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ACS Nano
ACS Nano
nn
ancac3
ACS Nano
1936-0851
1936-086X
American Chemical Society

39235302
10.1021/acsnano.4c05807
Article
Solvent Dependence of Ionic Liquid-Based Pt Nanoparticle Synthesis: Machine Learning-Aided In-Line Monitoring in a Flow Reactor
https://orcid.org/0000-0003-4303-7519
Pan Bin †⊥
https://orcid.org/0000-0003-4882-2807
Madani Majed S. #†⊥
https://orcid.org/0000-0002-8351-1011
Forsberg Allison P. ‡
https://orcid.org/0000-0002-7781-5596
Brutchey Richard L. *‡
https://orcid.org/0000-0002-1786-2614
Malmstadt Noah *†‡§∥
† Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 925 Bloom Walk, Los Angeles, California 90089-1211, United States
‡ Department of Chemistry, University of Southern California, 840 Downey Way, Los Angeles, California 90089-0744, United States
§ Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, California 90089-0260, United States
∥ USC Norris Comprehensive Cancer Center, University of Southern California, 1441 Eastlake Ave, Los Angeles, California 90033, United States
# Department of Chemical and Materials Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
* Email: brutchey@usc.edu.
* Email: malmstad@usc.edu.
05 09 2024
17 09 2024
18 37 2554225551
01 05 2024
22 08 2024
20 08 2024
© 2024 American Chemical Society
2024
American Chemical Society
https://creativecommons.org/licenses/by-nc-nd/4.0/ Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Colloidal platinum nanoparticles (Pt NPs) possess a myriad of technologically relevant applications. A potentially sustainable route to synthesize Pt NPs is via polyol reduction in ionic liquid (IL) solvents; however, the development of this synthetic method is limited by the fact that reaction kinetics have not been investigated. In-line analysis in a flow reactor is an appealing approach to obtain such kinetic data; unfortunately, the optical featurelessness of Pt NPs in the visible spectrum complicates the direct analysis of flow chemistry products via ultraviolet–visible (UV–vis) spectrophotometry. Here, we report a machine learning (ML)-based approach to analyze in-line UV–vis spectrophotometric data to determine Pt NP product concentrations. Using a benchtop flow reactor with ML-interpreted in-line analysis, we were able to investigate NP yield as a function of residence time for two IL solvents: 1-butyl-1-methylpyrrolidinium triflate (BMPYRR-OTf) and 1-butyl-2-methylpyridinium triflate (BMPY-OTf). While these solvents are structurally similar, the polyol reduction shows radically different yields of Pt NPs depending on which solvent is used. The approach presented here will help develop an understanding of how the subtle differences in the molecular structures of these solvents lead to distinct reaction behavior. The accuracy of the ML prediction was validated by particle size analysis and the error was found to be as low as 4%. This approach is generalizable and has the potential to provide information on various reaction outcomes stemming from solvent effects, for example, differential yields, orders of reaction, rate coefficients, NP sizes, etc.

platinum nanoparticles
polyol reduction
ionic liquids
flow chemistry
machine learning
spectrophotometry
reaction kinetics
National Renewable Energy Laboratory 10.13039/100006233 DE-AC36-08GO28308 King Abdulaziz University 10.13039/501100004054 NA document-id-old-9nn4c05807
document-id-new-14nn4c05807
ccc-price
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pmcIntroduction

Platinum nanoparticles (Pt NPs) have garnered attention because of their versatile applications, which include optoelectronics,1 catalysis,2 fuel cells,3 and diagnostics.4 Synthetic routes to colloidal Pt NPs via reduction of Pt(II) salts can be classified based on the reducing agent or process used, including ascorbic acid,5 citric acid,6 ethylene glycol and other polyols,7 and various biosyntheses.8,9 Among these strategies, polyol reduction stands out as a commonly used technique for fabricating not only Pt NPs but also a wide range of other metal NPs.10−12 More recently, researchers have paired polyol reductions with ionic liquid (IL) solvents in colloidal metal NP syntheses.13,14 IL solvents lead to high nucleation rates due to their low interfacial tension,15 and they electrostatically stabilize colloidal NPs due to their high dielectric constant and protective solvation layers formed at the particle surface.16,17 ILs can also be regarded as sustainable solvents with nonflammability, high chemical and thermal stability, and recyclability.18−20 While polyol reductions of metal salts are well-understood routes toward synthesizing colloidal NPs, the role that solvents can play in directing these reactions is not fully understood; this is particularly the case for IL solvents, which can play multifaceted roles in shaping reaction mechanism. Gaining insight requires analytical characterization techniques to explore precursor conversion and nucleation and growth kinetics.21

