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"Zhen, Zhong"
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3D Lamellar-Structured Graphene Aerogels for Thermal Interface Composites with High Through-Plane Thermal Conductivity and Fracture Toughness
HighlightsLamellar-structured graphene aerogels with vertically aligned and closely stacked high-quality graphene lamellae are fabricated.The superior thermally conductive capacity of the aerogel endows epoxy with a high through-plane thermal conductivity of 20.0 W m−1 K−1 at 2.30 vol% of graphene content.The nacre-like structure endows the epoxy composite with enhanced fracture toughness.Although thermally conductive graphene sheets are efficient in enhancing in-plane thermal conductivities of polymers, the resulting nanocomposites usually exhibit low through-plane thermal conductivities, limiting their application as thermal interface materials. Herein, lamellar-structured polyamic acid salt/graphene oxide (PAAS/GO) hybrid aerogels are constructed by bidirectional freezing of PAAS/GO suspension followed by lyophilization. Subsequently, PAAS monomers are polymerized to polyimide (PI), while GO is converted to thermally reduced graphene oxide (RGO) during thermal annealing at 300 °C. Final graphitization at 2800 °C converts PI to graphitized carbon with the inductive effect of RGO, and simultaneously, RGO is thermally reduced and healed to high-quality graphene. Consequently, lamellar-structured graphene aerogels with superior through-plane thermal conduction capacity are fabricated for the first time, and its superior through-plane thermal conduction capacity results from its vertically aligned and closely stacked high-quality graphene lamellae. After vacuum-assisted impregnation with epoxy, the resultant epoxy composite with 2.30 vol% of graphene exhibits an outstanding through-plane thermal conductivity of as high as 20.0 W m−1 K−1, 100 times of that of epoxy, with a record-high specific thermal conductivity enhancement of 4310%. Furthermore, the lamellar-structured graphene aerogel endows epoxy with a high fracture toughness, ~ 1.71 times of that of epoxy.
Journal Article
A novel visible and infrared image fusion method based on convolutional neural network for pig-body feature detection
The visible (VI) and infrared (IR) image fusion has been an active research task because of its higher segmentation accuracy rate during recent years. However, traditional VI and IR image fusion algorithms could not extract more texture and edge features of fused image. In order to more effectively extract pig-body shape and temperature feature, a new multisource fusion algorithm for shape segmentation and temperature extraction is presented based on convolutional neural network (CNN), named as MCNNFuse. Firstly, visible and infrared images are fused by modified CNN fusion model. Then, shape feature is extracted by Otsu threshold and morphological operation in view of fusion results. Finally, pig-body temperature feature is extracted based on shape segmentation. Experimental results show that segmentation model based on presented fusion method is capable of achieving 1.883–7.170% higher average segmentation accuracy rate than prevalent traditional and previously published methods. Furthermore, it establishes the groundwork for accurate measurement of pig-body temperature.
Journal Article
Direct Ink Writing of Highly Conductive MXene Frames for Tunable Electromagnetic Interference Shielding and Electromagnetic Wave-Induced Thermochromism
Highlights3D printing of MXene frames with tunable electromagnetic interference shielding efficiency is demonstrated.Highly conductive MXene frames are reinforced by cross-linking with aluminum ions.Electromagnetic wave is visualized by electromagnetic-thermochromic MXene patterns.The highly integrated and miniaturized next-generation electronic products call for high-performance electromagnetic interference (EMI) shielding materials to assure the normal operation of their closely assembled components. However, the most current techniques are not adequate for the fabrication of shielding materials with programmable structure and controllable shielding efficiency. Herein, we demonstrate the direct ink writing of robust and highly conductive Ti3C2Tx MXene frames with customizable structures by using MXene/AlOOH inks for tunable EMI shielding and electromagnetic wave-induced thermochromism applications. The as-printed frames are reinforced by immersing in AlCl3/HCl solution to remove the electrically insulating AlOOH nanoparticles, as well as cross-link the MXene sheets and fuse the filament interfaces with aluminum ions. After freeze-drying, the resultant robust and porous MXene frames exhibit tunable EMI shielding efficiencies in the range of 25–80 dB with the highest electrical conductivity of 5323 S m−1. Furthermore, an electromagnetic wave-induced thermochromic MXene pattern is assembled by coating and curing with thermochromic polydimethylsiloxane on a printed MXene pattern, and its color can be changed from blue to red under the high-intensity electromagnetic irradiation. This work demonstrates a direct ink printing of customizable EMI frames and patterns for tuning EMI shielding efficiency and visualizing electromagnetic waves.
