Organophosphate pesticides are used in agriculture due to their high effectiveness and low persistence in eradicating insects and pests. However, conventional detection methods encounter the limitation of undesired detection specificity. Thus, screening phosphonate-type organophosphate pesticides (OOPs) from their analogues, phosphorothioate organophosphate pesticides (SOPs), remains a challenge. Here, we reported a D-penicillamine@Ag/Cu nanocluster (DPA@Ag/Cu NCs)based fluorescence assay to screen OOPs from 21 kinds of organophosphate pesticides, which can be used for logic sensing and information encryption. Acetylthiocholine chloride was enzymatically split by acetylcholinesterase (AChE) to produce thiocholine, which reduced the fluorescence of DPA@Ag/Cu NCs due to the transmission of electrons from DPA@Ag/Cu NCs donor to the thiol group acceptor. Impressively, OOPs acted as an AChE inhibitor and retained the high fluorescence of DPA@Ag/Cu NCs due to the stronger positive electricity of the phosphorus atom. Conversely, SOPs possessed weak toxicity to AChE, which led to low fluorescence intensity. By setting 21 kinds of organophosphate pesticides as the inputs and the fluorescence of the resulting products as the outputs, DPA@Ag/Cu NCs could serve as a fluorescent nanoneuron to construct Boolean logic tree and complex logic circuit for molecular computing. As a proof of concept, by converting the selective response patterns of DPA@Ag/Cu NCs into binary strings, molecular cryptosteganography for encoding, storing, and concealing information was successfully achieved. This study is expected to advance the progress and practical application of nanoclusters in the area of logic detection and information security while also enhancing the relationship between molecular sensors and the world of information.
This paper proposes a new method to predict the spindle deformation based on temperature data. The method introduces ANFIS (adaptive neuro-fuzzy inference system). For building the predictive model, we first extract temperature data from sensors in the spindle, and then they are used as the inputs to train ANFIS. To evaluate the performance of the prediction, an experiment is implemented. Three Pt-100 thermal resistances is used to monitor the spindle temperature, and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that our prediction model can better predict the spindle deformation and improve the performance of the spindle.
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