A typical analytical technique to study the kinetics of metal NP syntheses is ultraviolet–visible (UV–vis) spectrophotometry; UV–vis spectroscopic monitoring of extinction spectra of the ongoing reaction suspensions offers a rapid method for kinetic analysis for a variety of metal NPs (Au, Ag, Cu, etc.),6,22−26 as extinction is the sum of absorption and scattering of incident light by NPs and other reaction components.26 Previous studies have relied on the measurement of the absorption of NP spectral signatures that have known extinction coefficients.22,23,27 Unlike some metal NPs (e.g., Au and Ag) that possess notable absorption bands in the visible range arising from a surface plasmon resonance, Pt NPs do not possess a defined absorption signature in this spectral range.28 The surface plasmon resonance of Pt NPs is in the far-mid UV (220 nm) where it overlaps with the strong absorption features of organic solvents.29 This entangled absorption spectrum has so far restricted spectrophotometric kinetic studies of metal NPs that lack a distinct spectral signature in the visible range.

Other analytical methods used to follow the kinetics of precursor conversion, nucleation, and growth include small-angle X-ray scattering (SAXS) and in situ transmission electron microscopy (TEM). In situ SAXS can provide quantitative information about NP nucleation and growth;30−32 however, challenges arise when investigating IL solvents with SAXS, primarily due to the high attenuation coefficient of ILs that often contain elements like fluorine and sulfur with high absorption cross sections that significantly reduce X-ray transmission at low energies.33 This necessitates the use of synchrotron radiation for effective SAXS analysis. In situ TEM enables the direct observation of the dynamic formation of colloidal metal NPs,34 yet the kinetic results could be impacted by the electron beam dose rate and the liquid cell substrate.35

To measure reaction kinetics, the evolution of product concentrations must be measured as a function of reaction trajectory. When using optical spectroscopy to measure the concentration of reaction species, the measured optical density is influenced not only by molecular absorbance, but also by scattering from NPs and changes in the refractive index of the reaction medium.36 Therefore, appropriate analytical methods are required to deconvolute the nonlinear relationship between optical density and concentration in order to deliver quantitative results.37 Machine learning (ML) methods have been increasingly applied to problems in chemistry,38−42 especially for the interpretation of complex optical spectral data.43−45 Among various ML approaches, artificial neural networks (ANNs) and their variants (e.g., convolutional neural networks, or CNNs) have the proven ability to extract quantitative data from complex, unprocessed spectra, as the ML algorithms can be trained on a confined data set, identify subtle spectral information, and make predictions from unknown samples.46−48 For example, Chen and co-workers constructed a one-dimensional CNN trained on the absorption spectra of 454 samples of various molecular weight gold nanocluster mixtures; the trained model was able to predict compositions with high accuracies and a mean absolute error as low as 0.0053.46 Principal component analysis (PCA) is another useful ML data processing tool for dimensionality reduction in spectral data. PCA identifies a small number of new variables from a high-dimensional data set, thereby extracting information from the raw spectrum with normally more than 1000 data points.49,50 In this work, we use PCA to identify key experimental determinants of spectral structure and guide the construction of a training set for a neural network. PCA is only used in the screening of the experimental space and is absent in the ML model training and validation; that is, the ML training sets consist of the original full spectra.

Herein, we study the kinetics of IL-based Pt NP polyol reduction reactions in a continuous-flow process with the aid of ML algorithms that interpret the complex and largely featureless optical spectra of the reaction mixtures captured in-line. The ML models are trained on 129 sample mixtures as the training set, determined by design of experiments (DoE) that systematically samples the experimental space of the Pt NP synthesis. Two typical IL solvents (Figure 1) suitable for colloidal Pt NP synthesis were evaluated (i.e., 1-butyl-1-methylpyrrolidinium triflate (BMPYRR-OTf) and 1-butyl-2-methylpyridinium triflate (BMPY-OTf)). These solvents were chosen following an earlier study that compared the yield of Pt NPs across a panel of IL solvents.51 Of this panel, BMPYRR-OTf as a solvent led to a yield of 94% while BMPY-OTf led to a yield of 10%. This striking contrast in the context of the molecular similarity between the two solvents motivates the present study. This work not only presents an ML-assisted method to analyze the optical spectra of reactions that lack a clear spectral signature to quantify concentrations and track kinetics, but also demonstrates the use of this method in the kinetic study of IL solvent-based Pt NP syntheses. As this study focuses on the effects of altering the solvent cation, other reaction parameters (e.g., temperature, capping agent, precursor concentration) were held constant across experiments.

Figure 1 Structures of BMPYRR-OTf and BMPY-OTf.

