From egg-shell to uniform distribution of platinum by atomic layer deposition on mesoporous alumina spheres: experiments and modeling

Abstract

Uniform material distribution by atomic layer deposition (ALD) inside porous materials is needed in multiple applications, including batteries and catalysis. Attaining this uniformity is not trivial, diffusion within the porous network being one of the main limiting factors. This work used a fluidized bed atmospheric ALD reactor to coat millimeter-size mesoporous alumina spheres with platinum, using the process based on (methylcyclopentadienyl)trimethylplatinum [MeCpPtMe₃] and oxygen. Using different exposure times and five reaction cycles, materials with platinum loading up to ∼4 wt% were prepared. The growth per cycle, expressed as average areal number density, was approximately 0.1 Pt atoms per nm². Cross-sectional analysis done using low-energy ion scattering indicated that with increasing exposure time, platinum distribution evolved from egg-shell to macroscopic uniform distribution through the particles. Diffusion–reaction modeling was done to support the experiments and showed a saturation of the Pt weight loading after uniform distribution. This work shows that it is possible to get a uniform distribution of platinum through mesoporous particles with an aspect ratio on the order of 100 000 : 1, when the ALD process is properly optimized.

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1 Introduction

Atomic layer deposition (ALD) is a technique that is based on sequential and self-limiting gas–solid surface reactions,^1,2 widely used for fabricating uniform thin films on flat substrates and conformal coatings in complex, high-aspect-ratio structures.^1,3 In principle, ALD can enable conformal growth on any surface, regardless of geometry, since the reactions occur by chemisorption. Over a thousand ALD chemistries have been developed, reflecting its broad application potential.^4,5 ALD is attractive for coating particles,^3,6–8 with growing applications in heterogeneous thermocatalysis,^9,10 battery electrodes,¹¹ fuel cells,¹² LED phosphors^13,14 and drug delivery systems.¹⁵ Recently, a review article overviewed ∼800 scientific articles reporting ALD on particulate materials.¹⁶

ALD is attractive for coating porous materials with expensive noble metals such as platinum, as it potentially provides precise particle size control and efficient use of precursors.¹⁷ Platinum is a critical catalyst in applications such as fuel cells,^12,18,19 where it facilitates the oxygen reduction and hydrogen oxidation reactions,²⁰ and in hydrogenation processes,^21,22 where it activates molecular hydrogen. Deposition of platinum by ALD has been demonstrated on a variety of substrates, including nonporous anodic alumina membranes^23–27 and porous nanotubes,^28–30 and trench-type high-aspect-ratio geometries.^31–34 Platinum ALD has been reported to date^4,5,16 using a handful of precursors: platinum(II) acetylacetonate [Pt(acac)₂],³⁵ dimethyl(η⁴-cyclohexa-1,5-diene)platinum [PtMe₂(η⁴-cyclohexa-1,5-diene)],³⁶ dimethyl(N,N-dimethyl-3-butene-1-amine-N)platinum (DDAP, [C₈H₁₉NPt]),^37,38 and (methylcyclopentadienyl)trimethylplatinum [MeCpPtMe₃].^39–43 Out of these, MeCpPtMe₃ is the most commonly used Pt precursor due to its stability and high volatility,^16,32 and has been used in this work.

Several experimental studies have investigated the macroscopic distribution of various ALD-deposited materials on porous substrates. Table 1 summarizes earlier work on porous spheres. While most studies^44–46 report only an eggshell-type coating, there are also reports where uniform coating (i.e., through particle coating) is seen.^47–49 Elam et al.⁴⁷ showed that progressively increasing the exposure (partial pressure × time) led to increasing penetration upto uniform coating for a Al(CH₃)₃/H₂O ALD process with a 90 s precursor exposure time in porous silica particles (aspect ratio 1667 : 1, calculated as the ratio of particle radius to average pore diameter). To date, the highest aspect ratio for which uniform coating on a porous sphere has been reported is 43 103 : 1, done using a Ru(EtCp)₂/O₂ ALD process on alumina spheres with a 180 s precursor exposure time.⁴⁸

Table 1 Summary of ALD-based metal distribution on porous spheres in the literature^a

