Polydimethylsiloxanes (PDMS) foam as one of next‐generation polymer foam materials shows poor surface adhesion and limited functionality, which greatly restricts its potential applications. ...Fabrication of advanced PDMS foam materials with multiple functionalities remains a critical challenge. In this study, unprecedented self‐adhesive PDMS foam materials are reported with worm‐like rough structure and reactive groups for fabricating multifunctional PDMS foam nanocomposites decorated with MXene/cellulose nanofiber (MXene/CNF) interconnected network by a facile silicone foaming and dip‐coating strategy followed by silane surface modification. Interestingly, such self‐adhesive PDMS foam produces strong interfacial adhesion with the hybrid MXene/CNF nano‐coatings. Consequently, the optimized PDMS foam nanocomposites have excellent surface super‐hydrophobicity (water contact angle of ≈159o), tunable electrical conductivity (from 10−8 to 10 S m−1), stable compressive cyclic reliability in both wide‐temperature range (from −20 to 200 oC) and complex environments (acid, sodium, and alkali conditions), outstanding flame resistance (LOI value of >27% and low smoke production rate), good thermal insulating performance and reliable strain sensing in various stress modes and complex environmental conditions. It provides a new route for the rational design and development of advanced PDMS foam nanocomposites with versatile multifunctionalities for various promising applications such as intelligent healthcare monitoring and fire‐safe thermal insulation.
Polydimethylsiloxanes (PDMS) foam usually exhibits poor surface adhesion and limited functionality, restricting the potential applications. Here, self‐adhesive PDMS foams with worm‐like rough structure and reactive groups are fabricated by a facile silicone foaming approach. Decorating with MXene/cellulose nanofiber interconnected network and using silane modification, exceptional multifunctionalities PDMS nanocomposites are prepared, showing versatile applications in thermal insulating and smart sensing fields.
Machine learning based predictions of protein⁻protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The ...intensive feature engineering in most of these methods makes the prediction task more tedious and trivial. The emerging deep learning technology enabling automatic feature engineering is gaining great success in various fields. However, the over-fitting and generalization of its models are not yet well investigated in most scenarios. Here, we present a deep neural network framework (DNN-PPI) for predicting PPIs using features learned automatically only from protein primary sequences. Within the framework, the sequences of two interacting proteins are sequentially fed into the encoding, embedding, convolution neural network (CNN), and long short-term memory (LSTM) neural network layers. Then, a concatenated vector of the two outputs from the previous layer is wired as the input of the fully connected neural network. Finally, the Adam optimizer is applied to learn the network weights in a back-propagation fashion. The different types of features, including semantic associations between amino acids, position-related sequence segments (motif), and their long- and short-term dependencies, are captured in the embedding, CNN and LSTM layers, respectively. When the model was trained on Pan's human PPI dataset, it achieved a prediction accuracy of 98.78% at the Matthew's correlation coefficient (MCC) of 97.57%. The prediction accuracies for six external datasets ranged from 92.80% to 97.89%, making them superior to those achieved with previous methods. When performed on
,
, and
datasets, DNN-PPI obtained prediction accuracies of 95.949%, 98.389%, and 98.669%, respectively. The performances in cross-species testing among the four species above coincided in their evolutionary distances. However, when testing
using the models from those species, they all obtained prediction accuracies of over 92.43%, which is difficult to achieve and worthy of note for further study. These results suggest that DNN-PPI has remarkable generalization and is a promising tool for identifying protein interactions.
The independent effects of short- and long-term experiences on visual perception have been discussed for decades. However, no study has investigated whether and how these experiences simultaneously ...affect our visual perception. To address this question, we asked participants to estimate their self-motion directions (i.e., headings) simulated from optic flow, in which a long-term experience learned in everyday life (i.e., straight-forward motion being more common than lateral motion) plays an important role. The headings were selected from three distributions that resembled a peak, a hill, and a flat line, creating different short-term experiences. Importantly, the proportions of headings deviating from the straight-forward motion gradually increased in the peak, hill, and flat distributions, leading to a greater conflict between long- and short-term experiences. The results showed that participants biased their heading estimates towards the straight-ahead direction and previously seen headings, which increased with the growing experience conflict. This suggests that both long- and short-term experiences simultaneously affect visual perception. Finally, we developed two Bayesian models (Model 1 vs. Model 2) based on two assumptions that the experience conflict altered the likelihood distribution of sensory representation or the motor response system. The results showed that both models accurately predicted participants' estimation biases. However, Model 1 predicted a higher variance of serial dependence compared to Model 2, while Model 2 predicted a higher variance of the bias towards the straight-ahead direction compared to Model 1. This suggests that the experience conflict can influence visual perception by affecting both sensory and motor response systems. Taken together, the current study systematically revealed the effects of long- and short-term experiences on visual perception and the underlying Bayesian processing mechanisms.
