Please cite this article in press as: Veenstra et al., Laser-Microstructured Copper Reveals Selectivity Patterns in the Electrocatalytic Reduction of
CO2, Chem (2020), https://doi.org/10.1016/j.chempr.2020.04.001
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Article
Laser-Microstructured Copper Reveals Selectivity
Patterns in the Electrocatalytic Reduction of CO2
Florentine L.P. Veenstra,1 Norbert Ackerl,2 Antonio J. Martın,1 and Javier Pe´ rez-Ramırez1,3,
´
´
SUMMARY
The Bigger Picture
It is of utmost importance to
convert CO2 into useful
The strategy of engineering the local chemical environment to direct
selectivity in the electroreduction of CO2 toward value-added prod-
ucts is only qualitatively understood. The unfeasibility of local con-
centration measurements and the limited applicability of simula-
tions to practical systems hinder more precise guidelines. Here,
we quantify the impact of the (electro)chemical environment on
the selectivity pattern by using microstructured copper (Cu) elec-
trodes prepared by ultra-short pulse laser ablation. We created
regularly distributed micro-probes and assessed their product dis-
tributions at distinct overpotentials. The regular geometry enabled
the accurate simulation of the local pH and CO2 concentration.
Selectivity maps useful for mechanistic and applied studies
emerged. They revealed clear patterns for C1–C3 products, suggest-
ing novel insights, such as the presence of two reaction mechanisms
for propanol. The effect on the selectivity pattern of operating pa-
rameters, such as enhanced mass transport and electrolyte compo-
sition, was also predicted by the maps.
compounds to close the carbon
cycle. Through electroreduction,
value-added compounds can be
obtained when using a copper
(Cu)-based catalyst, albeit with
limited control on the product
distribution. Currently, we know
qualitatively that the latter is
largely influenced by the
environment next to the electrode
surface, but a deeper
understanding of this effect is
necessary for transforming it into
an effective design tool for
material and process
optimization.
By combining experiments and
simulations on laser-
INTRODUCTION
The electrocatalytic CO2 reduction reaction (eCO2RR) toward fuels and chemicals in
aqueous media must still overcome technological challenges to contribute toward a
sustainable, circular economy. The lack of selectivity control on copper (Cu)-based
systems stands out among them given that Cu is the only material known to promote
value-added products, such as hydrocarbons, alcohols, and aldehydes, at appre-
ciable rates.1–3 The insufficient understanding of the variables to be optimized hin-
ders precise strategies for catalyst design, operating conditions, and electrode
microstructured Cu electrodes
with defined geometries, we
quantified the effect of the
(electro)chemical environment on
the product distribution,
generating selectivity maps that
accurately predict the influence of
key operating conditions. Our
study thus provides a powerful
tool for engineering devices for
the distributed production of fuels
and chemicals as it is of direct
application in the so-called
artificial leaves.
Historically, research in this field has been focused on materials development,6,7 but
the recent recognition of the local (electro)chemical environment as a key directing
agent for selectivity has opened new avenues. The applied potential and the surface
coverage of H and CO2 are central parameters influencing the eCO2RR and the
competing hydrogen evolution reaction (HER). However, their separated analysis
is unattainable because they are intertwined in the double-layer region by the car-
bonate chemical equilibria, the diffusion processes, and the interaction of charged
species in the intense electric field.4 Despite the complexity involved, the literature
shows how activity and/or selectivity can be successfully enhanced via locally tuned
electric fields,8 highly alkaline environments,9,10 mixed chemical-electrocatalytic
reduction routes,11 or free cationic and anionic species present.12,13 In this context,
rationalization efforts have become necessary for translating these observations into
quantitative guidelines for the optimization of the local environment. These efforts
Chem 6, 1–16, July 9, 2020 ª 2020 Elsevier Inc.
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