Results and Discussion

The colloidal Pt NP polyol reduction was carried out in an automated, continuous-flow process (Figure 2). Reagents were introduced to the reactor by syringe pumps, and different volumetric/molar ratios were achieved by controlling the volumetric flow rates of the infusions. The IL solvents have high viscosities (i.e., 148 cP for BMPYRR-OTf and 240 cP for BMPY-OTf at 25 °C,52,53 compared to 16 cP for ethylene glycol at 20 °C),54 which slows their diffusional mixing in microchannels. As a result, micromixers were applied to ensure thorough mixing at room temperature, including both a lamination mixer and a serpentine channel with wavy walls (“M” in Figure 2). The former achieves reduction of mass transfer distance by interdigitally splitting and recombining the flow,55 while the latter strengthens convective mixing by creating a velocity profile perpendicular to the wall.20 Complete mixing was verified experimentally (see Supporting Information). After homogenization of the reagents, the reaction mixture flowed first into a heated reactor and then through a cooled channel for thermal quenching (at 0 °C). After quenching the mixture flowed through a glass capillary flanked by optical fibers linked to a Xenon arc lamp and a UV–vis spectrophotometer. The resulting spectral data for each sample is the average of 30 spectra captured in-line consecutively to account for the potential fluctuation of the signal induced by flow disturbances (e.g., bubbles). For training data on known concentrations of products, the heated reactor and cooled channel block were bypassed.

Figure 2 Schematic of the continuous-flow process. Key: J = junction mixer; M = micromixers; R = reactor with temperature control; Q = thermoelectric quencher; D = UV–vis spectrometric detector. The number of syringe pumps is determined by types of process, and all infusion streams first meet at the junction mixer for a primary contact (black dots and black dashed line). Micromixers contain a lamination mixer and a wavy-wall serpentine channel mixer. Copper plate reactors accommodating tubing in different lengths can be assigned. The detector unit comprises of a glass capillary covered by a black block accommodating the optical cables to a light source and a spectrometer. Syringe pumps and spectrometry are controlled by custom-written Python scripts in silico (dark gray dotted line). For nonreaction processes, the stream flows from the micromixers directly to the detector, with reactor and quencher absent (gray dashed box). Appropriate liquid streams were filled in the tubing for illustrative purposes in the photograph.

The expected components of the postreaction mixture include unreacted ethylene glycol (reducing agent) and K2PtCl4 (Pt(II) precursor), poly(vinylpyrrolidone) (PVP, MW = 55,000) (capping agent), IL (solvent), Pt NPs (reduced products), glycol oxidation products,56 and other unspecified byproducts, with the first five components being the main factors determining the optical properties of the reaction mixture. To identify the significance of each component and their contributions to the transmittance intensity, a screening test using PCA and DoE was performed initially. Five streams were introduced into the flow process simultaneously: (1) BMPYRR-OTf (IL), (2) ethylene glycol, (3) PVP in IL, (4) K2PtCl4 in ethylene glycol, and (5) presynthesized colloidal Pt NPs in a mixture of IL and ethylene glycol (v/v 3:1). Presynthesized Pt NPs were obtained in batch synthesis under analogous conditions to the flow synthesis (methods detailed in the Experimental Procedures section and TEM analysis of NPs included in the Supporting Information). Different molar ratios of the components were achieved by controlling the ratios of the infusion volumetric flow rates. A two-level full factorial design of volumetric flow rates of the five components was generated in JMP Pro 16 and executed (full design table available in the Supporting Information, Table S1). Each observed spectrum corresponds to the specific composition of the mixture, containing 678 data points between 420–650 nm. The spectral data were dimensionally reduced by PCA with three principal components (PCs); that is, 678 dimensions were reduced to three dimensions (PC1, PC2 and PC3) perpendicular to each other. PC1, PC2 and PC3 represent the most, second, and third most variations in the original spectral data. A new dependent variable (amplitude) is generated for each independent variable PC1, PC2 and PC3. While the PCs capture the variation in the spectral data and the underlying statistical information, they have no physical correspondence to the spectra themselves. Table 1 lists the variance explained of each PC, showing a cumulative explained variance of over 99.9% output by the three PCs. Details for all PCs for the 31 runs can be found in Table S3. A Pareto chart (Figure 3) demonstrates the statistical effects of each component on the response (PCs). As shown, Pt NPs exerted the most significant effect on PC1 (>99% explained variance) with a p-value much less than the 0.05 significance level. The second most significant effect was dominated by K2PtCl4 on PC2, while the IL, ethylene glycol, and PVP concentrations had no significant effects seen on the spectral signals across any of the three PCs.