ALD grown material	Substrate material	ALD reactants	ALD process			Average support particle diameter 2R	Average pore diameter	Aspect ratio^b	Penetration depth		References
			Temp. (°C)	Cycles (—)	Exposure time/purge/exposure time/purge (s)	(mm)	(nm)		R (μm)	(% of R)
a Authors' interpretation of the literature data.b Aspect ratio was calculated as the ratio of particle radius R to pore diameter.c This ALD process was done using a Picosun R-200 reactor with stop flow deposition and purge cycles. The stop-flow cycle had a 0.7 s precursor pulse and 9.3 s equilibrium time.^44d The metal precursor injection time was 200 s and the substrate was exposed to the injected precursor for 30 s. Oxygen precursor was injected 10 s and remained for 30 s. The purge times are not reported.^45e This support had spherical and micrometer-sized macropores that communicated only through mesopores.^46f The ALD cycle consisted of a 0.5 s pulse of Pd(hfac)2, 15 s of exposure, and a 10 s Ar purge, followed by a 1 s pulse of formalin, 15 s of exposure, and again a 15 s purge with Ar.^46g This support had interconnected randomly dispersed macropores.^46h 180 s exposure times and 540 s purge times were used.^58i Silicyle support's pore diameter was 30 nm.^58j The results are included assuming the particles were spherical, although the shape was explicitly mentioned in the article.k Exposure and purge times are not directly reported. The Pt precursor dose was on the order of 200 s.^59l Metal precursor was injection time was 180 s. Then, O2 was injected into the reactor for 10 s and the sample was subsequently exposed to O2 for 30 s. Between injections of each precursor, the reactor was purged with nitrogen. The purge times are not reported.^48m Total porosity ∼90%; three-dimensional interconnected microcellular structure with voids 10 ± 3 μm and windows 3 ± 1 μm, typical of high internal phase emulsion polymerization. The window dimensions were taken as the basis for the aspect ratio calculation.^49n The second reactant's exposure time and the purge times are not reported.^49
Al2O3	Co–Pt/TiO2	Al(CH3)3, H2O	150	30	10/80/10/80^c	0.4	29	6897 : 1	9.6	4.8%	44
NiO	Al2O3	NiCp2, O2	260	50	200 + 30/—/10 + 30/—^d	1.1	11.6	47 414 : 1	15	2.8%	45
Pd	Closed macroporous^e Al2O3	Pd(hfac)2, formalin	220	300	0.5 + 15/10/1 + 15/15^f	2.5	10	125 000 : 1	16	1.3%	46
Pd	Randomly dispersed macroporous^g Al2O3	Pd(hfac)2, formalin	220	300	0.5 + 15/10/1 + 15/15^f	2.8	11	127 273 : 1	16	1.1%	46
Al2O3	Silica gel: Silicycle S10040M	Al(CH3)3, H2O	150	9	90/180/90/180	0.1	30	1667 : 1	45	100%	47
ZnO	Silicycle S10040M or fumed silica (Ineos USA LLC)	Zn(C2H2)2, H2O	200	3	180/540/—/—^h	0.075	30^i	1250 : 1 (Silicyle)		100%	58
Pt	Silica gel^j	MeCpPtMe3, O2	325	10	—/—/—/—^k	0.03–0.075	6	6250 : 1		100%	59
RuO2	Al2O3	Ru(EtCp)2, O2	315	50	180/—/10 + 30/—^l	1	11.6	43 103 : 1		100%	48
Al2O3	Nanocomposite polymer beads^m	Al(CH3)3, H2O	150	250	10/—/—/—^n	1.5	3000	250 : 1		100%	49
TiO2	Nanocomposite polymer beads^m	TiCl4, H2O	250	500	10/—/—/—^n	1.5	3000	250 : 1		100%	49
ZnO	Nanocomposite polymer beads^m	Zn(C2H2)2, H2O	175	250	10/—/—/—^n	1.5	3000	250 : 1		100%	49

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To better understand and predict film conformality in challenging geometries, various models including analytical,⁵⁰ diffusion–reaction,^51–54 ballistic transport–reaction,^53,55 and Monte-Carlo³ models have been developed to describe ALD growth in trenches, holes, and porous materials. For example, the analytical Gordon et al. model⁵⁰ showed that the penetration depth within a hole for Knudsen diffusion conditions (i.e. Knudsen number ≫ 1, where Knudsen number is the ratio of the gas mean free path to a characteristic length scale) is proportional to the square root of the precursor exposure time.⁵⁰ Recently, a diffusion–reaction model⁵⁶ was adapted for porous particles in the shape of slabs, cylinders and spheres, where reactant transport initiates at the particle's outer surface and proceeds through a tortuous pathway toward the center, with the effective diffusion coefficient governing diffusion throughout the particle.⁵⁶ This model shows that spherical particles require less reactant exposure for full surface saturation than slabs or cylinders of the same size due to multidimensional reactant diffusion propagation along three radial directions (spheres require about one-third of the exposure required to saturate slabs).^56,57

The goal of this work is to characterize ALD growth of platinum on mesoporous alumina spheres in a fluidized bed reactor at atmospheric pressure using the commonly used (trimethyl)methylcyclopentadienylplatinum(IV) reactant. We show that systematically increasing precursor exposure leads to increasing Pt penetration depth, from egg-shell to uniform distribution; on porous spheres up to 2.5 mm diameter with an aspect ratio on the order of 100 000 : 1. These experimental results are then compared with predictions from a diffusion–reaction model for spheres.⁵⁶ The model predictions align with the experiments, assuming a low precursor sticking coefficient.

2 Methods

2.1 Materials

Porous alumina spheres from SASOL Limited were used as the ALD support. The diameters were 1.0 mm (alumina spheres 1.0/160 prod. 610110), 1.8 mm (alumina spheres 1.8/210, prod. 604130) and 2.5 mm (alumina spheres 2.5/210 prod. 608114). The reactants used were: (trimethyl)methylcyclopentadienylplatinum(IV), MeCpPtMe₃ (99%) from Strem Chemicals Inc., and synthetic air (Linde) nitrogen (N₂, 99.999%, Linde) was used as the inert carrier gas.

