We present the orbitalwise coordination number CN^{α} (α=s or d) as a reactivity descriptor for metal nanocatalysts. With the noble metal Au (5d^{10}6s^{1}) as a specific case, the CN^{s} computed ...using the two-center s-electron hopping integrals to neighboring atoms provides an accurate and robust description of the trends in CO and O adsorption energies on extended surfaces terminated with different facets and nanoparticles of varying size and shape, outperforming existing bond-counting methods. Importantly, the CN^{s} has a solid physiochemical basis via a direct connection to the moment characteristics of the projected density of states onto the s orbital of a Au adsorption site. Furthermore, the CN^{s} shows promise as a viable descriptor for predicting adsorption properties of Au alloy nanoparticles with size-dependent lattice strains and coinage metal ligands.
Abstract
Building upon the
d
-band reactivity theory in surface chemistry and catalysis, we develop a Bayesian learning approach to probing chemisorption processes at atomically tailored metal sites. ...With representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption properties of transition metals predict site reactivity at a diverse range of intermetallics and near-surface alloys while naturally providing uncertainty quantification from posterior sampling. More importantly, this conceptual framework sheds light on the orbitalwise nature of chemical bonding at adsorption sites with
d
-states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronic descriptors for the prediction of novel catalytic materials.
The field of heterogeneous photocatalysis has almost exclusively focused on semiconductor photocatalysts. Herein, we show that plasmonic metallic nanostructures represent a new family of ...photocatalysts. We demonstrate that these photocatalysts exhibit fundamentally different behaviour compared with semiconductors. First, we show that photocatalytic reaction rates on excited plasmonic metallic nanostructures exhibit a super-linear power law dependence on light intensity (rate ∝ intensity(n), with n > 1), at significantly lower intensity than required for super-linear behaviour on extended metal surfaces. We also demonstrate that, in sharp contrast to semiconductor photocatalysts, photocatalytic quantum efficiencies on plasmonic metallic nanostructures increase with light intensity and operating temperature. These unique characteristics of plasmonic metallic nanostructures suggest that this new family of photocatalysts could prove useful for many heterogeneous catalytic processes that cannot be activated using conventional thermal processes on metals or photocatalytic processes on semiconductors.
The electrochemical nitrate reduction reaction (NO
RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, ...tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO
is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO
RR to ammonia with a Faradaic efficiency of 92.5% at -0.5 V
and a yield rate of 6.25 mol h
g
at -0.6 V
. This study provides machine-learned design rules besides the d-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.
Catalysis plays a critical role in chemical conversion, energy production and pollution mitigation. High activation barriers associated with rate-limiting elementary steps require most commercial ...heterogeneous catalytic reactions to be run at relatively high temperatures, which compromises energy efficiency and the long-term stability of the catalyst. Here we show that plasmonic nanostructures of silver can concurrently use low-intensity visible light (on the order of solar intensity) and thermal energy to drive catalytic oxidation reactions--such as ethylene epoxidation, CO oxidation, and NH₃ oxidation--at lower temperatures than their conventional counterparts that use only thermal stimulus. Based on kinetic isotope experiments and density functional calculations, we postulate that excited plasmons on the silver surface act to populate O₂ antibonding orbitals and so form a transient negative-ion state, which thereby facilitates the rate-limiting O₂-dissociation reaction. The results could assist the design of catalytic processes that are more energy efficient and robust than current processes.
Electrochemical reduction of N
to NH
provides an alternative to the Haber-Bosch process for sustainable, distributed production of NH
when powered by renewable electricity. However, the development ...of such process has been impeded by the lack of efficient electrocatalysts for N
reduction. Here we report efficient electroreduction of N
to NH
on palladium nanoparticles in phosphate buffer solution under ambient conditions, which exhibits high activity and selectivity with an NH
yield rate of ~4.5 μg mg
h
and a Faradaic efficiency of 8.2% at 0.1 V vs. the reversible hydrogen electrode (corresponding to a low overpotential of 56 mV), outperforming other catalysts including gold and platinum. Density functional theory calculations suggest that the unique activity of palladium originates from its balanced hydrogen evolution activity and the Grotthuss-like hydride transfer mechanism on α-palladium hydride that lowers the free energy barrier of N
hydrogenation to *N
H, the rate-limiting step for NH
electrosynthesis.
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•A machine-learning chemisorption model is developed for accelerating catalyst discovery.•Local electronegativity and effective coordination are used as input features.•Promising ...alloys for CO2 electroreduction are identified using the model.
We integrate machine-learning algorithms into the descriptor-based design approach for rapid screening of transition-metal catalysts. By engineering numerical representation of surface metal atoms using easily accessible features such as the local electronegativity and the effective coordination number that are dependent on the surroundings of an adsorption site, together with the intrinsic properties of active metal atoms including the electronegativity, ionic potential, and electron affinity, the machine-learning model optimized with ∼250 ab initio adsorption energies on bimetallic alloys can capture complex, non-linear adsorbate/substrate interactions with the root mean squared errors (RMSE) ∼0.12eV. We applied the model to search for {100}-terminated multimetallic copper (Cu) catalysts for electrochemical CO2 reduction where the *CO adsorption energy represents an important efficiency metric. Compared with the traditional high-throughput computational and experimental trial-and-error approach, the machine-learning chemisorption models have great potential in accelerating the discovery of interesting catalytic materials. As the complexity of catalyst structures increases, new features will be needed to learn underlying correlations and avoid introducing significant errors on top of the average DFT prediction errors expected with standard semi-local generalized gradient approximation (GGA) functionals.
Ischemic stroke is a leading cause of long-term disability and death worldwide. Current drug delivery vehicles for the treatment of ischemic stroke are less than satisfactory, in large part due to ...their short circulation lives, lack of specific targeting to the ischemic site, and poor controllability of drug release. In light of the upregulation of reactive oxygen species (ROS) in the ischemic neuron, we herein developed a bioengineered ROS-responsive nanocarrier for stroke-specific delivery of a neuroprotective agent, NR2B9C, against ischemic brain damage. The nanocarrier is composed of a dextran polymer core modified with ROS-responsive boronic ester and a red blood cell (RBC) membrane shell with stroke homing peptide (SHp) inserted. These targeted “core–shell” nanoparticles (designated as SHp-RBC-NP) could thus have controlled release of NR2B9C triggered by high intracellular ROS in ischemic neurons after homing to ischemic brain tissues. The potential of the SHp-RBC-NP for ischemic stroke therapy was systematically evaluated in vitro and in rat models of middle cerebral artery occlusion (MCAO). In vitro results showed that the SHp-RBC-NP had great protective effects on glutamate-induced cytotoxicity in PC-12 cells. In vivo pharmacokinetic (PK) and pharmacodynamic (PD) testing further demonstrated that the bioengineered nanoparticles can drastically prolong the systemic circulation of NR2B9C, enhance the active targeting of the ischemic area in the MCAO rats, and reduce ischemic brain damage.
Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited ...generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties.
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