Nowadays a number of computational approaches have been developed to effectively and accurately predict protein interactions. However, most of these methods typically perform worse when other ...biological data sources (e.g., protein structure information, protein domains, or gene neighborhoods information) are not available. In the present work, we propose a method for predicting protein interactions making full use of physicochemical characteristics of amino acids. A protein sequence is encoded at multi-scale by seven properties, including their qualitative and quantitative descriptions, of amino acids. Five kinds of protein descriptors, frequency, composition, transformation, distribution and auto covariance, are extracted from these encodings for representing each protein sequence. The new formed feature representation consisted of 347 dimensions is able to capture not only the compositional and positional information but also their statistical significance of amino acids in the sequence. Based on such a feature representation, the gradient boosting decision tree algorithm is introduced to predict protein interaction class. When the proposed method is tested with the PPI data of S.cerevisiae, it achieves a prediction accuracy of 95.28% at the Matthew's correlation coefficient of 90.68%. Compared with the state-of-the-art works on H.pylori and Human, the accuracies can be raised to 89.27% and 98.00% respectively. Extensive experiments are performed for a crossover protein-protein interactions network and the prediction accuracies are also very promising. Because of learning capabilities of the gradient boosting decision tree and the mutil-scale feature representation scheme, the proposed method might be a useful tool for future proteomics studies.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
A key issue for perovskite solar cells is the stability of perovskite materials due to moisture effects under ambient conditions, although their efficiency is improved constantly. Herein, an improved ...CH3NH3PbI3−xClx perovskite quality is demonstrated with good crystallization and stability by using water as an additive during crystal perovskite growth. Incorporating suitable water additives in N,N‐dimethylformamide (DMF) leads to controllable growth of perovskites due to the lower boiling point and the higher vapor pressure of water compared with DMF. In addition, CH3NH3PbI3−xClx · nH2O hydrated perovskites, which can be resistant to the corrosion by water molecules to some extent, are assumed to be generated during the annealing process. Accordingly, water additive based perovskite solar cells present a high power conversion efficiency of 16.06% and improved cell stability under ambient conditions compared with the references. The findings in this work provide a route to control the growth of crystal perovskites and a clue to improve the stability of organic–inorganic halide perovskites.
Water additive is incorporated into the perovskite precursor solution to control the oriented growth of crystal perovskites and improve the stability of perovskite solar cells. As a result, a power conversion efficiency of 16.06% and an improved cell stability under ambient conditions are achieved.
Interface design plays a crucial role in developing superior mechanical performance of graphene/polymer nanocomposites. Herein, we report a facile approach to the fabrication of advanced polymeric ...nanocomposites of epoxy by the incorporation of polyetheramine-functionalized graphene oxide (PEA-f-GO). Two types of PEA molecules with different molecular lengths were used to synthesize the PEA-f-GO sheets. The chemical bonds formed between the amine functional groups on the GO surface and the epoxy resin during curing provided strong sheet/matrix interfacial adhesion. The addition of PEA-f-GO was found to produce significant enhancements in the mechanical properties of epoxy, including elastic modulus, tensile strength, elongation at break and toughness. In particular, the PEA-f-GO sheets containing shorter PEA molecules produced higher improvement in strength but smaller increases in both ductility and toughness than those containing longer PEA molecules. For example, at 0.50 wt% filler loading, two nanocomposites showed increases of 63% and 51% in tensile strength and 90% and 119% in toughness as compared to the unfilled epoxy. Our results suggest that the interphases between the GO and the polymer matrix can be tuned by varying the molecular lengths of grafted modifiers, thereby providing a new route for the rational designing and development of the GO-based composite materials.
