Improved Charge Injection Balance in Quantum Dot Light-Emitting Diodes through Interface Texture.

The existing methods to improve the charge balance of quantum dot light-emitting diodes (QLEDs) rely on energy-level matching, but these approaches have been limited by material availability and Fermi-level pinning. Here, we propose a solution that does not require changes to the materials' electronic properties. By using nanoimprinting technology to texture the interface between the hole-transporting layer (HTL) and colloidal quantum dot (CQD) layer, we can increase the HTL-CQD contact area. This significantly enhances the hole injection rate while keeping the electron injection rate essentially unchanged. Compared with the conventional planar structure, QLEDs with textured HTL exhibit lower luminance threshold voltage, significantly higher external quantum efficiency at low bias voltages, improved operational stability, and a similar Lambertian factor. Comprehensive measurements confirm that the HTL-CQD interface texture allows more efficient hole injection into CQDs to occur under lower bias, resulting in less CQD charging and more efficient exciton recombination.

Attribute-Aware Deep Hashing With Self-Consistency for Large-Scale Fine-Grained Image Retrieval.

Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i.e., the same sub-category labels) highest based on the fine-grained details in the query. It is desirable to alleviate the challenges of both fine-grained nature of small inter-class variations with large intra-class variations and explosive growth of fine-grained data for such a practical task. In this paper, we propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes to not only make the retrieval process efficient, but also establish explicit correspondences between hash codes and visual attributes. Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations. Our models are also equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities. Then, driven by preserving original entities' similarity, the required hash codes can be generated from these attribute-specific vectors and thus become attribute-aware. Furthermore, to combat simplicity bias in deep hashing, we consider the model design from the perspective of the self-consistency principle and propose to further enhance models' self-consistency by equipping an additional image reconstruction path. Comprehensive quantitative experiments under diverse empirical settings on six fine-grained retrieval datasets and two generic retrieval datasets show the superiority of our models over competing methods. Moreover, qualitative results demonstrate that not only the obtained hash codes can strongly correspond to certain kinds of crucial properties of fine-grained objects, but also our self-consistency designs can effectively overcome simplicity bias in fine-grained hashing.

Dual-Active Au@PNIPAm Nanozymes for Glucose Detection and Intracellular H2O2 Modulation.

As a nanozyme, gold nanoparticles have some advantages compared with natural enzymes, such as stable structure, adjustable catalytic activity, multifunctionality, and recyclability. Due to their special dimension, they are easy to aggregate rapidly and lose their catalytic performance when exposed to normal saline or special pH environment. To avoid such a situation, Au@PNIPAm nanozymes with core-shell structure are constructed and their mimic peroxidase and glucose oxidase enzymatic activities are investigated. Kinetic examinations manifest that Au@PNIPAm nanozymes exhibited a high affinity for 3,3,5,5-tetramethylbenzidine (TMB), hydrogen peroxide (H2O2), and glucose. These predominant peroxidase-like and glucose-like oxidase Au@PNIPAm catalytic activities are successfully used in the detection of H2O2 or glucose (LOD is 2.43 mM or 5.07 mM). Otherwise, the potential Au@PNIPAm nanozymes are provided with a clear ability for decomposing the intracellular H2O2 in living cells. And it could protect cells from oxidative stress damage with inducing by H2O2. Therefore, it is easy to consider that Au@PNIPAm nanozymes show a certain possibility to retard cell senescence and increase the production of the hydroxyl radical which could prevent carcinogenesis of the cell.

Thermo-sensitivity of hybrid nanogels for specific endogenous hydrogen sulfide detection and efficient flash chill treatment.

Thermo-responsive nanogels, poly(NIPAM-AAM) were prepared by a facile method of free radical one-pot precipitation based on monomeric N-isopropylacrylamide (NIPAM). At the same time, surface carboxyl group-modified fluorescent conjugated polymer nanoparticles (PNPs-COOH) were immobilized in the nanogel networks by hydrogen bonding to show bright green photoluminescence and outstanding thermo-responsive properties with a typical two-phase Tai Chi structure. Poly(NIPAM-AAm)-PNPs-COOH displays a larger change of hydrodynamic diameter (Dh) and a reversible volume transition from ∼150 nm at 40 °C to ∼1.0 µm at 4 °C. The change will cause apoptosis of cells to finish flash chill treatment. Meanwhile, the fluorescence of poly(NIPAM-AAm)-PNPs-COOH demonstrates reversible quenching and recovery with the expansion and collapse of the nanogel network induced by temperature changes. In addition, when adding Fe3+, the fluorescence of poly(NIPAM-AAm)-PNPs-COOH can be quenched due to the chelation between the PNPs-COOH and Fe3+ and then recovered in the presence of hydrogen sulfide because the chelate is broken; this was attributed to the stronger affinity between Fe3+ and S2-. Therefore, the fluorescence nanoplatform, Fe3+/poly(NIPAM-AAm)-PNPs-COOH, was successfully used to detect endogenous hydrogen sulfide in living A549 cells.