Journal Article
Polycrystalline SnSe with a thermoelectric figure of merit greater than the single crystal
Thermoelectric materials generate electric energy from waste heat, with conversion efficiency governed by the dimensionless figure of merit, ZT. Single-crystal tin selenide (SnSe) was discovered to exhibit a high ZT of roughly 2.2–2.6 at 913 K, but more practical and deployable polycrystal versions of the same compound suffer from much poorer overall ZT, thereby thwarting prospects for cost-effective lead-free thermoelectrics. The poor polycrystal bulk performance is attributed to traces of tin oxides covering the surface of SnSe powders, which increases thermal conductivity, reduces electrical conductivity and thereby reduces ZT. Here, we report that hole-doped SnSe polycrystalline samples with reagents carefully purified and tin oxides removed exhibit an ZT of roughly 3.1 at 783 K. Its lattice thermal conductivity is ultralow at roughly 0.07 W m
–1
K
–1
at 783 K, lower than the single crystals. The path to ultrahigh thermoelectric performance in polycrystalline samples is the proper removal of the deleterious thermally conductive oxides from the surface of SnSe grains. These results could open an era of high-performance practical thermoelectrics from this high-performance material.
SnSe has a very high thermoelectric figure of merit ZT, but uncommonly polycrystalline samples have higher lattice thermal conductivity than single crystals. Here, by controlling Sn reagent purity and removing SnO
x
impurities, a lower thermal conductivity is achieved, enabling ZT of 3.1 at 783 K.
Journal Article
Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation
AlphaFold model has reshaped biological research. However, vast unstructured data in the entire AlphaFold field requires further analysis to fully understand the current research landscape and guide future exploration. Thus, this scientometric analysis aimed to identify critical research clusters, track emerging trends, and highlight underexplored areas in this field by utilizing machine-learning-driven informatics methods. Quantitative statistical analysis reveals that the AlphaFold field is enjoying an astonishing development trend (Annual Growth Rate = 180.13%) and global collaboration (International Co-authorship = 33.33%). Unsupervised clustering algorithm, time series tracking, and global impact assessment point out that Cluster 3 (Artificial Intelligence-Powered Advancements in AlphaFold for Structural Biology) has the greatest influence (Average Citation = 48.36 ± 184.98). Additionally, regression curve and hotspot burst analysis highlight “structure prediction” (s = 12.40, R
2
= 0.9480,
p
= 0.0051), “artificial intelligence” (s = 5.00, R
2
= 0.8096,
p
= 0.0375), “drug discovery” (s = 1.90, R
2
= 0.7987,
p
= 0.0409), and “molecular dynamics” (s = 2.40, R
2
= 0.8000,
p
= 0.0405) as core hotspots driving the research frontier. More importantly, the Walktrap algorithm further reveals that “structure prediction, artificial intelligence, molecular dynamics” (Relevance Percentage[RP] = 100%, Development Percentage[DP] = 25.0%), “sars-cov-2, covid-19, vaccine design” (RP = 97.8%, DP = 37.5%), and “homology modeling, virtual screening, membrane protein” (RP = 89.9%, DP = 26.1%) are closely intertwined with the AlphaFold model but remain underexplored, which implies a broad exploration space. In conclusion, through the machine-learning-driven informatics methods, this scientometric analysis offers an objective and comprehensive overview of global AlphaFold research, identifying critical research clusters and hotspots while prospectively pointing out underexplored critical areas.
