Phosphorescence has rarely been observed in pure organic chromophore systems at room temperature. We herein report efficient phosphorescence from the crystals of benzophenone and its derivatives with a general formula of (X-C 6 H 4 ) 2 CdO (X ) F, Cl, Br) as well as methyl 4-bromobenzoate and 4,4′-dibromobiphenyl under ambient conditions. These luminogens are all nonemissive when they are dissolved in good solvents, adsorbed on TLC plates, and doped into polymer films, because active intramolecular motions such as rotations and vibrations under these conditions effectively annihilate their triplet excitons via nonradiative relaxation channels. In the crystalline state, the intramolecular motions are restricted by the crystal lattices and intermolecular interactions, particularly C-H · · · O, N-H · · · O, C-H · · · X (X ) F, Cl, Br), C-Br · · · Br-C, and C-H · · · π hydrogen bonding. The physical constraints and multiple intermolecular interactions collectively lock the conformations of the luminogen molecules. This structural rigidification effect makes the luminogens highly phosphorescent in the crystalline state at room temperature.

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Intracellular pH (pHi) is an important parameter associated with cellular behaviors and pathological conditions. Sensing pHi and monitoring its changes in live cells are essential but challenging due to the lack of effective probes. We herein report a pH-sensitive fluorogen for pHi sensing and tracking. The dye is a tetraphenylethene-cyanine adduct (TPE-Cy). It is biocompatible and cell-permeable. Upon diffusing into cells, it responds sensitively to pHi in the entire physiological range, visualizing the acidic and basic compartments with intense red and blue emissions, respectively. The ratiometric signal of the red and blue channels can thus serve as an indicator for local proton concentration. The utility of TPE-Cy in pHi imaging and monitoring is demonstrated with the use of confocal microscopy, ratiometric analysis, and flow cytometry.

CO(2) sensing is of great societal implications, as CO(2) is a component of gas mixtures from many natural and anthropogenic processes with huge impacts on globe climate and human well-being. Herein we report a CO(2) assay scheme over a wide concentration range, utilizing a fluorogen with an aggregation-induced emission feature and a liquid with tunable polarity and viscosity. The CO(2) sensing process is specific, quantitative, and interferent tolerant.

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： Cleaner production (CP) is considered as one of the most important means for manufacturing enterprises to achieve sustainable production and improve their sustainable competitive advantage. However, implementation of the CP strategy was facing barriers, such as the lack of complete data and valuable knowledge that can be employed to provide better support on decision-making of coordination and optimization on the product lifecycle management (PLM) and the whole CP process. Fortunately, with the wide use of smart sensing devices in PLM, a large amount of real-time and multi-source lifecycle big data can now be collected. To make better PLM and CP decisions based on these data, in this paper, an overall architecture of big data-based analytics for product lifecycle (BDA-PL) was proposed. It integrated big data analytics and service-driven patterns that helped to overcome the above-mentioned barriers. Under the architecture, the availability and accessibility of data and knowledge related to the product were achieved. Focusing on manufacturing and maintenance process of the product lifecycle, and the key technologies were developed to implement the big data analytics. The presented architecture was demonstrated by an application scenario, and some observations and findings were discussed in details. The results showed that the proposed architecture benefited customers, manufacturers, environment and even all stages of PLM, and effectively promoted the implementation of CP.In addition, the managerial implications of the proposed architecture for four departments were analyzed and discussed.The new CP strategy provided a theoretical and practical basis for the sustainable development of manufacturing enterprises.

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As a major challenge and opportunity for traditional manufacturing, intelligent manufacturing is facing the needs of sustainable development in future. Sustainability assessment undoubtedly plays a pivotal role for future development of intelligent manufacturing. Aiming at this, the paper presents the digital twin driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle based on the classic digital twin mapping system. The sustainability assessment method segment of the architecture includes indicator system building, indicator value determination, indicator importance degree determination and intelligent manufacturing project assessing. A novel approach for treating the ambiguity of expert' judgment in indicator value determination by introducing trapezoidal fuzzy number into analytic hierarchy process is proposed, while the complexity of the influence relationship among the indicators is processed by the integration of complex networks modeling and PROMETHEE II for the indicator importance degree determination. A two-stage evidence combination model based on evidence theory is built for intelligent manufacturing project assessing lastly. The presented digital-twin-driven information architecture and the sustainability assessment method is tested and validated on a study of sustainability assessment of 8 intelligent manufacturing projects of an air conditioning enterprise. The results of the presented method were validated by comparing them with the results of the fuzzy and rough extension of the PROMETHEE II, TOPSIS and VIKOR methods, indicator importance degree determining method by entropy and indicator value determining method by accurate expert scoring.