Figure 3 Pareto charts showing the effects of the reaction components on the three PCs. The vertical dashed lines correspond to -log(p-value = 0.05). A p-value lower than 0.05 indicates a statistically significant result. Ethylene glycol is abbreviated here as EG.

Table 1 Explained Variance of each PC and Cumulative Explained Variance

 	PC1	PC2	PC3	cumulative	
explained variance	99.1%	0.86%	0.03%	>99.9%	

To construct ML models to extrapolate concentration information on the Pt NPs formed in the reaction based on the raw spectral data, a training data set consisting of distinct reaction compositions was required. Based on the observations from the above-mentioned significance study, a full factorial experimental design for the infusion flow rates of each reaction component was generated. The design matrix consisted of three factors (flow rates of PVP-IL solution, K2PtCl4-ethylene glycol solution, and Pt NP-IL-ethylene glycol solution). The number of flow rates evaluated (i.e., the number of “levels” in DoE terminology) was determined via DoE to be 26 for the PVP-IL solution, three for the K2PtCl4-ethylene glycol solution, and two for the Pt NP-IL-ethylene glycol solution. Here, three levels in a factor (the infusion flow rate of the K2PtCl4 solution) represent three values (0, 50, 100 μL/min) evenly spanning the space between zero and the maximum practical flow rate (100 μL/min). Some flow rate combinations result in identical compositions, such as (16, 100, 0) and (8, 50, 0) for Pt NP solution, K2PtCl4 solution and PVP solution. In these cases, all but one of the combinations were removed from the matrix. Additional flow rate combinations were also manually added to examine the experimental space that the full factorial design did not explore. A total of 129 flow rate combinations make up a training set. The complete experimental design matrix can be found in Table S4. To account for the fact that the Pt NPs fabricated in different solvents may have distinct properties (e.g., NP size and amounts of PVP ligands on the surface),51 we proceeded with three separate training sets for the non-IL polyol reaction and the two IL solvent-based reactions (i.e., BMPYRR-OTf and BMPY-OTf). The ML architecture was trained three times: once for each solvent environment. Two widely applied ML methods were investigated: an artificial neural network (ANN) and a partial least-squares regression (PLSR).

For the ANN, spectra were divided into training and validation sets with predicted molar concentrations of Pt NP as the outputs (Figure 4). The model architecture consisted of two hidden layers with ReLU (Rectified Linear Unit) activations; the hidden layers were adjusted to balance the complexity of the model against computational efficiency, with model sizes being determined through iterative trials. For model training, k-fold cross-validation was employed to enhance robustness against overfitting and ensure model generalizability. An early stopping mechanism was implemented to halt training when the validation loss stopped decreasing, optimizing training time and preventing overfitting. Parameters were determined using grid search.

Figure 4 Flowchart of using ML methods to predict concentrations, where “conc.” refers to concentration of the Pt NPs.

The results of our k-fold cross-validation in training the BMPYRR-OTf data set are illustrated in a series of scatter plots (Figure 5), each representing one of the ten folds utilized in our model evaluation. The scatter plots depict the actual vs predicted concentrations of the Pt NP component. The consistency of the high R2 values across the training folds, all above 0.98, indicates an excellent fit of the model to the training data, capturing the underlying patterns with high fidelity. The validation R2 values, although slightly lower, are commendably robust, averaging around 0.78 with a mean squared error (MSE) of 0.0087, affirming the generalizability of the model and the capability of seizing information from the unseen data without being overly tailored to the training set or being excessively influenced by noise.57 A variance of 2.69 × 10–5 across the 10-fold cross-validation was observed, indicating a consistent model performance across different data subsets. The training results for the BMPY-OTf and neat ethylene glycol data set are available in the Supporting Information (Figures S10 and S11).

Figure 5 Scatter plots of the relationship between actual and predicted concentrations of Pt NPs across ten folds of cross-validation from ANN. Each plot denotes a separate fold, with data points representing individual predictions for training (blue) and validation (red) data sets using BMPYRR-OTf solvent. The concentration axes are dimensionless and scaled between 0 and 1, using the MinMaxScaler described in the Experimental Procedures section.

Figure 6 shows the performance of the ANN over training epochs. Consistently across the ten folds, we observed a rapid decline in training loss, indicating effective learning. The validation loss, while displaying initial fluctuations, stabilizes similarly to the training loss, demonstrating the ability of this model to generalize beyond the training set. Notably, the validation loss in each fold closely mirrors the training loss after the initial epochs, suggesting that the model is not overfitting the training data. This tight coupling between training and validation loss is indicative of a well-tuned model exhibiting high predictive reliability.58 For example, in Fold 1, 2, and 10, the validation loss descends and plateaus at values suggesting minimal discrepancy between the performance of the model on both seen and unseen data. This consistent pattern reaffirms the robustness of the model and underscores its potential efficacy. Moreover, the variability in the epoch range (170–900 epochs) for different folds indicates the adaptive nature of the early stopping mechanism in response to the differing complexities, data characteristics, and potential overfitting tendencies present in each fold.