When moving in the environment, optic flow and form (e.g., motion streaks) information generally appear simultaneously. Previous studies have shown that observers can estimate their heading by ...integrating the simultaneously presented form and optic flow information. Recent work also found that the previously seen optic flow affected the current heading estimation. The current study conducted two experiments to explore whether and how the heading estimation from optic flow was affected by the previously seen form information. We found that the current heading estimates from optic flow were biased toward the location of the focus of expansion of the previously seen form stimulus, showing an attractive effect of the previous form. Additionally, the results revealed that the attractive effect of the previous form occurred at the perceptual stage rather than postperceptual stages (e.g., working memory). Our findings suggest that our visual system can integrate dynamic optic flow and static form information across the temporal domain to estimate our heading direction.
The surface intertidal sediments in the Pearl River Estuary of China were analyzed from multiple perspectives, including the distribution characteristics, potential sources, and biological risks of ...polycyclic aromatic hydrocarbons (PAHs). The average concentration of PAHs, ranging from 73.68 ng/g to 933.25 ng/g, was 346.78 ng/g. PAHs are mainly composed of the 2- and 3-ring PAHs, with naphthalene (Nap), phenanthrene (Phe), pyrene (Pyr), benzo(g,h, i) perylene (Dib), fluoranthene (Flua), and indeno (1,2,3-c,d) pyrene (Ind) as the dominant constituents. The principal component analysis combined with multiple linear regression showed that petroleum combustion and biomass/coal combustion have contributed 52.78% and 40.53%, respectively, to the PAHs in intertidal sediments of Pearl River Estuary. The occurrence of adverse biological effects as a result of PAH contamination in the intertidal sediments of Pearl River Estuary has increased by 8% based on the mean value of the probable effect quotient.
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•PAHs in intertidal sediments from Pearl River Estuary were investigated.•The composition of PAHs was characterized by the 2- and 3-ring PAHs.•Two emission sources were identified by PCA coupled with MLR.•Sediments had 8% toxic effect to aquatic biota.
Mercury (Hg) is a global, persistent and inevitable pollutant, the toxicity of which is mostly reflected in its species including inorganic Hg (InHg) and methyl mercury (MeHg). Using diffusive ...gradients in thin films (DGT) is deemed as a reliable technique to determine the bioavailability of pollutants. This study is the first attempt to assess the integrated toxicity of mercury species mixtures in sediments to the aquatic biota based on the DGT technique. In the course, the Daya Bay under serious anthropogenic influences was selected as the study case. The results showed that the DGT concentrations of InHg and MeHg were detected as 0.30–1.93 μg/L and 0.28–1.94 μg/L respectively in the surface sediments collected from the Daya Bay. In terms of the toxicity of single mercury species, the risk quotient (RQ) values of InHg and MeHg significantly exceeded 1, indicating that the adverse effects of InHg and MeHg should not be ignored. In terms of the integrated toxicity of mercury species mixtures, the probabilistic biological risk assessment results demonstrate that Daya Bay features low (3.32%) probability of toxic effects in its surface sediments to the aquatic biota.
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•Diffusive gradients in thin films to assess bioavailabilities of Hg species in sediments•Fist time to evaluate ecotoxicological of Hg species and their mixtures to aquatic biota based on bioavailability•Potential toxicities for individual Hg species and mixtures have occurred.
Today's state‐of‐the‐art perovskite solar cells (PSCs) are utilizing polycrystalline perovskite thin films via solution‐processing at low temperature (<150 °C). It is extremely significant to enlarge ...grain size and passivate trap states for perovskite thin films to achieve high power conversion efficiency. Herein, a strategy for defect passivation of perovskite films via metal ion Ni2+ is for the first time reported. It is found that addition of Ni2+ can significantly generate polyporous PbI2 films due to a different solubility between NiCl2 and PbI2 which benefits penetration of MAI and thus formation of large grain perovskite films eventually. It further demonstrated that Ni2+ ions can effectively passivate PbI3− antisite defects and restrain the generation of Pb0 by interacting with the under‐coordinated halide anions and halide‐rich antisites. Therefore, introducing moderate Ni2+ ions result in a significant increase in photoluminescence lifetime from 285 to 732 ns. Accordingly, a power conversion efficiency of 20.61% can be achieved for the 3% Ni2+ addition‐based PSCs with an enhanced cell stability under ambient conditions. This work provides a promising route toward perovskite films featuring with high crystallinity and low trap‐density.
An effective strategy of promoting grain growth and defects passivation simultaneously for perovskite film by using Ni2+ addition is demonstrated. An appreciated efficiency of 20.6% can be achieved for an inverted planar perovskite solar cells device based on a CH3NH3PbI3 (Ni2+) film.
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