Molecular Oligomerization and Donor Engineering Strategies for Achieving Superior NIR-II Fluorescence Imaging and Thermotherapy under 1064 nm Laser Irradiation.

An enormous challenge still exists for designing molecules with the second near-infrared (NIR-II, 1000-1700 nm) window absorption, NIR-II fluorescence emission, and batch-to-batch reproducibility, which is the premise for high-performance NIR-II phototheranostics. Although organic small molecules and polymers have been largely explored for phototheranostics, it is difficult to satisfy the above three elements simultaneously. In this work, molecular oligomerization (the general structure is S-D-A-D'-A-D-S) and donor engineering (changing the donor linker D') strategies are applied to design phototheranostic agents. Such strategies are proved to be efficient in adjusting molecular configuration and energy level, affecting the optical and thermal properties. Three oligomers (O-T, O-DT, and O-Q) are further prepared into water-soluble nanoparticles (NPs). Particularly, the O-T NPs exhibit a higher molar extinction coefficient at 1064 nm (≈4.3-fold of O-DT NPs and ≈4.8-fold of O-Q NPs). Furthermore, the O-T NPs show the highest NIR-II fluorescence brightness and heating capacity (PCE = 73%) among the three NPs under 1064 nm laser irradiation and served as agents for NIR-II imaging guided in vivo photothermal therapy. Overall, by using molecular oligomerization and donor engineering strategies, a powerful example of constructing high-performance NIR-II phototheranostics for clinical translation is given.

A Robust Predicted Performance Analysis Approach for Data-Driven Product Development in the Industrial Internet of Things.

Industrial Internet of Things (IoT) is a ubiquitous network integrating various sensing technologies and communication technologies to provide intelligent information processing and smart control abilities for the manufacturing enterprises. The aim of applying industrial IoT is to assist manufacturers manage and optimize the entire product manufacturing process to improve product quality and production efficiency. Data-driven product development is considered as one of the critical application scenarios of industrial IoT, which is used to acquire the satisfied and robust design solution according to customer demands. Performance analysis is an effective tool to identify whether the key performance have reached the requirements in data-driven product development. The existing performance analysis approaches mainly focus on the metamodel construction, however, the uncertainty and complexity in product development process are rarely considered. In response, this paper investigates a robust performance analysis approach in industrial IoT environment to help product developers forecast the performance parameters accurately. The service-oriented layered architecture of industrial IoT for product development is first described. Then a dimension reduction approach based on mutual information (MI) and outlier detection is proposed. A metamodel based on least squares support vector regression (LSSVR) is established to conduct performance prediction process. Furthermore, the predicted performance analysis method based on confidence interval estimation is developed to deal with the uncertainty to improve the robustness of the forecasting results. Finally, a case study is given to show the feasibility and effectiveness of the proposed approach.

An NIR-II-emitting type-I photosensitizer for efficient hypoxic tumor phototheranostics.

A small molecule-based NIR-II type-I photosensitizer (IT-IC) with a strong push-pull effect and good planar π-conjugated structure was synthesized. The IT-IC NPs exhibited strong light absorption, outstanding NIR-II fluorescence emission, excellent photothermal conversion and efficient type-I/II ROS generation, showing encouraging therapeutic outcomes for hypoxic tumors.

A diketopyrrolopyrrole-based small molecule with an extended conjugated skeleton and J-aggregation behavior for 808 nm laser triggered phototheranostics.

The development of phototheranostic agents, specifically those based on organic small molecules (OSMs) with long wavelength excitation/emission, is an attractive but challenging project. In this contribution, we designed and synthesized a novel conjugate small molecule with a linear structure, named DPP-OPIC. Water-soluble nanoparticle DPP-OPIC NPs were fabricated. They exhibited strong absorption in the region of 600-1000 nm, which was due to the extended conjugate length of the molecular skeleton and J-aggregation behavior. Under 808 nm laser excitation, DPP-OPIC NPs were capable of producing outstanding near-infrared-II (NIR-II, 900-1700 nm) fluorescence. The photoluminescence quantum yield was determined as 0.58%, which enabled high-resolution in vivo tumor imaging. Additionally, a notable photothermal effect with a high photothermal conversion efficiency (41.5%) was achieved by the irradiation of DPP-OPIC NPs. Hence, DPP-OPIC NPs can be used as superior phototheranostic agents, providing valuable contributions to NIR-II fluorescence imaging and photothermal therapy.

Design, fabrication and application of self-spiraling pattern-driven 4D-printed actuator.

