Udfoldning af adsorption på metal nanopartikler:Forbindelse af stabilitet med katalyse

Metal nanopartikler har fået stor opmærksomhed på grund af deres anvendelser inden for forskellige områder fra medicin, katalyse, energi og miljø. Imidlertid, de grundlæggende egenskaber ved nanopartikeladsorption på en overflade mangler at blive forstået. James Dean og et tværfagligt forskerhold i afdelingen for Kemiteknik, i USA indført en universel adsorptionsmodel for at tage højde for de strukturelle egenskaber, metalsammensætning og forskellige adsorbater af nanopartikler via maskinlæring (ML). Modellen passer til et stort antal data for nøjagtigt at forudsige adsorptionstendenser på monometalliske og legeringsbaserede nanopartikler. Skabelonen var enkel og leverede hurtigt beregnede data for metaller og adsorbater. Forskerholdet forbandt adsorptionen med stabilitetsadfærd for at fremme designet af optimale nanopartikler til applikationer af interesse. Forskningen er nu offentliggjort på Videnskabens fremskridt .

Metal nanopartikler (NP'er) har betydelige anvendelser inden for katalyse, lige fra brændstof- og kemikalieproduktion, til solenergi og kemisk energi. Men deres stabilitet og katalytiske aktivitet viser generelt modsatrettede tendenser, hvor meget aktive katalysatorer kun kan fungere i nogle få cykler. Et nøgletræk ved omfanget af metallisk katalytisk funktionalitet afhænger af adsorptionsstyrken for en række arter på katalysatoroverfladen. Ifølge Sabatier-princippet, udviklet for mere end et århundrede siden, aktive katalysatorer bør binde adsorbater med en bindingsstyrke, der hverken er stærk eller svag. Mens stærkt adsorberede arter kan forgifte katalysatoroverfladen, svagt bundne reaktanter desorberes let. I et mellemscenarie, reaktanterne kan møde hinanden og reagere på de katalytiske overflader. Forskere bruger i øjeblikket beregningssimulering og teoretiske kemimetoder til at stimulere katalytisk adfærd på metalkatalysatorer med stor nøjagtighed til at guide efterfølgende eksperimenter i laboratoriet.

Beregningsmæssige bestræbelser har fokuseret på at screene forskellige metalkatalysatorer for at opdage den "magiske" bindingsenergi (BE) af kemiske arter på katalysatoroverflader for at danne meget aktive katalysatorer. In silico design af katalytisk aktive materialer, imidlertid, mangler at blive realiseret. Ulemperne skyldes hovedsageligt designbestræbelser, der ofte forsømmer katalysatorernes stabilitet. NP-katalysatorer har også en høj grad af site-heterogenitet på deres overflade til adsorption og katalyse. Forskere havde udviklet adsorptionsmodeller til at relatere bindingsenergi af adsorbaterne til overfladekarakteristika for NP'er, såsom koordinationsnumre (CN'er) for at forstå det stedspecifikke adsorptionsrespons. Endnu, for klarhed, variationen i bindingsenergi (BE) medfører også sekundære deskriptorer såsom krumning og elektroniske egenskaber af NP'er.

I nærværende arbejde, Dean et al. anvendt tæthedsfunktionel teori (DFT) og maskinlæringsteknikker til at udlede en simpel fysikbaseret model til nøjagtigt at fange den varierende adsorptionsenergi. De estimerede variablen som en funktion af det lokale adsorptionsstedsmiljø på NP-overfladen såvel som typen af ​​metal-NP. Den generaliserede model kunne anvendes på enhver metalnanostruktur for at forstå adsorptionsadfærd på NP-katalysatoren og katalysatorens stabilitet; at screene og designe katalysatorer til adskillige applikationer.

Forskerne antog først de vigtigste faktorer mellem monometalliske NP'er og adsorbater. De definerede derefter den lokale sammenhængende energi (CE _lokal ) i bulkmetaller og opfangede CE'er i NP'er ved hjælp af en bindingscentreret model, som opsummerede hver metal-metal bindingsenergi. By applying similar concepts, they described the stability of binding sites and showed how chemically unsaturated sites (fewer metal-metal bonds) bound adsorbates with an increased strength. The research team focused on describing the binding capacity of a single adsorbate-metal pair. They plotted the DFT-calculated binding energy of carbon monoxide (CO) to a 172-atom gold (Au) cube and a 147-atom gold (Au) cuboctahedron or icosahedron. The team observed a strongly inverse relationship between the local cohesive energy (CE _lokal ) and binding energy (BE) to suggest the strongest adsorption sites to be those exhibiting the weakest local cohesion.

The team further developed their model and performed ordinary least squares (OLS) regression to understand adsorption on monometallic NPs and slabs using three adsorbates [Methyl radical (CH ₃ ), CO, hydroxyl radical (OH)] on three different metals (Cu, Ag—silver, Au). The metallic NPs contained different morphologies (172-atom cube, 55- and 147-atom icosahedron and 55- and 147-atom cuboctahedron). They observed that the binding affinity to the adsorbates decreased as the cohesion of the local sites increased. And as the adsorbate's chemical potential increased, they became less stable and bound a metal NP with higher tendency. The direct correlation with the metal Adsorbate (MAD) intuitively described the tendency of the metal to bind the adsorbate.

Dean et al. tested the generalizability of the model and trained the simulation on a single metal or single morphology, although it accurately captured other metals or morphologies as well. The work provided strong evidence that the model captured the underlying physics of the binding interactions, allowing the team to extend the work from non-periodic NPs to periodic slab systems. Computationally inexpensive systems could parameterize the model to extend to larger systems, which was not thus far possible due to the computational costs involved.

Dean et al. then extended the model from monometallic NPs to bimetallic systems. Til disse eksperimenter, they plotted the BE—trained on monometallic NPs, across several sites of bi-metallic, 55-atom icosahedron NPs (Cu ₃₁ Ag ₂₄ and Cu ₂₂ Ag ₃₃ ). The model very accurately captured trends in adsorption on the bimetallic Cu/Ag NPs as well. This was an interesting result since the scientists had only trained the model on monometallic systems. The results showed the generalizability of the model for both monometallic and bimetallic NPs. Imidlertid, the team will account additional descriptors including binding site electronegativity to understand the adsorption behavior for bimetallic systems in depth.

Although Dean et al. trained the ML (machine learning) algorithm to capture the adsorption trends of just one type of d9 metal, it could accurately predict the behavior of similar d9 metals (Cu—coper, Ag and Au). When they trained the model on a dataset of CH _3, CO and OH adsorbed to Cu, Ag and Au NPs, they could also capture general adsorption trends for similar elements in other columns of the periodic table. They then improved the complexity of the machine learning techniques to provide additional avenues to improve the model of adsorption.

På denne måde James Dean and his colleagues developed a simple yet powerful physics-based model to capture trends on the strength of binding interactions between different adsorbates and metal NPs using machine learning techniques. The study was the first to develop an adsorption model that accurately connected the properties of diverse metal NPs with the stability of the adsorption site. The model introduced simple descriptors to capture the adsorption on any site, relative to monometallic and bimetallic NPs. The team generalized the model to effectively stimulate a range of binding interactions, including variations on the types of metals, their composition, sites of adsorption and adsorbates.

Although the team did not test the applicability of the model for ternary systems, the physical properties may remain relevant to accurately model multimetallic systems as well. The adsorption model can accurately describe the binding strength of a variety of molecules on any site of NPs, including alloys. The scientists expect the model to be highly applicable as a screening tool for the high throughput search of potential catalysts.

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