Journal Article
A Novel Multi-Source Image Registration of Porcine Body for Multi-Feature Detection
The safety of animal-related agricultural products has been a hot issue. To obtain a multi-feature representation of porcine bodies for detecting their health, visible and infrared imaging is valuable for exploiting multiple images of a porcine body from different modalities. However, the direct registration of visible and infrared porcine body images can easily cause the dislocation of structural information and spatial position, due to different resolutions and spectrums of multi-source images. To overcome the problem, a novel multi-source image feature representation method based on contour angle orientation is proposed and named Gabor-Ordinal-based Contour Angle Orientation (GOCAO). Moreover, a visible and infrared porcine body image registration method is described and named GOCAO-Rough to Fine (GOCAO-R2F). First, contour and texture features of the porcine body are acquired using a Gabor filter with variable scales and an ordinal operation. Second, feature points in contours are obtained by curvature scale space (CSS), and the main orientation of each feature point is determined by GOCAO. Third, modified scale-invariant feature transform (MSIFT) features are received on the main orientation and registered with bilateral matching. Finally, accurate registrations are extracted by R2F. Experimental results show that the proposed registration algorithm accurately matches multi-source images for porcine body multi-feature detection and is capable of achieving lower average root-mean-square error than current registration algorithms.
Journal Article
Layered Birnessite Cathode with a Displacement/Intercalation Mechanism for High-Performance Aqueous Zinc-Ion Batteries
HighlightsA layered sodium-ion/crystal water co-intercalated Na0.55Mn2O4·0.57H2O (NMOH) cathode is synthesized successfully with a selectively etching method for zinc-ion batteries.A displacement/intercalation mechanism is confirmed in the Mn-based cathode for the first time.The NMOH cathode delivers a competitive reversible capacity of 201.6 mA h g−1 at 500 mA g−1 after 400 cycles.Mn-based rechargeable aqueous zinc-ion batteries (ZIBs) are highly promising because of their high operating voltages, attractive energy densities, and eco-friendliness. However, the electrochemical performances of Mn-based cathodes usually suffer from their serious structure transformation upon charge/discharge cycling. Herein, we report a layered sodium-ion/crystal water co-intercalated Birnessite cathode with the formula of Na0.55Mn2O4·0.57H2O (NMOH) for high-performance aqueous ZIBs. A displacement/intercalation electrochemical mechanism was confirmed in the Mn-based cathode for the first time. Na+ and crystal water enlarge the interlayer distance to enhance the insertion of Zn2+, and some sodium ions are replaced with Zn2+ in the first cycle to further stabilize the layered structure for subsequent reversible Zn2+/H+ insertion/extraction, resulting in exceptional specific capacities and satisfactory structural stabilities. Additionally, a pseudo-capacitance derived from the surface-adsorbed Na+ also contributes to the electrochemical performances. The NMOH cathode not only delivers high reversible capacities of 389.8 and 87.1 mA h g−1 at current densities of 200 and 1500 mA g−1, respectively, but also maintains a good long-cycling performance of 201.6 mA h g−1 at a high current density of 500 mA g−1 after 400 cycles, which makes the NMOH cathode competitive for practical applications.
Journal Article
Vertically aligned reduced graphene oxide/Ti3C2Tx MXene hybrid hydrogel for highly efficient solar steam generation
Effective utilization of abundant solar energy for desalination of seawater and purification of wastewater is one of sustainable techniques for production of clean water, helping relieve global water resource shortage. Herein, we fabricate a vertically aligned reduced graphene oxide/Ti
3
C
2
T
x
MXene (A-RGO/MX) hybrid hydrogel with aligned channels as an independent solar steam generation device for highly efficient solar steam generation. The vertically aligned channels, generated by a liquid nitrogen-assisted directional-freezing process, not only rapidly transport water upward to the evaporation surface for efficient solar steam generation, but also facilitate multiple reflections of solar light inside the channels for efficient solar light absorption. The deliberate slight reduction endows the RGO with plenty of polar groups, decreasing the water vaporization enthalpy effectively and hence accelerating water evaporation efficiently. The MXene sheets, infiltrated inside the A-RGO hydrogel on the basis of Marangoni effect, enhance light absorption capacity and photothermal conversion performance. As a result, the A-RGO/MX hybrid hydrogel achieves a water evaporation rate of 2.09 kg·m
−2
·h
−1
with a high conversion efficiency of 93.5% under 1-sun irradiation. Additionally, this photothermal conversion hydrogel rapidly desalinates seawater and purifies wastewater to generate clean water with outstanding ion rejection rates of above 99% for most ions.
Journal Article