Mineral acid-triggered multicolor room-temperature phosphorescence nanoprobes for time-resolved bioimaging.

We present a facile strategy to achieve color-tunability room-temperature phosphorescence (RTP) nanoprobes by doping mineral acids (i.e., boric acid and phosphoric acid) in an organic silicon scaffold through a cross-linking process. Such RTP nanoprobes exhibit inherent tunable phosphorescence (from 420-650 nm) with long lifetime (emission lasting for ∼5-15 s, RTP lifetime: ∼0.53-2.11 s) and high quantum yields (∼13.1-43.0%). Therefore, the as-prepared nanoprobes enable multiple imaging in live cells with a high signal-to-background ratio value of ∼52.

Supermolecular Confined Silicon Phosphorescence Nanoprobes for Time-Resolved Hypoxic Imaging Analysis.

Room temperature phosphorescence (RTP) nanoprobes play crucial roles in hypoxia imaging due to their high signal-to-background ratio (SBR) in the time domain. However, synthesizing RTP probes in aqueous media with a small size and high quantum yield remains challenging for intracellular hypoxic imaging up to present. Herein, aqueous RTP nanoprobes consisting of naphthalene anhydride derivatives, cucurbit[7]uril (CB[7]), and organosilicon are reported via supermolecular confined methods. Benefiting from the noncovalent confinement of CB[7] and hydrolysis reactions of organosilicon, such small-sized RTP nanoprobes (5-10 nm) exhibit inherent tunable phosphorescence (from 400 to 680 nm) with microsecond second lifetimes (up to ∼158.7 µs) and high quantum yield (up to ∼30%). The as-prepared RTP nanoprobes illustrate excellent intracellular hypoxia responsibility in a broad range from ∼0.1 to 21% oxygen concentrations. Compared to traditional fluorescence mode, the SBR value (∼108.69) of microsecond-range time-resolved in vitro imaging is up to 2.26 times greater in severe hypoxia (<0.1% O2), offering opportunities for precision imaging analysis in a hypoxic environment.

Multi-colour room-temperature phosphorescence from fused-ring compounds for dynamic anti-counterfeiting applications.

We present a facile strategy to achieve purely organic multi-colour room-temperature phosphorescence (RTP) films by doping typical fused-ring compounds into a poly(vinyl alcohol) matrix. Such RTP films demonstrate inherent RTP emission ranging from green to red with a long lifetime and high quantum yield (QY) (lifetime: ∼0.56 ms, QY: ∼35.4%). We further exploit such high-performance RTP films for dynamic information encryption.

Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network.

The application of edge computing combined with the Internet of Things (edge-IoT) has been rapidly developed. It is of great significance to develop a lightweight network for gearbox compound fault diagnosis in the edge-IoT context. The goal of this paper is to devise a novel and high-accuracy lightweight neural network based on Legendre multiwavelet transform and multi-channel convolutional neural network (LMWT-MCNN) to fast recognize various compound fault categories of gearbox. The contributions of this paper mainly lie in three aspects: The feature images are designed based on the LMWT frequency domain and they are easily implemented in the MCNN model to effectively avoid noise interference. The proposed lightweight model only consists of three convolutional layers and three pooling layers to further extract the most valuable fault features without any artificial feature extraction. In a fully connected layer, the specific fault type of rotating machinery is identified by the multi-label method. This paper provides a promising technique for rotating machinery fault diagnosis in real applications based on edge-IoT, which can largely reduce labor costs. Finally, the PHM 2009 gearbox and Paderborn University bearing compound fault datasets are used to verify the effectiveness and robustness of the proposed method. The experimental results demonstrate that the proposed lightweight network is able to reliably identify the compound fault categories with the highest accuracy under the strong noise environment compared with the existing methods.

Phosphorescence Enzyme-Mimics for Time-Resolved Sensitive Diagnostics and Environment-Adaptive Specific Catalytic Therapeutics.

Enzyme mimics (EMs) with intrinsic catalysis activity have attracted enormous interest in biomedicine. However, there is a lack of environmentally adaptive EMs for sensitive diagnosis and specific catalytic therapeutics in simultaneous manners. Herein, the coordination modulation strategy is designed to synthesize silicon-based phosphorescence enzyme-mimics (SiPEMs). Specifically, the atomic-level engineered Co-N4 structure in SiPEMs enables the environment-adaptive peroxidase, oxidase, and catalase-like activities. More intriguingly, the internal Si-O networks are able to stabilize the triplet state, exhibiting long-lived phosphorescence with lifetime of 124.5 ms, suitable for millisecond-range time-resolved imaging of tumor cells and tissue in mice (with high signal-to-background ratio values of ∼60.2 for in vitro and ∼611 for in vivo). Meanwhile, the SiPEMs act as an oxidative stress amplifier, allowing the production of ·OH via cascade reactions triggered by the tumor microenvironment (∼136-fold enhancement in peroxidase catalytic efficiency); while the enzyme-mimics can scavenge the accumulation of reactive oxygen species to alleviate the oxidative damage in normal cells, they are therefore suitable for environment-adaptive catalytic treatment of cancer in specific manners. We innovate a systematic strategy to develop high-performance enzymemics, constructing a promising breakthrough for replacing traditional enzymes in cancer treatment applications.