalysts have wide substrate applicability and good catalytic performance. [ABSTRACT FROM AUTHOR]

As a special ecosystem in cities, urban wetland parks have important environmental regulation and social service functions. This paper proposes a new methodology of urban wetland planning and management based on the vulnerability of the ecological environment. The Jixi National Wetland Park (JNWP) was taken as the research area to analyze the ecological, geological and environmental factors that affect urban wetlands. A remote sensing image, digital elevation model, and environmental quality interpolation processing were used to generate the factor layer, and a comprehensive evaluation index system was established. The fuzzy Delphi analytic hierarchy process (FDAHP) method was used to calculate the comprehensive weight of each evaluation factor. A model to evaluate the ecological environment vulnerability of the JNWP was established. Then, an improved k-means clustering algorithm was used to classify the ecological environment of the study area. The ecological environment vulnerability of the wetland was evaluated. The results showed that the vulnerability of the ecological environment in the study area could be divided into five levels, including very low, low, medium, high and very high vulnerability areas. According to the vulnerability level and the results of k-means++ cluster analysis, the JNWP is divided into five areas. The wetland buffer zone is the main factor that determines the distribution of ecological environment vulnerability in urban wetlands. However, cultivated land development and ecological environmental restoration are the main factors that determine the evolution of ecological environment vulnerability in urban wetlands. The FDAHP and geographic information systems (GIS), combined with cluster analysis, are effective methods to evaluate the vulnerability of the ecological environment of urban wetlands, which provides a scientific and accurate methodology for the management and sustainable development of urban wetlands. [Display omitted] • A new methodology was developed to quantify the vulnerability of urban wetlands. • Multi-sources data were used to extract vulnerability factors. • The vulnerability assessment considers ecological, geological and environmental aspects. • Urban planning affects vulnerability more than geological conditions and natural environment. • Different vulnerable areas and ecological environment types have different planning strategies. [ABSTRACT FROM AUTHOR]

The α-Fe 2 O 3 /SnO 2 -RGO-15 possessed a competitive sensing performance with a high response of 7.4 towards 1 ppm NO 2 at room temperature. [Display omitted] As promising sensing materials, reduced graphene oxide (RGO)-based nanomaterials have drawn considerable attention in the fields of gas monitoring owing to their low operating temperature. However, constructing RGO-based room-temperature gas sensors possessing ppb-level limit of detection with high sensitivity remains challenging. In this work, a series of highly sensitive NO 2 sensors were fabricated using α-Fe 2 O 3 and SnO 2 co-decorated RGO hybrids (designated as α-Fe 2 O 3 /SnO 2 -RGO) as sensing materials. They were rationally synthesized by a one-pot hydrothermal method. Compared to SnO 2 modified RGO hybrids (SnO 2 -RGO with bandgap of 3.88 eV), the bandgap energy of α-Fe 2 O 3 /SnO 2 -RGO hybrids (3.53 eV) was reduced by adding α-Fe 2 O 3; the narrower bandgap facilitated the sensing materials to release more electrons and form more oxygen ions at room temperature. Besides, the high carrier migration of RGO, which served as continuous phase, identical structure with ultrasmall particle size of α-Fe 2 O 3 and SnO 2 (about 3–6 nm), and abundant chemisorbed oxygen species on the surface (20.8%) of the sensing materials, as well as their suitable bandgap (3.53 eV) in the sensing materials, significantly improved NO 2 response at room temperature. Among the sensors fabricated, α-Fe 2 O 3 /SnO 2 -RGO-15-based NO 2 sensor had the highest response of 7.4 with a short response time of 59 s towards 1 ppm NO 2 ; it could even reach a response of 2.6 towards 100 ppb NO 2. Notably, α-Fe 2 O 3 /SnO 2 -RGO-15 sample has excellent capability to recognize NO 2 , where the response value (7.4) towards 1 ppm NO 2 is about 7 times higher than that of 100 ppm ammonia and common volatile organic compounds (formaldehyde, toluene, ethanol and acetone). Such NO 2 sensor has superior repeatability with negligible response deviation towards 1 ppm NO 2 for four reversible cycles. This makes it to have a great potential application in the field of NO 2 detection. [ABSTRACT FROM AUTHOR]