Figure 6 Training and validation loss curves over successive epochs for each of the ten folds in the k-fold cross-validation, training loss (blue) and validation loss (red), for BMPYRR-OTf solvent data set.

The second model utilized was PLSR, a statistical method that combines features from PCA and multiple regression, which reduces the dimensionality of the data by extracting a set of orthogonal factors (latent variables) that maximize the covariance between the predictors (spectral data) and the responses (Pt NP concentrations). These latent variables are then employed in the regression model to accurately predict concentrations. The 10-fold cross-validation process yielded an average MSE of 0.013 and R2 of 0.68 (Figures S12–S14). While the models had similar R2 values, the ANN noticeably outperformed PLSR in MSE and was therefore used going forward.

To operate the continuous-flow reduction, the streams of Pt(II) precursor (K2PtCl4 in ethylene glycol) and IL solvent (PVP in either BMPYRR-OTf or BMPY-OTf) were introduced to the reactor. A conventional reaction without IL present (i.e., in neat ethylene glycol) was also investigated, with the ratio of reagents kept the same as in the IL-based reactions (K2PtCl4/ethylene glycol/PVP = 5 mg:1 mL:84 mg). Residence time was controlled by changing reactant flow rates. For short residence time (<60 s), a length of PTFE tubing was placed on a custom-machined copper plate, and for long residence time (>60 s), the PTFE tubing was placed in another larger copper plate reactor, where the temperature in both setups was controlled by a heating unit to guarantee a reaction temperature of 150 °C. Figure 7 plots the predicted Pt NP concentration vs reaction time in each solvent environment, with the insets showing the raw spectra with decreasing normalized transmittance intensities of the reaction mixture, which corresponds to an increase of Pt NP concentration with time. This behavior is also consistent with light scattering, where scattering decreases at higher wavelengths.

Figure 7 Pt NP concentrations predicted by ANN as a function of reaction time in different solvents: (a) BMPYRR-OTf, (b) BMPY-OTf, and (c) neat ethylene glycol (EG), with insets showing normalized transmittance spectra of the reaction mixtures captured in-line corresponding to the reaction times. (d) Pt NP concentrations normalized from 0 to 1 over reaction time in the given solvents and their kinetic fittings.

Production of Pt NPs in the solvent BMPYRR-OTf underwent a rapid early stage of nucleation and a brief decrease of reaction rate at ca. 30 s followed by a reacceleration after ca. 60 s, and finally reached a steady plateau at ca. 300 s (Figure 7a), while the reaction of Pt NPs in BMPY-OTf showed a steep, unitary nucleation burst and growth and was complete at ca. 200 s (Figure 7b). The neat ethylene glycol reaction (Figure 7c) achieved rapid completion in ca. 100 s. Final concentrations of Pt NPs synthesized in BMPYRR-OTf, BMPY-OTf and neat ethylene glycol were measured to be 11.6, 3.08, and 5.27, respectively in 10–6 mol/L, or in 6.99, 1.85, and 3.17, respectively in 1018 NP/L. A classic kinetic rate law of dx/dt = k(C0 – mx)n was used to model the production of Pt NPs, where x is the fractional conversion of Pt NPs (x = CPtNP,t/CPtNP,max) at t seconds (i.e., Pt NP concentrations normalized from 0 to 1), k is the rate coefficient, C0 is the initial concentration of Pt(II), m is a constant related to the stoichiometry, and n is the order of the reaction.59 Fitting results are shown in Figure 7d and Table 2. Fittings of the data for each of the three reactions all presented good performance, with each having an R2 > 0.95 (Table 2). Reactions in BMPYRR-OTf and neat ethylene glycol behaved as pseudo-first order reactions while the reaction in BMPY-OTf approximated second order behavior. The rate constants for the first-order reactions (in BMPYRR-OTf and neat ethylene glycol) are similar. This is despite the fact that the diffusivity of the reactants in neat ethylene glycol is nearly twice that as in the IL (see Table S5), suggesting that the reaction is not diffusion-limited. It is possible that the reaction could be affected by the formation of an IL ion layer on the NPs altering transport of species to the surface.17 In fact, we analyzed the Pt NPs produced in the two IL-solvent systems via Fourier-transform infrared (FT-IR) (Figures S15 and S16), and the results revealed that in contrast to Pt NPs synthesized in neat ethylene glycol, the triflate anions from the ILs were observable on the surface of the NPs. A ν(S=O) stretching band centered around 1150 cm–1 as well as a strong ν(C–F) stretching band centered at 1030 cm–1 can be associated with the presence of the triflate anion from the ILs. This points to the possible phenomenon that both ILs can form protective layers on the Pt NPs that impede the reaction with Pt monomers.

