P-doping of conjugated polymers requires electron transfer from the conjugated polymer to the p-dopant. This implies that the highest occupied molecular orbital (HOMO) of the conjugated polymer has to be higher than the lowest unoccupied molecular orbital (LUMO) of the p-dopant. Although commonly used p-dopants such as 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) have a low LUMO of −5.24 eV, most conjugated polymers used in high-performance field-effect transistors are donor–acceptor-type polymers with deep HOMO values, making them difficult to be effectively doped by F4TCNQ. Here, we utilized the proquinoidal 2,6-dialkyl-benzo­[1,2-d;4,5-d′]­bistriazole (BBTa26) moiety in conjugated polymers to destabilize HOMO, allowing effective p-doping using very dilute F4TCNQ solutions. The extent of the quinoidal character and hence their intrinsic conductivities and the ability to be doped are dependent on the dihedral angles and aromaticity of the aryl spacer groups along the polymer backbone. Intrinsic conductivities as high as 10–2 S cm–1 were achieved. Upon doping using F4TCNQ, highly delocalized polarons were observed. As such, electrical conductivities of over 100 S cm–1 and an enhancement of the Seebeck coefficient from carrier-induced softening can be achieved. A maximum power factor of 11.8 μW m–1 K–2 was achieved in thin-film thermoelectric devices. These results are among the highest for solution-phase p-doping using F4TCNQ without additional processing.

Metal-salen molecular cages are efficient and recyclable heterogeneous catalysts for cycloaddition of CO2, achieving full conversion at ambient conditions.

Intensive research in electrochemical CO2 reduction reaction has resulted in the discovery of numerous high-performance catalysts selective to multi-carbon products, with most of these catalysts still being purely transition metal based. Herein, we present high and stable multi-carbon products selectivity of up to 76.6% across a wide potential range of 1 V on histidine-functionalised Cu. In-situ Raman and density functional theory calculations revealed alternative reaction pathways that involve direct interactions between adsorbed histidine and CO2 reduction intermediates at more cathodic potentials. Strikingly, we found that the yield of multi-carbon products is closely correlated to the surface charge on the catalyst surface, quantified by a pulsed voltammetry-based technique which proved reliable even at very cathodic potentials. We ascribe the surface charge to the population density of adsorbed species on the catalyst surface, which may be exploited as a powerful tool to explain CO2 reduction activity and as a proxy for future catalyst discovery, including organic-inorganic hybrids.

Binary catalyst systems comprising a cationic Ru-CNC pincer complex and an alkali metal salt were developed for selective hydroboration of CO utilizing pinacolborane at r.t. and 1 atm CO, with the combination of [Ru(CNC)(CO)(H)][PF] and KOCOBu producing formoxyborane in 76% yield. A bicyclic catalytic mechanism was proposed and discussed.

Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of experimental parameters in automated active learning driven high throughput experiments in materials science and chemistry. Previous studies suggest that optimization performance of the typical surrogate model in the BO algorithm, Gaussian processes (GPs), may be limited due to its inability to handle complex datasets. Herein, various surrogate models for BO, including GPs and neural network ensembles (NNEs), are investigated. Two materials datasets of different complexity with different properties are used, to compare the performance of GP and NNE—the first is the compressive strength of concrete (8 inputs and 1 target), and the second is a simulated high‐dimensional dataset of thermoelectric properties of inorganic materials (22 inputs and 1 target). While NNEs can converge faster toward optimum values, GPs with optimized kernels are able to ultimately achieve the best evaluated values after 100 iterations, even for the most complex dataset. This surprising result is contrary to expectations. It is believed that these findings shed new light on the understanding of surrogate models for BO, and can help accelerate the inverse design of new materials with better structural and functional performance.

A highly π-acidic dipyridinium-naphthalene diimide acceptor shows anion-π interactions with halides and PF6-. Lewis basicity and redox potential of the anion affect the chemistry, and photophysical and electrochemical properties, as well as both ionic and electrical conductivities. Our results provide insights into doping, degradation and ion transport mechanisms in organic n-type semiconductors.

Anion–π-radical interaction plays a key role in photophysical and magnetic properties.

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