2.2 Platinum ALD

Platinum ALD was done at atmospheric pressure in a fluidized bed reactor with a process temperature of 110 °C. The fluidized-bed ALD reactor used in this study has been described in detail elsewhere;^60,61 a schematic is provided in the SI (Fig. S2). The process conditions were similar to those reported by Grillo et al.⁶⁰ The Pt precursor was placed in a stainless steel bubbler and was heated to 70 °C. The process consisted of the steps: MeCpPtMe₃ exposure (duration from 180 s to 1440 s), purge I (600 s), synthetic air exposure (600 s), purge II (600 s). Parameters varied during the experiments are in Table 2. The alumina spheres were mixed with glass beads of 120–150 μm diameter. Each run used 10 grams of glass beads and 0.25 grams of alumina spheres for each diameter of 1.0 mm, 1.8 mm, and 2.5 mm. Given that the particle mixing time in the fluidized bed (∼1 s) is significantly shorter than the precursor pulse time, each particle is expected to experience a uniform time-averaged concentration, promoting a homogeneous coating across the batch.^16,62,63 In the experiments conducted, a flow rate of 11 min⁻¹ (normal liter per second at 1 atm and 20 °C) was used, resulting in a fluidization velocity of approximately 3.4 cm s⁻¹. Detailed conditions for fluidization are in the SI (section S1.4).

Table 2 Varied flows used in the experimental setup (total flow was constant: 11 min⁻¹). One cycle consisted of the following steps: MeCpPtMe₃ exposure, purge I, synthetic air exposure, purge II

Step	V̇N2,purge I^a	V̇N2,bubbler^b	V̇N2,purge II^c	V̇N2,makeup^d	V̇air^e
	L min^−1	L min^−1	L min^−1	L min^−1	L min^−1
a V̇N2,purge I is the nitrogen volume flow bypassing the MeCpPtMe3 bubbler.b V̇N2,bubbler is the nitrogen volume flow through the MeCpPtMe3 bubbler.c V̇N2,purge II is the nitrogen volume flow through the air line.d V̇N2,makeup is the nitrogen makeup volume flow.e V̇air is the air volume flow.
MeCpPtMe3 exposure	0	0.4	0	0.6	0
Purge I	0.8	0	0	0.2	0
Synthetic air exposure	0	0	0	0	1
Purge II	0	0	0.8	0.2	0

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Before ALD, the glass beads for the sample mixture were washed with diluted isopropanol (∼20%), and after that, with diluted nitric acid (∼10%). Four samples of Pt/Al₂O₃ were obtained by varying the MeCpPtMe₃ pulse time (180, 360, 720, and 1440 s) while the oxidizer's pulse time was kept constant at 600 s; five cycles of ALD were done for all samples.

2.3 Nitrogen physisorption

The surface area, total pore volume, and pore size of the spheres was determined by nitrogen physisorption. The measurements were made with a Micromeritics Tristar II 3020 instrument. The specific surface area was determined using the Brunauer–Emmett–Teller (BET)⁶⁴ method. Total pore volume and the pore size distribution were determined by the Barrett–Joyner–Halenda (BJH)⁶⁵ method.

2.4 Inductively coupled plasma-optical emission spectrometry

The average metal weight loading of platinum on the porous alumina spheres was determined using a PerkinElmer Optima 8000 inductively coupled plasma-optical emission spectrometer. In the inductively coupled plasma-optical emission spectrometry (ICP-OES) analysis, each particle size fraction was analyzed separately. Approximately 30 mg of the sample was digested in a microwave for 60 minutes in a mixture of 4.5 ml of 30% hydrochloric acid and 1.5 ml of 65% nitric acid. The samples were then diluted to 50 ml with purified water before the analysis with ICP-OES 8000.

2.5 Average areal number density calculation

From experimental values of average metal weight loadings (in this work determined from ICP-OES), the average number of metal atoms per surface area of the support, referred to as areal number density c_M (nm⁻²) is calculated as^2,8Here, w_M is the weight fraction of the metal, N₀ (mol⁻¹) is Avogadro's constant, M_M (g_metal mol⁻¹) is the molar mass of the metal, S (m² g_support⁻¹) is the specific surface area of the support, m_t (g_sample) is the total mass of the sample including the amount of the deposited material, and m_s (g_support) is the mass of the original support. An example of the average areal number density calculation is in section S2.2 of the SI. Additionally, for the largest sphere size (2.5 mm), we calculated the areal number density on the coated area, based on the coated volume fraction ϕ_coat (Table S2 in the SI).

2.6 CO pulsed chemisorption

CO pulsed chemisorption was used to analyse platinum crystallite size and dispersion of ALD coated Pt/Al₂O₃ spheres of diameter 2.5 mm. An AutoChem-III 2930 tool (Micromeritics Instrument Corporation) with an external Cirrus™ 3 mass spectrometer (MS; MKS Instruments) was used.