Table 2 Summary of the Kinetic Fitting

solvent	k	units of k	n	R2	
BMPYRR-OTf	3.834	s–1·(L/mol)1.024	1.024	0.9595	
BMPY-OTf	12,476	s–1·(L/mol)2.109	2.109	0.9824	
neat ethylene glycol	3.980	s–1·(L/mol)1.018	1.018	0.9633	

Using BMPYRR-OTf as the reaction solvent resulted in a higher predicted Pt NP concentration upon completion compared to the reaction that uses BMPY-OTf as the solvent (Figure 7a,b). This agrees with our previous work reporting the isolated yields of Pt NPs synthesized in batch reactions with these two ILs separately: 94% for BMPYRR-OTf vs 10% for BMPY-OTf.51 The Pt NPs synthesized in BMPY-OTf were smaller than those synthesized in BMPYRR-OTf (Table 3). In the two IL environments, the difference in yield, particle size, and observed reaction order may result from differences in how precursor ions interact with the solvent. For instance, the availability of reactive Pt(II) might be limited by electrostatic interaction of PtCl42– by the BMPY+ cations.

Table 3 Size Comparison between Calculation from the ANN Model Measurement and Observation under TEMa

solvent	Cprecursor (mg/mL)	Y (%)	Cmolar (10–6 mol/L)	Dcal (nm)	DTEM (nm)	error (%)	
BMPYRR-OTf	1.25	94	11.6	1.91	2.86	33	
BMPY-OTf	1.25	10	3.08	1.41	1.47	4	
neat ethylene glycol	5.00	99	5.27	4.02	1.94	107	
a Error points to |Dcal – DTEM|/DTEM.

To further validate our observations and test the accuracy of the prediction of the ML model, we used an analytical technique to predict the size of Pt NPs analyzed by the ANN (Dcal) and compared that to the true size of NPs obtained from TEM analysis (DTEM). Feng and co-workers have developed a relationship based on apparent optical extinction that relates the concentrations of spherical particles to their sizes601

where Dcal is the calculated mean particle diameter, a is the fcc lattice constant (0.3912 nm for Pt), M is the molar mass (195 g/mol for Pt), and Cmass and Cmolar are the mass and molar concentrations for the Pt NPs, respectively.

Cmass can be calculated from eq 2. Cmolar is the prediction output by the ANN model. As shown in Table 3 and Figure S17, while in neat ethylene glycol the Pt NP Dcal was 4.02 nm compared to 1.91 nm for DTEM, in the two IL-based reactions Dcal was close to the corresponding DTEM (errors: 33% for BMPYRR-OTf and 4% for BMPY-OTf), indicating a good performance in the accuracy of the ANN model extrapolating the concentrations of Pt NP products in the reaction mixtures, and correspondingly offering a prediction of NP size.2

where mPt is the molecular weight of Pt, Y is the yield of Pt NPs, Cprecursor is the initial precursor concentration in mass, and MK2PtCl4 is molar mass of the precursor.

Conclusions

We describe a methodological, analytical technique to quantitatively measure the conversion kinetics of a colloidal Pt NP synthesis using a polyol reduction in IL solvents. By pairing spectroscopic in-line flow reactor monitoring with machine learning methods, we bridged the gap between the featureless spectroscopic data of Pt NPs (i.e., lacking a visible surface plasmon resonance) and their corresponding concentration in the reaction mixture as a function of reaction time. Two machine learning algorithms were evaluated—an artificial neural network (ANN) and a partial least-squares regression (PLSR). The two models were trained by a spectral data set of reaction mixtures with known concentrations of chemical components. The training data set was designed based on a preliminary study using PCA and design of experiments that indicated Pt NPs exerted the most significant effect on the spectral outcomes. The well-trained ANN model was applied to measure unknown Pt NP concentrations in a continuous-flow reaction process, and the accuracy of the model resolving NP concentrations was affirmed by a size estimate verification via TEM. While the ML models served as a tool for spectroscopic analysis, they have no utility in interpreting the underlying chemistry. Observations of the change in Pt NP concentration with reaction time could be fit to a kinetic model, revealing that the two different IL solvents led to different reaction orders.

