The electroreduction of p-benzoquinone ͑BQ͒ in unbuffered aqueous solution was studied by electrochemical and spectroelectrochemical techniques. p-Benzoquinone anion radical formed in the process of p-benzoquinone electroreduction in aqueous solution was confirmed by in situ electron spin resonance spectrum for the first time. In situ transmission UV-visible spectra of electrogenerated BQ anion radical were also studied in channel cell. In aqueous solution BQ anion radical was not generated from the one-electron direct electroreduction of BQ, but from the comproportionation reaction of benzoquinone dianion and the parent benzoquinone. A mechanism of radical formation and decay is proposed, and the rate constant of BQ anion radical decay is estimated according to the approximation simulation solution of the postulated mechanism in a channel cell.

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Purpose This study aims to address the challenge of training a detection model for the robot to detect the abnormal samples in the industrial environment, while abnormal patterns are very rare under this condition. Design/methodology/approach The authors propose a new model with double encoder–decoder (DED) generative adversarial networks to detect anomalies when the model is trained without any abnormal patterns. The DED approach is used to map high-dimensional input images to a low-dimensional space, through which the latent variables are obtained. Minimizing the change in the latent variables during the training process helps the model learn the data distribution. Anomaly detection is achieved by calculating the distance between two low-dimensional vectors obtained from two encoders. Findings The proposed method has better accuracy and F1 score when compared with traditional anomaly detection models. Originality/value A new architecture with a DED pipeline is designed to capture the distribution of images in the training process so that anomalous samples are accurately identified. A new weight function is introduced to control the proportion of losses in the encoding reconstruction and adversarial phases to achieve better results. An anomaly detection model is proposed to achieve superior performance against prior state-of-the-art approaches.