In a U-shaped reactor tube, approximately 60 mg of sample was placed and diluted with 300 mg of silicon carbide, SiC (Thermo Scientific, 46 grit). The sample was then dried by heating to 200 °C (ramp rate: 10 °C min⁻¹) for 1 hour in a helium (He, Woikoski, 99.9995%) flow of 50 ml min⁻¹ and then cooled to 35 °C after drying. Flow was then changed from He to a gas mixture of H₂/Ar (10% H₂ in Ar, Air products, 9.99%) with a flow rate of 50 ml min⁻¹. The sample was then reduced in a gas mixture of H₂/Ar while heating to 400 °C (ramp rate: 10 °C min⁻¹). The hold time at 400 °C was 0.5 hour. After reduction, flow was changed to He to remove chemisorbed hydrogen (flow rate 50 ml min⁻¹), and after 0.5 hour, the sample was cooled to 35 °C.

Then, pulse chemisorption of CO was carried out. In total, 25 pulses of CO/He (10% CO in He, Air products, 9.998%) were dosed to the sample using a loop. The physically calibrated loop volume was 0.5185 cm³. The temperature of the loop and the equipment's lines was 110 °C. The thermal conductivity detector (TCD) and mass spectrometer, m/z = 28 (CO) signals were used to estimate the amount of CO adsorbed. Pt dispersion and platinum crystallite particle size were calculated based on the CO consumption, assuming hemispherical Pt particles and an adsorption stoichiometry of 1. The equations used to calculate metal dispersion and hemispherical crystallite size are provided in the SI (section S1.3).

2.7 Low-energy ion scattering

Low-energy ion scattering (LEIS) surface spectroscopy was performed on the inner surfaces of the samples using an IONTOF Qtac 100 low-energy ion scattering spectrometer. The purpose was to study the propagation of the ALD Pt coating from the outer surface towards the center of the porous alumina spheres. Before analysis, the spheres were mechanically cut approximately in half with scissors to obtain cross-sections, and the samples were cleaned in the ultra-high vacuum of the instrument by exposure to atomic oxygen extracted from a remote plasma source. ⁴He⁺ was used as the analysis ion (3 keV, 5.2 nA) with an acquisition time of 1200 s, scanning over an analysis area of 2.8 × 2.8 mm² using a 256 × 256 pixel raster. The resulting ion dose density was 4.8 × 10¹⁴ ions per cm². The PtO₂ surface coverage was quantified via a PtO₂ powder reference sample (Sigma Aldrich, CAS: 1314-15-4). The horizontal line scans through the center of the alumina spheres are averaged over 16 pixels in the vertical direction. Line scans are plotted with 11 pt adjacent averaging.

2.8 Diffusion–reaction model

A diffusion–reaction model was used to compare the saturation profiles obtained experimentally with those predicted by simulations. The model equations are based on the diffusion–reaction model for porous spheres by Heikkinen et al.⁵⁶ and the model by Ylilammi et al.^51,52 The main equations are described in this section, and some additional equations are shown in the SI (section S1.1). The set of equations was solved using a Python-based script.⁶⁶ This model assumes uniform porosity, tortuosity, and pore size across the entire particle. It takes the precursor partial pressure at the entrance p_A0 (related to the reactant number density n_A0 through the ideal gas equation) as a constant value, and describes reactant transport through diffusion from the outer surface towards the core of a sphere of radius R:and rate of change of surface coverage:Here, n_A (m⁻³) is volumetric reactant A number density (related to the partial pressure of the reactant p_A through the ideal gas equation), D_eff (m² s⁻¹) is the effective diffusion coefficient, θ (—) is surface coverage, s̅ (—) is the ratio of specific surface area (S) (m² g⁻¹), and pore volume (V_pore) (m³ g⁻¹), v̅_A (m s⁻¹) is the mean thermal velocity, q (m⁻²) is the adsorption capacity, c (—) is the sticking coefficient, and P_d (s⁻¹) is the desorption probability.

The effective diffusion coefficient D_eff in eqn (2) is calculated as^56,67

Here, D_A (m² s⁻¹) is the molecular diffusion coefficient, which describes gas phase collisions (molecule–molecule interactions), and the D_Kn (m² s⁻¹) is the Knudsen diffusion coefficient which dominates at low pressures and describes molecule–wall interactions. Porosity, ε (unitless) of the spheres is calculated using the relation:⁶⁸In eqn (5), V_pore (cm³ g⁻¹) is the pore volume, and ρ_s (g cm⁻³) is the skeletal density of the support material γ-alumina, 3.6 g cm⁻³.⁶⁹ Tortuosity τ (unitless) is calculated from the Beeckman relationship for heterogeneous catalysts as follows:^70,71

The weight percentage of the metal for a partly coated particle can be calculated by

where M_M (g mol⁻¹) is the molar mass of the metal, S (m² g⁻¹) is the specific surface area of the support, m_S (g) is the mass of the support, N₀ is Avogadro's constant, m_t (g) is the total particle mass, and ϕ_coat (—) is the coated volume fraction of the porous sphere. The ϕ_coat is obtained from integration of the simulated saturation profile θ(t_final, r) as a function of 1 − (r/R)3. For a fully coated porous sphere, ϕ_coat is one. In the case of a step-like saturation profile with a penetration depth d, the coated volume fraction is equivalent to^6,72where V_d is the coated volume, and V_R is the total internal volume of the sphere. A simplified figure showing the coated volume fraction is in the SI (Fig. S1).

