Critical aspects in exploring time analysis for the voltammetric estimation of kinetic parameters of surface electrode mechanisms coupled with chemical reactions

The accurate determination of the rate constant related to the electron transfer step of so-called “surface redox active compounds” by voltammetric measurements is very important because it is linked to the reactivity and stability of many biological and chemical systems such as redox enzymes, vitamins, hormones, and many more. Evaluation of the kinetics of the electron transfer is often challenging, especially when chemical equilibria are coupled to the electron transfer step. In this work, we theoretically consider some critical aspects of the time-related methodologies in square-wave voltammetry (SWV), which is designed to analyze the kinetics of the electron transfer step of surface mechanisms coupled with chemical reactions. We demonstrate with a series of simulated scenarios that caution must be taken when exploring the time-related analysis for kinetic characterizations for both surface CE and EC mechanisms. The main concern stems from the fact that the SW frequency simultaneously affects both the kinetics of electron transfer and that of chemical reactions as well. Under defined conditions, the SW frequency variation in the case of surface EC and CE mechanisms may produce unexpected features of the voltammetric patterns. In many scenarios, time-independent analysis, such as those related to the square-wave amplitude and potential increment, are seen as alternative tools to evaluate the rate parameter of electrode reactions.

Transient Absorption Spectroscopy of the Electron Transfer Step in the Photochemically Activated Polymerizations of N-ethylcarbazole and 9-phenylcarbazole.

The polymerization of photoexcited N-ethylcarbazole (N-EC) in the presence of an electron acceptor begins with an electron transfer (ET) step to generate a radical cation of N-EC (N-EC+.). Here, the...

TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection

We propose TandA, an effective technique for fine-tuning pre-trained Transformer models for natural language tasks. Specifically, we first transfer a pre-trained model into a model for a general task by fine-tuning it with a large and high-quality dataset. We then perform a second fine-tuning step to adapt the transferred model to the target domain. We demonstrate the benefits of our approach for answer sentence selection, which is a well-known inference task in Question Answering. We built a large scale dataset to enable the transfer step, exploiting the Natural Questions dataset. Our approach establishes the state of the art on two well-known benchmarks, WikiQA and TREC-QA, achieving the impressive MAP scores of 92% and 94.3%, respectively, which largely outperform the the highest scores of 83.4% and 87.5% of previous work. We empirically show that TandA generates more stable and robust models reducing the effort required for selecting optimal hyper-parameters. Additionally, we show that the transfer step of TandA makes the adaptation step more robust to noise. This enables a more effective use of noisy datasets for fine-tuning. Finally, we also confirm the positive impact of TandA in an industrial setting, using domain specific datasets subject to different types of noise.