In this study, we report a metallogel developed based on metal-phenolic coordination of natural low-cost polyphenolic molecule and metal ions. Gelation occurs by mixing tannic acid (TA) and group ...(IV) titanium ions (Ti
) to form TA-Ti
gel. The TA-Ti
gel exhibits good capability to incorporate diverse metal ions by in situ co-gelation. Herein, five antimicrobial metal ions, i.e. ferric (Fe
), copper (Cu
), zinc (Zn
), cobalt (Co
) and nickel (Ni
) ions, were employed to include in TA-Ti
gels for developing intelligent dressings for infected wounds. The chemical and coordinative structures of TA-Ti
metallogels were characterized by UV-Vis and Fourier-transform infrared (FT-IR) spectroscopies. Cytotoxicity of antimicrobial metallogels was explored by MTT assay with NIH 3T3 fibroblasts. The release of metal ions was evaluated by inductively coupled plasma mass spectrometry (ICP-MS), indicating the different releasing profiles upon the coordinative interactions of metal ions with TA. The formation and disassembly of metallogels are sensitive to the presence of acid and an oxidizer, H
O
, which are substances spontaneously generated in infected wounds due to the metabolic activity of bacteria and the intrinsic immune response. The Cu
releasing rates of TA-Ti
-Cu
metallogels at different pH values of 5.5, 7.4 and 8.5 have been studied. In addition, addition of H
O
trigger fast release of Cu
as a result of oxidation of galloyl groups in TA. Consequently, the antimicrobial potency of TA-Ti
-Cu
metallogels can be simultaneously activated while the wounds are infected and healing. The antimicrobial property of metallogels against Gram-negative Escherichia coli, and Gram-positive Methicillin-Resistant Staphylococcus aureus (USA300) and Staphylococcus epidermidis has been investigated by agar diffusion test. In an animal model, the TA-Ti
-Cu
metallogels were applied as dressings for infected wounds, indicating faster recovery in the wound area and extremely lower amount of bacteria around the wounds, compared to TA-Ti
gels and gauze. Accordingly, the intelligent nature derived metallogels is a promising and potential materials for medical applications.
The thermal properties of epoxy‐based binary composites comprised of graphene and copper nanoparticles are reported. It is found that the “synergistic” filler effect, revealed as a strong enhancement ...of the thermal conductivity of composites with the size‐dissimilar fillers, has a well‐defined filler loading threshold. The thermal conductivity of composites with a moderate graphene concentration of fg = 15 wt% exhibits an abrupt increase as the loading of copper nanoparticles approaches fCu ≈ 40 wt%, followed by saturation. The effect is attributed to intercalation of spherical copper nanoparticles between the large graphene flakes, resulting in formation of the highly thermally conductive percolation network. In contrast, in composites with a high graphene concentration, fg = 40 wt%, the thermal conductivity increases linearly with addition of copper nanoparticles. A thermal conductivity of 13.5 ± 1.6 Wm−1K−1 is achieved in composites with binary fillers of fg = 40 wt% and fCu = 35 wt%. It has also been demonstrated that the thermal percolation can occur prior to electrical percolation even in composites with electrically conductive fillers. The obtained results shed light on the interaction between graphene fillers and copper nanoparticles in the composites and demonstrate potential of such hybrid epoxy composites for practical applications in thermal interface materials and adhesives.
A strong enhancement in thermal conductivity of composites with graphene and copper nanoparticle fillers is demonstrated. The dissimilar size and aspect ratios of the fillers result in a “synergistic effect,” which reveals itself as an abrupt change in thermal properties at the thermal percolation threshold. The study shows a possibility of achieving thermal percolation without inducing electrical conductivity.
Designing and modulating the local structure of metal sites is the key to gain the unique selectivity and high activity of single metal site catalysts. Herein, we report strain engineering of curved ...single atomic iron‐nitrogen sites to boost electrocatalytic activity. First, a helical carbon structure with abundant high‐curvature surface is realized by carbonization of helical polypyrrole that is templated from self‐assembled chiral surfactants. The high‐curvature surface introduces compressive strain on the supported Fe−N4 sites. Consequently, the curved Fe−N4 sites with 1.5 % compressed Fe−N bonds exhibit downshifted d‐band center than the planar sites. Such a change can weaken the bonding strength between the oxygenated intermediates and metal sites, resulting a much smaller energy barrier for oxygen reduction. Catalytic tests further demonstrate that a kinetic current density of 7.922 mA cm−2 at 0.9 V vs. RHE is obtained in alkaline media for curved Fe−N4 sites, which is 31 times higher than that for planar ones. Our findings shed light on modulating the local three‐dimensional structure of single metal sites and boosting the catalytic activity via strain engineering.
Compressive strain engineering of curved single atomic iron‐nitrogen sites could boost the catalytic activity for electrocatalytic oxygen reduction reaction.
Indium-gallium-zinc oxide (IGZO) photodetectors have been mostly studied in the ultraviolet region and rarely in the X-ray region. This study fabricates IGZO X-ray detectors on glass substrates using ...different post-deposition annealing (PDA) times. The photo-to-dark current ratio increases significantly from 2.6 to 392.3 after PDA because of a considerable reduction of the deep-level states. There are fewer residual electrons in the conduction band and recombination centers in the middle of the bandgap are eliminated. An IGZO X-ray detector with optimal PDA time has a sensitivity of <inline-formula> <tex-math notation="LaTeX">8.5\times 10^{-{3}}\,\,\mu \text{C} </tex-math></inline-formula>/(mGy<inline-formula> <tex-math notation="LaTeX">\cdot </tex-math></inline-formula>cm<inline-formula> <tex-math notation="LaTeX">^{{2}}{)} </tex-math></inline-formula> and a rise/decay time of 5.1/12.2 s with a bias of 10 V at a dose rate of 100 mGy/s. This result shows that IGZO is eminently suited to applications for X-ray detection.