3 Results

3.1 Support characterization

Support surface characteristics were studied by nitrogen physisorption. The average specific surface area, pore volume, and pore diameter values for the spheres of different diameters and the resulting aspect ratios are in Table 3. All alumina supports were mesoporous with an average pore diameter from 9.9 to 11.7 nm and a narrow pore size distribution (Figs. S3 and S4 in the SI).

Table 3 Nitrogen physisorption results: BET surface area, total pore volume, median pore radius. Calculated aspect ratio,^a porosity,^b and tortuosity^c values are also included

Sphere diameter (mm)	BET surface area, S (m^2 g^−1)	Pore volume, Vpore (cm^3 g^−1)	Average pore diameter, dpore (nm)	Aspect ratio^a (—)	Porosity^b ε (—)	Tortuosity^c τ (—)
a Aspect ratio is calculated as the ratio of particle radius R to average pore diameter dpore.b Porosity ε (—) is calculated using pore volume Vpore (cm^3 g^−1) and the skeletal density of γ-alumina, ρS (3.6 g cm^−3)^69 described in eqn (5).c Tortuosity τ is calculated from the porosity using eqn (6).
1.0	158	0.49	11.7	42 700 : 1	0.64	1.49
1.8	202	0.55	10.3	87 300 : 1	0.67	1.47
2.5	206	0.54	9.9	126 000 : 1	0.66	1.48

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3.2 Quantification of metal weight loading: ICP-OES

After Pt ALD using reactants MeCpPtMe₃ and synthetic air, the average metal weight loading on spherical alumina was determined by ICP-OES. Results for weight loadings on spheres of different diameters are presented in Fig. 1a and platinum loading in terms of the average areal number density, i.e., metal atoms per support surface area, is in Fig. 1b. For spheres of all sizes, the average platinum weight loading increased with exposure time, though a slower increase was seen for the longest exposure time (1440 s). Up to 4.05 Pt wt% metal loading was observed, for sphere with a diameter of 1.8 mm (surface area = 202 m² g⁻¹) at the longest exposure time (1440 s). As seen in Fig. 1b, the maximum average areal number density after five cycles of about 0.71 Pt atoms per nm² was observed for the sphere size 1.0 mm with the highest exposure time (1440 s), with all sphere sizes showing a close value.

Fig. 1 (a) Platinum loading (wt%) and (b) average platinum areal number density (atoms per nm²) as a function of the MeCpPtMe₃ exposure time (exposure time per cycle), based on ICP-OES and surface area of the support as in Table 3. Five ALD cycles were done at a reaction temperature of 110 °C, in a fluidized bed at atmospheric pressure. Numerical values corresponding to this figure are in Table S1 of the SI.

3.3 CO pulse chemisorption

Results from CO pulse chemisorption are presented in Table 4. As the ALD exposure time of the MeCpPtMe₃ reactant increased from 180 s to 1440 s, the cumulative quantity of CO chemisorbed on the surface initially increased and then settled. The CO adsorption capacity of the samples varied from 65 μmol g⁻¹ for the shortest exposure to 118 μmol g⁻¹ for the longest exposure. For the shortest exposure time, the number of CO molecules chemisorbed exceeds the number of platinum atoms on the surface (by ICP-OES), likely indicating an adsorption mode where one platinum atom can bind to more than one CO molecule. The dispersion calculation assumes the bonding of one CO per platinum atom (SI, Section S1.3). While for the shortest exposure time, the dispersion cannot be calculated with this assumption (it is very high, presumably ∼100%), for the other exposure times, the calculated dispersion varies from 84% to 41%, and dispersion decreases with exposure time. The particle size calculated assuming hemispherical particles at the same time increases from 1.3 to 2.8 nm.

Table 4 Characterization results from CO pulse chemisorption (TCD measurements) done on Pt-coated alumina spheres of diameter 2.5 mm^a

MeCpPtMe3 exposure time (s)	Cumulative CO quantity (cm^3) (STP)	Cumulative CO quantity (μmol g^−1)	Cumulative quantity (CO molecules per nm^2)	ICP-OES weight loading (%) for 2.5 mm sphere	Average areal number density (Pt atoms per nm^2)	Metal dispersion	Crystallite size (hemisphere) (nm)
a Mass spectrometer results are in Table S3 of the SI.b The cumulative quantity of CO chemisorbed exceeded the number of Pt atoms, and dispersion can be assumed to be approximately 100%. The stoichiometry of adsorption may differ from the stoichiometry value of one, assumed in the dispersion and crystallite size calculations.
180	1.46	65.2	0.19	0.94	0.14	—^b	—^b
360	1.59	70.7	0.21	1.64	0.25	84.1%	1.3
720	2.67	119.1	0.35	2.85	0.44	81.5%	1.4
1440	2.65	118.4	0.35	4.00	0.62	57.7%	2.0