[Display omitted] The metal oxides-based chemiresitive gas sensors have attracted enormous interest because of their exellent sensing perforamcnes, which have emerged as very promising candidates for gas monitoring. However, from the view of organophosphorus compounds detection, a unique combination of low detection limit and fast respons/recovery rate remains challenging. Herein, the synersgitic effects of oxygen vacancy engineering and surface gold decoration enabling excellent sensing performances for detection of dimethyl methyl phosphonate (DMMP, a typical organophosphorus) is reported. To demonstrate the proof of concept, Au nanoparticles (NPs) decorated oxygen vacancy-enriched SnO 2 hybrids (designated as Au-O-SnO 2) were designed as sensing materials, where the O-SnO 2 samples were fabricated by introduction of oxygen vacancies onto commercial SnO 2 through organometallic chemistry-assisted approach using (CH 3) 2 SnCl 2 as precursor, followed by deposition of Au NPs by an in-situ reduction routine. After optimizing Au NPs content in hybrids (1 wt%, 3 wt%, 5 wt% and 7 wt%), O-SnO 2 decorated with 5 wt% Au NPs (designated as Au-O-SnO 2 -5) exhibits excellent DMMP sensing performances, such as, an enhanced recoverable response of 1.67 to 680 ppb DMMP, low detection limit of 4.8 ppb, short response time of 26 s and recovery time of 32 s, as well as good selectivity, which are much better than that of commercial SnO 2 (C-SnO 2) and O-SnO 2 , and Au NPs decorated C-SnO 2. Based on the detailed investigation, the enhanced DMMP sensing performances of Au-O-SnO 2 hybrids can be mainly ascribed to the synergistic effect of increasing surface active sites induced by oxygen vacancies, the chemical and electronic sensitization of Au NPs. As a result, Au-O-SnO 2 -5 hybrids display relatively low activation energy of 24.11 kJ/mol for DMMP oxidization, which is lower than that of O-SnO 2 (35.54 kJ/mol). Our results provide a feasible method for boosting sensing performances for DMMP detection, paving new way for fabrication of metal oxides-based gas sensors for rapid detection of trace organic compounds with complexed structures. [ABSTRACT FROM AUTHOR]

[Display omitted] Application of metal organic frameworks (MOFs) on sensors is of great interest for researchers. Film forming ability of the sensing material is very important for both the preparation process and sensing properties of the devices. Humidity sensors based on UIO-66 derived polyelectrolyte films were well prepared by in situ thiol-ene click cross-linking polymerization in this work. The hydrophilicity of the sensing film could be controlled by the feed ratios. The optimized humidity sensor shows a fast response to RHs change (Res/Rec time is 3.1 s/1.5 s, respectively) with ∼1.2% RH of humidity hysteresis. The water molecules adsorption behavior of the film and the sensing mechanism were also be investigated. The humidity sensor with good water and thermal stability and repeatability was applied in breath monitoring, which can well distinguish different breath states. [ABSTRACT FROM AUTHOR]

DNA-binding is an important feature of proteins, and protein-DNA interaction involves in many life processes. Various computational methods have been developed to predict protein-DNA complex structures due to the difficulty of experimentally obtaining protein-DNA complex structures. However, prediction of protein-DNA complex is still a challenging problem compared with prediction of protein-RNA complex, this may be due to the large conformational changes between bound and unbound structure in both protein and DNA. We extend PRIME 2.0 to PRIME 2.0.1 to model protein-DNA complex structures. By comparing sequence and structure alignment methods, we found that structure-based methods can find more templates than sequence-based methods. The results of all-to-all structure alignments showed that DNA structure plays an important role in prediction of protein-DNA complex structure. By exploring the relationship of sequence and structure, we found that in protein-DNA interaction, numerous structures with dissimilar sequences have similar 3D structures and perform the similar function. • PRIME 2.0.1 predicts the full-atom model of protein-DNA complex structure based on the 3D structure template of protein and DNA. • DNA plays a key role in the protein-DNA complexes structure prediction. • Numerous protein-DNA complexes with dissimilar sequences share similar 3D structures and perform the similar function. [ABSTRACT FROM AUTHOR]