This solvent-dependent control of this reaction has implications for the design of routes for NP synthesis. This work builds on previous efforts to investigate metal NP synthesis using optical extinction spectroscopy;22,47 this approach not only offers a low-cost, laboratory-based approach for rapid study of NP syntheses, but also broadens the range for reactions devoid of clear spectral signals, thereby providing insight into the kinetics of NP formation. The use case demonstrated here is generalizable to systems with complex spectral signals that cannot be deconvolved analytically. The reaction trajectory in this work is the result of a kinetic model fitting of concentration values predicted by the ML model trained by spectral profiles of Pt NPs. In the future, the exact nature of the underlying mechanisms can be further proved by integrating in situ measurements (e.g., SAXS) that can reveal size and shape information.

Experimental Procedures

Materials

K2PtCl4 (99.99% trace metals basis), ethylene glycol (99.8%), and poly(vinylpyrrolidone) (MW = 55,000) were purchased from Sigma-Aldrich and used as received. 1-Butyl-1-methylpyrrolidinium triflate (BMPYRR-OTf, 99%) and 1-butyl-2-methylpyridinium triflate (BMPY-OTf, 99%) were purchased from IoLiTec and used as received.

Reagent Preparation

K2PtCl4 was added to ethylene glycol and stirred at room temperature for 2 h to give clear solutions of Pt(II) precursors at 5 mg/mL (volume: 5–20 mL). PVP was added to the solvent of interest (ethylene glycol, BMPYRR-OTf, or BMPY-OTf) and the mixture was stirred at 150 °C for 30 min to give a clear solution at 28.4 mg/mL (volume: 10–50 mL). Dry presynthesized Pt NPs were added to a mixture of ethylene glycol and IL (BMPYRR-OTf or BMPY-OTf) at a 1:3 volumetric ratio and stirred at room temperature for 1 h to give a homogeneous solution for algorithm training.

Synthesis and Workup of Pt NPs in Batch

In a typical procedure, 15 mL of PVP solution was added to a 250 mL round-bottom flask in an oil bath at 150 °C and was preheated for 10 min. The Pt(II) precursor solution was then hot injected into the flask with stirring for a 15 min reaction at 150 °C. The flask was then immediately placed in an ice bath to thermally quench the reaction. After quenching, the reaction solution was equally split and transferred to two 50 mL centrifuge tubes. 40 mL of acetone was added to each tube and vortex mixed to precipitate the Pt NPs. The tubes were then centrifuged (5 min, 6000 rpm) for phase separation and the supernatant was decanted and discarded. Ten mL of ethanol was added to each tube to redisperse the Pt NPs followed by adding 40 mL of hexanes and centrifuging to reprecipitate the NPs. After decanting and discarding the hexanes layer, fresh ethanol and hexanes were added again for another purification cycle. A total of three washes by ethanol and hexanes were executed. The purified Pt NPs were resuspended in ethanol to store for further use.

Fabrication of Device in Continuous-Flow Process

Micromixers, quenching unit and detection block were fabricated by stereolithography (SLA) 3D printing (printer: Asiga MAX X UV385). Drawings are available in the Supporting Information (Figures S6–S9). A clear methacrylate-based resin (GR-10, Pro3dure Medical) was used to produce the micromixers and the quenching block, and a black, nontransparent resin (FunToDo F1 +, black) was used to produce the detection block. The blocks were thoroughly washed with isopropanol and air-dried before use.

Setup of Continuous-Flow Processes

Programmable syringe pumps (Legato OED syringe pump, KD Scientific) were used to drive the reagent flows. A miniature spectrophotometer (Flame-S-UV-vis-ES, Ocean Insight) and a Xenon light source (PX-2, Ocean Insight) were employed. For screening and training processes, reagents were mixed in the micromixers before entering a glass capillary (I.D. 1.12 mm, TW150-6, WPI) through the detection block. For Pt NP syntheses, reagents upon mixing entered a PTFE tubing reactor (I.D. 1/32 in., Cole-Palmer), and were then heated by a custom-machined copper plate at the desired temperature. The reaction flow then entered the quenching unit with a homemade thermoelectric cooler (at 0 °C) and subsequently went through the as-described capillary for detection. The detection began at 1.5× the residence time (the time of flow in the reactor) from the flow entering the heated reactor. The reaction mixture was collected downstream in a flask. The flow processes were automated and controlled by custom Python scripts. Synthesized Pt NPs were washed following the above workup procedure, and stored in ethanol for further characterization. After 6 h of operation, an accumulation of dark material could be observed on the inner surface of the tubing. To clean both the reactor channel and auxiliary tubing, ethanol was constantly infused to the reactor after cooling below 70 °C until no fouling was visible. The reactor was filled with ethanol when idle.

Principal Component Analysis (PCA) on Screening Data

PCA was conducted in Python with sklearn package. Raw intensity data captured by the spectrometer were first standardized using function sklearn.preprocessing.StandardScaler(), and three principal components were chosen.