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3.4 Surface chemical composition analysis: LEIS

LEIS reveals the presence of Pt, Al and O on the surface (example spectra in the SI, Fig. S5). Fig. 2 shows distribution maps of PtO₂ by LEIS and Fig. 3 gives the quantified results for the PtO₂ signal through the particle. Overall, the Pt penetration increased with increasing exposure time. An egg-shell coating is seen in samples with lower exposure times of 180, 360 and 720 s. For the sample with the longest exposure time of 1440 s, the coating appeared to be macroscopically uniform throughout the sample. The corresponding surface fraction of PtO₂ was approximately 5%. Considering that an average monolayer of PtO₂ has about 9.9 Pt atoms per nm² (calculated from the bulk density ρ = 11.8 g cm⁻³, and molar mass = 227.08 g mol⁻¹ of PtO₂), the LEIS results correspond to a surface areal number density of about 0.5 Pt atoms per nm².

Fig. 2 Low-energy ion scattering (LEIS) qualitative maps showing platinum surface across cross-sections of 2.5 mm (diameter) ALD coated Pt/Al₂O₃ spheres. Brighter areas indicate higher surface coverage of platinum. The platinum reactant exposure time per cycle was (a) 180 s, (b) 360 s, (c) 720 s, and (d) 1440 s.

Fig. 3 Quantified line scans of the LEIS images in Fig. 2 for ALD coated Pt/Al₂O₃ spheres (diameter: 2.5 mm). The horizontal line scans through the center of the alumina spheres are averaged over 16 pixels in the vertical direction and are plotted with 11-point adjacent averaging.

3.5 Diffusion–reaction simulations

Diffusion–reaction simulations were made to support the experimental study. The modeling was made assuming ideal ALD, i.e., saturating and irreversible adsorption steps, although the model would allow reversibility as well. Realistic parameters were chosen that are related to the ALD process conditions; the parameters are listed in the caption of Fig. 4. To have a resemblance between simulations and experiments, the partial pressure of the platinum reactant and the sticking coefficient were varied (the average areal number density of platinum was assumed as 0.12 nm⁻²). To have results similar to LEIS (and X-ray photoelectron spectroscopy, see SI section S2.6) that show through-penetration (uniform macroscopic distribution) for the exposure times of 1440 s, the sticking coefficient had to be made very small, on the order of 10⁻⁹.

Fig. 4 (a) Simulated surface coverage profiles, along the radial direction from the outer surface towards the center and back, along the radius of a 2.5 mm alumina sphere. (b) Comparison of the average Pt weight loading from the model to the experiments (ICP-OES results in Fig. 1). The sticking coefficient used for the simulation was 10⁻⁹. Corresponding pressure profiles are shown in the SI (Fig. S8). Other parameters were: reactant was MeCpPtMe₃; particle diameter = 2.5 mm (R = 0.00125 m); ε = 0.66; τ = 1.5; d_pore = 9.9 nm; S = 206 m² g⁻¹; V_pore = 5.4 × 10⁻⁷ m³ g⁻¹; ρ_S = 3600 kg m⁻³; q = 0.12 nm⁻²; P_d = 0 s⁻¹; T = 383 K; p_A0 = 340 Pa; p_I = 99 660 Pa; the molar mass of reactant M_A: 0.319 kg mol⁻¹; the molar mass of inert M_I: 0.028 kg mol⁻¹; d_A = 8.13 × 10⁻¹⁰ m; d_I = 3.74 × 10⁻¹⁰ m; M_M = 0.195 kg mol⁻¹. The calculated Kn number was ∼1.4 (transition region) and the Thiele modulus was ∼12 (diffusion-limited process), see SI section S1.2.

4 Discussion

4.1 Comparison of Pt loading to literature values

The Pt metal loading observed in this study is consistent with values for platinum reported in the literature for ALD on particulate materials. To enable comparison with literature, the average areal number density was calculated from the reported values of Pt weight loading. In this work, the weight loading for the longest exposure time (1440 s) ranged from 3.5 wt% for the 1.0 mm sphere to 4 wt% for the 2.5 mm sphere (Fig. 1). These weight loadings translate to growth per cycle (GPC) in terms of average areal number density of 0.71 and 0.62 atoms per nm² after five cycles, or ∼0.14 and 0.12 Pt atoms per nm² per cycle for the sphere sizes 1.0 and 2.5 mm, respectively (Table S1). The GPC of 0.14 and 0.12 Pt atoms per nm² corresponds to approximately 1% of an average Pt monolayer, calculated from the bulk density and mass of Pt (see eqn (16) in ref. 73, ρ = 21.45 g cm⁻³, areal number density of an average platinum monolayer c^ml_M ≈ 16.4 atoms per nm²).

