Computational Design of Quaterpyridine‐Based Fe/Mn–Complexes for the Direct Hydrogenation of CO₂ to HCOOH: A Direction for Atom‐Economic Approach

Abstract

On the basis of first principle calculations, we propose a new class of earth-abundant quaterpyridine complexes for the direct hydrogenation of CO 2 to HCOOH. The mechanistic studies reveal that the complexes are advantageous in many aspects. First of all, they can perform both in added-base and base-free conditions, the latter being atom-economic as well as a greener way. Most effectively, the energy barrier for the heterolytic hydrogen cleavage and hydride transfer processes could easily be monitored by changing the p-acidic character of the axial ligand, solvent polarity of the medium and also by ligand modification through substitution.

Brain activity reveals multiple motor-learning mechanisms in a real-world task

“…In the following, we will discuss how we could map the distinct groups of learners we discovered in our real-world task into the above learning frameworks (error-based and reward-based). We speculate that the group that showed the neural patterns which were previously reported in error-based motor adaptation (PMBR increase, (Tan et al, 2014a, 2016)) and behavioural patterns of error-based adaptation (e.g. no decay in AFC, small decay in intertrial-variability, low trial-to-trial change) – probably used more error-based adaptation to adapt an existing motor control policy.…”

“…PMBR was reported to increase over learning in adaptation error-based learning tasks (e.g. Tan et al, 2014a, 2016; Torrecillos et al, 2015), showing negative correlations with the decreasing errors. On the other hand, in skill-learning tasks it was reported to decrease (itself or its magnetic resonance spectroscopy correlate) over the learning (e.g.…”

“…EEG signals were first band-pass filtered at 5-35 Hz using a basic FIR filter, and then decomposed into independent component (IC) and artefact ICs were removed with ADJUST, an EEGLAB plug-in for automatic artefact detection (Mognon et al, 2011). Following previous PMBR studies in motor learning (Tan et al, 2014a, 2016; Torrecillos et al, 2015; Alayrangues et al, 2019), all further analysis was performed on the EEG activity over the motor cortex contralateral to the moving arm. As all subjects were right-handed and the movement during the trial was done almost exclusively by the right arm (Haar et al, 2019), we focused on the left motor cortex.…”

“…Each block was transformed in the time-frequency domain by convolution with the complex Morlet wavelets in 1 Hz steps. Event-related EEG power change was subsequently calculated as the percentage change by log-transforming the raw power data and then normalizing relative to the average power calculated over the block, as no clear baseline could be defined during the task (Tan et al, 2014a, 2016; Torrecillos et al, 2015; Alayrangues et al, 2019), and then subtracting one from the normalized value and multiplying by 100. While this normalization procedure might be less common than one based on motion-free pre-movement baseline period, it was used by most of the PMBR motor learning studies mentioned above and enabled the natural free-behaviour aspect of the task of self-paced movement, with as many preparatory movements as the subject needs for each shoot, and no go-cues or hold-cues.…”

“…In motor adaptation studies, PMBR over the motor cortex contralateral to the moving hand was reported to negatively correlate with movement errors, lower errors induced higher PMBR (e.g. Tan et al, 2014a, 2016; Torrecillos et al, 2015) and therefore PMBR increases over learning. In reward-based tasks the PMBR shows the opposite trend; e.g., in a force tracking task PMBR decreased with learning (Kranczioch et al, 2008).…”