Self-spiraling actuators are widely found in nature and have high research and actuator-application value in self-lock and self-assembly. Four-dimensional (4D) printing is a new generation additive manufacturing of smart materials and has shown great potential for the fabrication of multi-functional and customized structures. The microarchitecture design of a bilayer actuator could bring flexible and diversified self-spiraling behaviors and more possibilities for practical application by combing 4D printing. This work investigates the stimuli effects of fiber patterns and fabrication parameters on self-spiraling behaviors of the bilayer actuator via both experimental and theoretical methods. This work may potentially provide pattern design guidance for 4D-printed self-spiraling actuators to meet different application requirements.

Crashworthiness Optimization Design of Aluminum Alloy Thin-Walled Triangle Column Based on Bioinspired Strategy.

Aluminum alloy thin-walled structures have been well used in applications of energy absorption. In the present work, a bioinspired design strategy for aluminum alloy thin-walled structures is proposed to improve the performance of out-of-plane crashworthiness by altering the material distribution. According to the proposed strategy, a novel fractal thin-walled triangle column (FTTC) is designed, which is composed by iteratively applying the affine transformation of a base triangle up to 2nd-order. The finite element model is established to investigate the out-of-plane crashworthiness of FTTC and validated by experiment results. The numerical analysis of the crashworthiness of FTTC with different fractal orders (0th, 1st and 2nd) are performed, and the results show that 1st- and 2nd-order FTTC enhance the energy absorption of structures and crush force efficiency. In particular, 2nd-order FTTC has better energy absorption ability due to the optimal distribution of materials, which are efficiently organized by the proposed bioinspired design strategy. In addition, a parameter study is performed to investigate the effect of FTTC geometric details on the crushing procedure. The collapse mode shows that it tends to change from unstable to stable with the increase in thickness and side length and the decrease in height. Moreover, a positive relevant relationship is identified between the thickness and the crashworthiness for FTTC.

Identification of the cDNA Encoding the Growth Hormone Receptor (GHR) and the Regulation of GHR and IGF-I Gene Expression by Nutritional Status in Reeves' Turtle (Chinemys reevesii).

Chinemys reevesii (Reeves' turtle) is a slow-growing reptile that is distributed widely across China. Prior to this study, the cDNA sequence of the growth hormone receptor (GHR) in the Reeve's turtle, or how periods of starvation might influence the gene expression of GHR and insulin-like growth factor I (IGF-I) in this species, were unknown. Here, we identified the full-length sequence of the cDNA encoding GHR in Reeves' turtle by using RT-PCR and RACE. The full-length GHR cDNA was identified to be 3936 base-pairs in length, with a 1848 base-pair open reading frame (ORF) that encodes a 615 amino acid protein. Analysis showed that GHR mRNA was detectable in a wide range of tissues; the highest and lowest levels of expression were detected in the liver and the gonad, respectively. IGF-I was also expressed in a range of tissues, but not in the gonad; the highest levels of IGF-I expression were detected in the liver. After 4 weeks of fasting, the expression levels of GHR and IGF-I in the liver had decreased significantly; however, these gradually returned to normal after refeeding. We report the first cloned cDNA sequence for the GHR gene in the Reeve's turtle. Our findings provide a foundation from which to investigate the specific function of the GHR in Reeve's turtle, and serve as a reference for studying the effects of different nutrient levels on GHR expression in this species.

Excellent oxygen evolution reaction of NiO with a layered nanosphere structure as the cathode of lithium-oxygen batteries.

A layered nanosphere structured NiO catalyst was successfully synthesized by a simple and efficient hydrothermal method as a cathode material for lithium-oxygen (Li-O2) batteries. Cyclic voltammetry (CV), dual electrode voltammetry (DECV) and chronoamperometry (CA) by rotating ring-disk electrode (RRDE) were carried out to investigate the catalytic activity of this catalyst for the oxygen evolution reaction (OER). The results revealed that the layered nanosphere NiO exhibited excellent electrochemical performance, stability and a typical four-electron reaction as a cathode electrocatalyst for rechargeable nonaqueous Li-O2 batteries. The overpotential of the NiO is only up to 0.61 V. X-ray photoelectron spectroscopy (XPS) characterization shows that the Li2O2 and Li2CO3 formed during the discharge process and decomposed after charging. Moreover, the cut-off voltage of discharging is about 2.0 V in the NiO-based Li-O2 batteries, while the specific capacity is up to 3040 mA h g-1. There is no obvious performance decline of the battery after 50 cycles at a current density of 0.1 mA cm-2 with a superior limited specific capacity of 800 mA h g-1. Herein, the layered nanosphere structured NiO catalyst is considered a promising cathode electrocatalyst for Li-O2 batteries.