The values of average areal number density estimated from the literature data vary over a broad range. For example, with MeCpPtMe₃ on mesoporous silica gel at 325 °C, the Pt loading corresponds to an average areal number density of 0.03 atoms per nm² per cycle (over three cycles).⁵⁹ For another study with MeCpPtMe₃ on graphene nanoplatelets at 100 °C the average areal number density corresponds to 0.13 atoms per nm² per cycle (over 10 cycles)⁷⁴ and on carbon black at 300 °C corresponds to 0.16 atoms per nm² per cycle (over 15 cycles).⁷⁵ On other types of carbon supports, even higher average areal number density values were also observed. For example in the case of MeCpPtMe₃ on multi-walled carbon nanotubes^30,76 at 300 °C (one cycle), average areal number density corresponds to 0.49 atoms per nm² and on carbon (Vulcan XC72R) at 300 °C for fuel cells,⁷⁷ average areal number density corresponds to 2.2 atoms per nm² per cycle (over five cycles). Overall, the literature range for Pt average areal number density is broad, from 0.03 (ref. 59) up to 2.2 (ref. 77) atoms per nm² per cycle. The average areal number density results of this work, values between 0.14 to 0.12 Pt atoms per nm² per cycle fall well within the range observed in previous literature.

4.2 Choice of a low sticking coefficient for simulations

For simulations, a resemblance with experimental saturation profile data can be achieved only with the selection of a low sticking coefficient. In this case, we used a sticking coefficient of 10⁻⁹ which is lower than most sticking coefficient values reported previously for metal ALD.^3,78 With higher sticking coefficients of the order of 10⁻⁶, and 10⁻³, a step-like saturation profile was seen, with an abrupt adsorption front (SI Fig. S9). The low sticking coefficient may be linked to the nucleation delay reported for MePtCpMe₃/O₂ in earlier studies.^79–81 Also, the ALD temperature in our study, 110 °C, which was lower than that reported in most of the literature for Pt ALD on particulate materials, which typically used ∼300 °C^{30,75–77,82,83} and the low temperature may be linked to the low sticking coefficient. As shown previously,^74,84 using a low deposition temperature is possible because operating at atmospheric pressure allows a higher partial pressure of O₂, enabling the process to proceed at a lower temperature. This temperature was chosen for the ALD process because lower process temperatures typically lead to a narrower particle size distribution and more stable Pt catalysts.⁷⁴

4.3 Pt particle size

With increasing ALD exposure time of MeCpMePt₃, there was an increase in Pt crystallite size and a decrease in dispersion seen from the CO chemisorption results (Table 4). The increase in particle size may be related to a slow increase of Pt uptake with longer exposure time (Table S2 in the SI).^79,85 This increasing Pt particle size might also be explained by Ostwald particle ripening phenomena⁸⁶ and/or island aggregation.⁸⁷ Coalescence-driven growth of Pt has been reported elsewhere, where particle size evolution was largely governed by surface migration and diffusion-driven coalescence rather than precursor adsorption alone.^79,88

4.4 Comparison to state of the art mesoporous particle coating by ALD

The results of this work seem to indicate that the platinium reactant does not thermally decompose at the used ALD reaction temperature (110 °C). It was previously shown through Monte Carlo simulations³ that for processes that in addition to ideal ALD include a continuous CVD-type decomposition component, thermal decomposition would be expected to manifest itself through a higher platinum content in the outer areas of the particles. The absence of such a concentration gradient points to the absence of thermal decomposition.

In this work, a uniform Pt distribution was obtained for porous spheres with an AR of 126 000 : 1 (AR, taken as the ratio of the particle radius to the pore diameter). Previously, to the authors' knowledge on porous spheres (see Table 1), the highest previously reported AR was 43 103 : 1 (ref. 48) for which a uniform distribution by noble metal ALD (Ru(EtCp)₂/O₂ process) was seen. For Pt, uniform coating on mesoporous silica gel particles (size: 30–75 μm) was shown up to an AR of 6250 : 1 using a MeCpPtMe₃/O₂ process in a fluidized bed reactor operated at vacuum conditions.⁵⁹ In the case of a Pd(hfac)₂/formalin process on porous alumina spheres with extremely high aspect ratios (∼125 000 : 1), only a thin egg-shell type coating was demonstrated.⁴⁶

Other studies have also demonstrated uniform distribution in porous high aspect ratio materials with different shapes than spheres. On silica aerogel monolith slabs, the penetration depth increased with increasing exposure time and uniform coating was seen for AR 60 000 : 1.^89,90 On mesoporous alumina (size: 125–300 μm) with AR up to 12 500 : 1, a TiO₂ overcoat extending through the alumina support was observed (Ti[OCH(CH₃)₂]₄/H₂O process) with some additional titanium around the edges of the particles,⁹¹ which may suggest the presence of some CVD type decomposition in addition to ALD.³

In conclusion, while uniform ALD coatings on mesoporous supports have been previously reported before under specific ALD process conditions, the present work extends such uniformity to significantly higher aspect ratios, ca. 125 000 : 1.