Artificial Neural Network (ANN)

The ANN architecture was designed with an input layer matching the dimensionality of the spectral data, two hidden layers comprising 510 and 220 neurons, respectively, and concluding with an output layer for Pt NP concentration estimate. The rectified linear unit (ReLU) served as the activation function, alongside the Adam optimizer for network training. Grid search was conducted to tune the number of neurons and layers in the ANN to maximize the cross-validated R2. Performance was quantified using cross-validated MSE and R2 metrics. The MSE was calculated using the predictions and the actual values from the validation set, presented in a dimensionless form based on the normalized data. The ANN model was trained on data that had been scaled in [0, 1] using the MinMaxScaler which was defined as3

where x′ is the scaled value, x is the original unscaled value, xmin and xmax are the minimal and maximal values in the data set, respectively.

Partial Least-Squares Regression (PLSR)

PLSR preprocessing involved mean-centering and variance scaling to improve model robustness. The optimal number of latent variables was determined through 10-fold cross-validation to minimize prediction errors. Model performance was evaluated using the mean-squared error (MSE) and coefficient of determination (R2).

Training with Machine Learning Models

To compare the two machine learning algorithms, PLSR and ANN, we employed k-fold cross-validation. Scikit-learn was used for the implementation of all models, ensuring a consistent and reproducible framework for our machine learning pipeline. In the k-fold cross-validation approach, the data set was partitioned into k equal-sized folds. For each iteration, one fold was retained as the validation data for model validation, and the remaining k - 1 folds were used as training data. The cross-validation process was then repeated k times, with each of the k folds used exactly once as the validation data. The results from the k folds were then averaged to produce a single estimation, providing insight into the generalization ability of the model on an independent data set. The predictive performance of each model was evaluated using the R2 value and the MSE. For each algorithm, a thorough grid search was conducted to tune hyperparameters, ensuring optimal performance. The specific hyperparameters included the number of latent variables for PLSR, and the number of neurons and layers in the ANN. These hyperparameters were fine-tuned to maximize the cross-validated R2 value. Upon completion of the cross-validation, the best-performing model based on best R2 value was selected to predict the concentration of Pt NPs during the synthesis process.

Transmission Electron Microscopy (TEM)

TEM images were acquired on a Thermo Fischer FEI TALOS F200× microscope operating at 200 kV with a single tilt holder. Each sample was prepared for microscopy by drop casting a sonicated suspension of Pt NPs onto a 400 mesh lacy carbon coated copper grid (Ted Pella, Inc.) and dried overnight under vacuum at room temperature. The average sizes of the Pt NPs were measured by hand using ImageJ a pixel counting software (N = 300).

Fourier-Transform Infrared Spectroscopy (FT-IR)

FT-IR spectra were acquired on a Bruker Vertex 80 spectrophotometer using 16 scans, 4 cm–1 resolution, 4000–400 cm–1 spectral range and transmittance as the operational parameters. KBr was oven-dried then finely ground using an agate mortar and pestle. Dried Pt NP samples (4 mg) were added to the finely ground KBr (200 mg), mixed, and then pressed manually into a 9 mm diameter disk using a hand operated screw press. Before introduction of the sample into the spectrophotometer, a background spectrum was performed on a similarly prepared neat KBr pellet.

Determination of Pt NP Yield

The isolated yield of Pt NPs was gravimetrically calculated via TGA utilizing a TGA Q50 instrument. To determine the yield of pure Pt, a sample of ca. 10 mg of isolated and worked up and dried Pt NP powder was added to an alumina pan and heated to 700 °C under flowing air at a heating rate of 10 °C/min. The residual mass of Pt left over after complete ligand (PVP and IL) decomposition was utilized to back calculate the yield of Pt for the entire reaction. This was carried out for Pt NPs synthesized in all three reaction solvents.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsnano.4c05807.Additional design details for continuous-flow process, details of model training, FT-IR spectra, and TEM images (PDF)

Supplementary Material

nn4c05807_si_001.pdf

Author Contributions

⊥ B.P. and M.S.M. contributed equally to this work.

The authors declare no competing financial interest.

Acknowledgments

This work was supported by the Alliance for Sustainable Energy, LLC (DE-AC36-08GO28308) under subcontract through the National Renewable Energy Laboratory and by the USC Office of Research and Innovation President’s Sustainability Initiative Large Program Award. M.S.M. would like to acknowledge King Abdulaziz University for the financial support. The authors would also like to thank Dr. Lanja R. Karadaghi in the Department of Chemistry, University of Southern California for supplying the Pt NPs for model screening and insights to this work.
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