The filament in aAu/Ta2O5/Au system is analyzed and determined to be a nanoscaled TaO2−x filament. A shrunken anode localizes the filament formation and the defect boundary leads to faster ...accumulation of oxygen vacancies. The defect changes the switching domination between electron transport and oxygen‐vacancy migration. The migration of oxygen vacancies limits the filament dynamics, indicating the crucial role played by oxygen defects.
High‐performance MoS2 transistors are developed using atomic hexagonal boron nitride as a tunneling layer to reduce the Schottky barrier and achieve low contact resistance between metal and MoS2. ...Benefiting from the ultrathin tunneling layer within 0.6 nm, the Schottky barrier is significantly reduced from 158 to 31 meV with small tunneling resistance.
Increasing evidence demonstrates that commensal microorganisms in the human skin microbiome help fight pathogens and maintain homeostasis of the microbiome. However, it is unclear how these ...microorganisms maintain biological balance when one of them overgrows. The overgrowth of Propionibacterium acnes (P. acnes), a commensal skin bacterium, has been associated with the progression of acne vulgaris. Our results demonstrate that skin microorganisms can mediate fermentation of glycerol, which is naturally produced in skin, to enhance their inhibitory effects on P. acnes growth. The skin microorganisms, most of which have been identified as Staphylococcus epidermidis (S. epidermidis), in the microbiome of human fingerprints can ferment glycerol and create inhibition zones to repel a colony of overgrown P. acnes. Succinic acid, one of four short-chain fatty acids (SCFAs) detected in fermented media by nuclear magnetic resonance (NMR) analysis, effectively inhibits the growth of P. acnes in vitro and in vivo. Both intralesional injection and topical application of succinic acid to P. acnes-induced lesions markedly suppress the P. acnes-induced inflammation in mice. We demonstrate for the first time that bacterial members in the skin microbiome can undergo fermentation to rein in the overgrowth of P. acnes. The concept of bacterial interference between P. acnes and S. epidermidis via fermentation can be applied to develop probiotics against acne vulgaris and other skin diseases. In addition, it will open up an entirely new area of study for the biological function of the skin microbiome in promoting human health.
Hepatitis B virus X protein (HBx) and hepatic stellate cells (HSCs) are critical for liver fibrosis development. Anti-fibrosis occurs via reversion to quiescent-type HSCs or clearance of HSCs via ...apoptosis or ferroptosis. We aimed to elucidate the role of chrysophanol in rat HSC-T6 cells expressing HBx and investigate whether chrysophanol (isolated from Rheum palmatum rhizomes) influences cell death via ferroptosis in vitro. Analysis of lipid reactive oxygen species (ROS), Bip, CHOP, p-IRE1α, GPX4, SLC7A11, α-SMA, and CTGF showed that chrysophanol attenuated HBx-repressed cell death. Chrysophanol can impair HBx-induced activation of HSCs via endoplasmic reticulum stress (ER stress) and ferroptosis-dependent and GPX4-independent pathways.
Observations with a global coverage are very important for space physics research and space weather monitoring. However, due to the technical limitations, it would be very expensive or even ...impossible to achieve a seamless global coverage even with advanced observational devices. It would be useful to fill missing data gaps to create a global map from the available data, but up until now this has been very challenging. Fortunately, the deep learning method, a recent breakthrough in artificial intelligence, may provide an effective way to solve this problem by making full use of data from reliable observations. In this paper, a promising deep learning algorithm, deep convolutional generative adversarial network (DCGAN), is investigated to fill the missing data of total electron content (TEC) map images. The direct use of DCGAN fails to fill missing data for the completion of TEC maps because there are always missing TEC data in some regions, such as oceans, where the features vary with time and geophysical conditions. Thus, no useful information can be utilized by DCGAN to achieve a meaningful image completion. In order to overcome this shortcoming of the original DCGAN method, a novel regularized DCGAN (R‐DCGAN) is proposed by adding an extra discriminator and some widely used reference TEC maps from the International Global Navigation Satellite Systems Service Ionosphere Working Group. The proposed R‐DCGAN method generates satisfactory ionospheric peak structures at different times and geomagnetic conditions, which demonstrate its effectiveness on filling the missing data in TEC maps. The proposed R‐DCGAN framework can be readily extended to a broad application in other fields of space sciences, particularly for addressing the missing observation data issues.
Plain Language Summary
This paper proposes an improved deep learning algorithm, regularized deep convolutional generative adversarial network (R‐DCGAN), for the image completion of total electron content (TEC) maps. The traditional DCGAN (which is a very popular and powerful deep learning algorithm for image completion, such as human face images) needs the training data to be completed observations. Since there is lack of distinct features in the missing data part of the training data that can be utilized by DCGAN to fill these missing values, DCGAN fails to directly learn the observation with data missing. In order to overcome the shortcoming of the original DCGAN method, an improved algorithm, R‐DCGAN, is proposed to fulfill missing data completion for the Massachusetts Institute of Technology‐TEC maps. The R‐DCGAN is designed from DCGAN, with an extra discriminator and the reference TECs. The R‐DCGAN produces satisfactory ionospheric peak structures at different times and geomagnetic conditions, and the results demonstrate that the deep learning algorithm is promising to fill the missing data.
Key Points
This study proposes an improved deep learning algorithm to deal with common missing observation data issues
The result generated by the algorithm can show satisfactory ionospheric peak structures at different times and geomagnetic conditions
The traditional DCGAN fails to directly learn the observation with data missing; in order to overcome this, DCGAN extended to a broader application
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