5 Conclusion

In this work, ALD of Pt on mesoporous alumina spheres was shown using (methylcyclopentadienyl)trimethyl platinum [MeCpPtMe₃] and synthetic air at 110 °C in a fluidized bed reactor operated at atmospheric pressure. Macroscopic distribution of platinum, analyzed by LEIS, varied from egg-shell to uniform, depending on the exposure time. Uniform distribution could be obtained even on spheres of 2.5 mm diameter, with an aspect ratio of ca. 125 000 : 1 (ratio of particle radius to average pore diameter). For the longest exposure time (2880 s) and five ALD cycles, a Pt weight loading on the order of 3.5 to 4 wt% was obtained, giving an average GPC, expressed as average areal number density, of 0.14 to 0.12 Pt atoms per nm².

A diffusion–reaction model for porous spheres⁵⁶ was used to simulate Pt distribution. In order to make the modeling results resemble the experimental results, a very low sticking coefficient (on the order of 10⁻⁹) had to be assumed. This is likely in line with the strong nucleation delay observed for this process. The diffusion–reaction simulations, based on the ideal ALD assumption (i.e., saturating, irreversible reactions) were able to reproduce the main features of the process.

Author contributions

Christine Gonsalves: data curation, formal analysis, investigation, methodology, validation, visualization, writing – original draft, writing – review and editing. Jänis Järvilehto: conceptualization, data curation, formal analysis, investigation, methodology, validation, visualization, writing – original draft, writing – review and editing. Saeed Saedy: conceptualization, data curation, investigation, methodology, validation, writing – review and editing. Jorge A. Velasco: investigation, methodology, software, supervision, writing – original draft, writing – review and editing. Philipp Brüner: data curation, investigation, visualization, writing – original draft, writing – review and editing. Thomas Grehl: data curation, supervision. Niko Heikkinen: investigation, software, validation. Juha Lehtonen: software, supervision. Ruud van Ommen: conceptualization, methodology, resources, supervision, writing – review and editing. Riikka Puurunen: conceptualization, funding acquisition, methodology, project administration, resources, supervision, writing – review and editing.

Conflicts of interest

There are no conflicts of interest to declare.

List of symbols

c	Sticking coefficient (—)
cM	Areal number density (nm^−2)
DA	Molecular diffusion coefficient (m^2 s^−1)
Deff	Effective diffusion coefficient (m^2 s^−1)
DKn	Knudsen diffusion coefficient (m^2 s^−1)
dA	Hard-sphere diameter of molecule A (m)
dI	Hard-sphere diameter of the inert gas molecule (m)
dpore	Pore diameter of the support (m)
ε	Porosity (—)
ϕcoat	Coated volume fraction (Vd/VR) of the porous sphere (—)
Kn	Knudsen number
ms	Mass of the support (g)
mt	Total mass of the sample (g)
MM	Molar mass of the metal (g mol^−1)
MA	Molar mass of reactant A (kg mol^−1)
MI	Molar mass of inert gas I (kg mol^−1)
n	Stoichiometric factor (number of CO molecules adsorbed per surface metal atom) (—)
nA	Volumetric reactant A number density (m^−3)
N0	Avogadro's constant (mol^−1)
Pd	Desorption probability (s^−1)
q	Adsorption capacity of metal M atoms in the ALD growth of film of the MyZx material (nm^−2) (i.e., GPC expressed as areal number density)
ρS	Skeletal density of the support, here γ-alumina 3.6 g cm^−3 (ref. 69)
S	Specific surface area of the support (m^2 g^−1)
s̄	Ratio of surface area (S) and pore volume (—)(Vpore)
τ	Tortuosity (—)
θ	Surface coverage (—)
t	Time (s)
T	Temperature (K)
v̄A	Thermal velocity of molecule A (m s^−1)
Vd	Coated volume of the sphere (cm^3 g^−1)
Vpore	Pore volume (cm^3 g^−1)
VR	Total volume of a sphere (cm^3 g^−1)
wM	Weight fraction of the metal (%)

Data availability

The simulation code used for the diffusion–reaction model of porous spheres will be made publicly available as open research software on GitHub (https://github.com/Aalto-Puurunen/ALD_porous-sphere_JV). Data will be made available upon reasonable request.

Supplementary information (SI) is available. See DOI: https://doi.org/10.1039/d5lf00395d.

Acknowledgements

The authors thank Dirk Niemeyer (SASOL Ltd.) for providing the alumina supports. C. G. thanks Reetta Karinen for fruitful discussions and feedback. This work was financially supported by the Research Council of Finland (former Academy of Finland) ALDI consortium, decision no. 331082; by the Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters; by the Business Finland project Forest CUMP (Dnro 2158/31/2022); and by the Business Finland project e-Fuel (Dnro 43287/31/2020). C. G. thanks the Walter Ahlström foundation for an encouragement grant. Computational resources were provided by the Aalto Science-IT services, and CSC – IT Center for Science, Finland.

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