Herein, a facile and general way for the synthesis of TiO 2 coated cobalt manganese oxide CoMn 2 O 4 (CMO) has been developed. In this contribution, uniform Co 0.33 Mn 0.67 CO 3 spheres are firstly ...fabricated via a solvothermal method. Porous CMO with a diameter of about 800 nm is obtained by a subsequent annealing procedure. Finally, a thin layer of TiO 2 is coated on the surface of the CMO through a hydrolysis and subsequent condensation process. When evaluated as anode materials for lithium ion batteries (LIBs), after 500 cycles at a current density of 1000 mA g −1 , the CMO@TiO 2 spheres possessed high reversible capacities of 940 mA h g −1 with a suitable discharge plateau of ∼0.6 V, much higher than the pristine CMO. In addition, the CMO@TiO 2 shows better rate performance than CMO, as high as 196 mA h g −1 at a very fast discharge current of 5 A g −1 . The high electrochemical performance of CMO@TiO 2 should be attributed to its special structure of nanometer scale spheres with high porosity and the thin layer coating of TiO 2 as a zero-strain shell, which can effectively reduce the diffusion length of electrolyte, Li + and electrons, buffer volume expansion during the Li + insertion/extraction processes and thus reduce the materials' pulverization.
Tea waste (TW) was modified by depositing hydrated manganese oxide (HMO) onto it through
in situ
precipitation and a novel hybrid bio-adsorbent, namely HMO-TW, was obtained. The successful deposition ...of HMO in/on tea waste was confirmed by transmission electron microscopy (TEM) and Fourier transform infrared spectroscopy (FT-IR) analysis. The removal of four typical heavy metals (
i.e.
, Pb(
ii
), Cd(
ii
), Cu(
ii
), Zn(
ii
)) by HMO-TW was pH-dependent, and higher pH favored the sorption at the tested pHs of 2-7. HMO-TW showed excellent sorption selectivity toward all four metal ions, and the removal efficiency of target metal ions was sustained at 30%-90% even in the presence of 50 times higher competing Ca(
ii
) and Mg(
ii
) ions. Sorption isotherms of four metal ions by HMO-TW are all well represented by the Freundlich model, and the maximum experimental sorption capacities of Pb(
ii
), Cd(
ii
), Cu(
ii
), Zn(
ii
) were 174.3, 78.38, 54.38 and 37.5 mg g
−1
, respectively. Compared to the unmodified tea waste, the sorption capacities and selectivity of Pb(
ii
), Cd(
ii
) and Cu(
ii
) onto HMO-TW improved significantly. The sorption process reached equilibrium within 200 min, and the kinetics could be well fitted by a pseudo-second order model. Fixed-bed column sorption results further showed that the bed volume (BV) of
C
e
/
C
0
reaching 0.5 for Pb(
ii
), Cd(
ii
), Cu(
ii
), Zn(
ii
) were 1170, 1130, 820 and 1450 BV, respectively. In addition, the exhausted HMO-TW can be effectively regenerated using a 0.5 M HCl solution. All results reported herein validate that HMO-TW is a promising sorbent for practical treatment of heavy metal contaminated water.
A novel composite sorbent with hydrated manganese oxide (HMO) (<5 nm) highly dispersed on tea waste (TW), enjoying synergistic benefits from both materials, efficiently and selectively sorbed Pb(
ii
), Cd(
ii
), Cu(
ii
) and Zn(
ii
) from water.
Virtual staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve ...time-consuming chemical processes, virtual staining offers an efficient and low infrastructure alternative. Leveraging microscopy-based techniques, such as confocal microscopy, researchers can expedite tissue analysis without the need for physical sectioning. However, interpreting grayscale or pseudo-color microscopic images remains a challenge for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, specifically designed for virtual staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. We enhance nuclei contrast in RCM images using aluminum chloride preprocessing for skin tissues. Training the model with virtual H\&E labels featuring two fluorescence channels eliminates the need for image registration and provides pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for virtual staining tasks and advancing the field of histological image analysis.
Virtual staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve ...time-consuming chemical processes, virtual staining offers an efficient and low infrastructure alternative. Leveraging microscopy-based techniques, such as confocal microscopy, researchers can expedite tissue analysis without the need for physical sectioning. However, interpreting grayscale or pseudo-color microscopic images remains a challenge for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, specifically designed for virtual staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. We enhance nuclei contrast in RCM images using aluminum chloride preprocessing for skin tissues. Training the model with virtual H\&E labels featuring two fluorescence channels eliminates the need for image registration and provides pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for virtual staining tasks and advancing the field of histological image analysis.
An improved GPSR protocol based on stratification of traffic density is designed to get lower possibility to encounter local optimum in VANET. In the sparse region, every node maintains a list that ...recorded its multi-hop neighbor nodes, but in the dense region, every node just maintains its one-hop-neighbor nodes. Every node will change their strategy according to the change of the periphery traffic density. Simulation experiment compares with traditional GPSR protocol, 2-hop GPSR and the improved protocol. The simulation result shows that the improved protocol has a higher transmission rate compared to the GPSR protocol, and the transmission overhead was smaller than the 2-hop GPSR protocol.
Virtual staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve ...time-consuming chemical processes, virtual staining offers an efficient and low infrastructure alternative. Leveraging microscopy-based techniques, such as confocal microscopy, researchers can expedite tissue analysis without the need for physical sectioning. However, interpreting grayscale or pseudo-color microscopic images remains a challenge for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, specifically designed for virtual staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. We enhance nuclei contrast in RCM images using aluminum chloride preprocessing for skin tissues. Training the model with virtual H\&E labels featuring two fluorescence channels eliminates the need for image registration and provides pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for virtual staining tasks and advancing the field of histological image analysis.
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