# Steady vs. Dynamic Contributions of Different Doped Conducting Polymers in the Principal Components of an Electronic Nose's Response

Wiem Haj Ammar, Aicha Boujnah, Aimen Boubaker, Adel Kalboussi, Kamal Lmimouni, Sébastien Pecqueur

► To cite this version:

Wiem Haj Ammar, Aicha Boujnah, Aimen Boubaker, Adel Kalboussi, Kamal Lmimouni, et al.. Steady vs. Dynamic Contributions of Different Doped Conducting Polymers in the Principal Components of an Electronic Nose's Response. Eng, 2023, 4 (4), pp.2483-2496. 10.3390/eng4040141 . hal-04212911

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Submitted on 20 Sep 2023

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# Steady vs. Dynamic Contributions of Different Doped Conducting Polymers in the Principal Components of an Electronic Nose's Response

Wiem Haj Ammarª, Aicha Boujnahª, Aimen Boubakerª Adel Kalboussiª, Kamal Lmimouniᵇ & Sébastien Pecqueurᵇ*,

Email: [sebastien.pecqueur@iemn.fr](mailto:sebastien.pecqueur@iemn.fr)

## Abstract

Multivariate data analysis and machine-learning classification become popular tools to extract features without physical models for complex environments recognition. For electronic noses, time sampling over multiple sensing elements must be a fair compromise between a period sufficiently long to output a meaningful information pattern, and sufficiently short to minimize training time for practical applications. Particularly when reactivity's kinetic differs from thermodynamic in sensitive materials, finding the best compromise to get the most from data is not obvious. Here, we investigate on the influence of data acquisition to improve or alter data clustering for molecular recognition on a conducting polymer electronic nose. We found out that waiting for sensing elements to reach their steady state is not required for classification, and that reducing data acquisition down to the first dynamical information suffice to recognize molecular gases by principal component analysis with the same materials. Especially for online inference, this study shows that a good sensing array is no array of good sensors, and that new figure-of-merits shall be defined for sensing hardware aiming machine-learning pattern-recognition rather than metrology.

Keywords: conducting polymer, electronic nose, feature extraction, principal component analysis, molecular recognition

<footnote>ªDepartment of Physics, University of Monastir Tunisia.</footnote>

<footnote>ᵇUniv. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520 - IEMN, F-59000 Lille, France.</footnote>
Introduction

Current advances in artificial intelligence and internet-of-things push identifying new input
layers as data generators to feed machine-learning (ML) for information classification. Before this,
electronic noses (eNoses) have nourished the fantasy for more than 60 years of emulating a biological
sense to perform classification tasks that sensors can barely make with molecular patterns.[1-5] It is
therefore very appropriate to wonder whether conventional approaches to select sensing hardware
are adapted to information processing requirements, with pattern recognition having more standards
nowadays.

Chemistry is no trivial physics: it is no 1D-continuum nor spectrum, but volatile molecules
compose a vast group of hundred billion countable objects.[6, 7] Therefore, measuring all of them
simultaneously with restricted and well-chosen set of ultimately selective sensors is unrealistic. If such
complexity suits better ML-supported recognition than quantification using metrological sensors,
proposing clear sensing figure-of-merits (FoM) that guide technologists to identify the right sets of
materials is still needed in such context.[8-11] The main difficulty in identifying those lies in the fact
that smart sensing arrays do not involve physical models at the opposite of metrological sensors: ML
is purely mathematically-driven, which does not help in identifying device physical features to generate
qualitative data. Each output signal conditions individual components of an input data, but the quality
of the output data itself relates to features that affects the performances of the classifier as a group of
device properties. The relationship between the data transformation as a result and the physical
properties of a chosen sensing hardware is not straightforward and largely depends on the association
of a software classifier with a sensing hardware. For an eNose, conductometric sensing arrays are often
used as a hardware,[12-15] in conjunction with principal component analysis (PCA)/k-means clustering
(k-means) as a classifier [5, 16-18]. In such systems, time-dependent current signals are inputted by
environmentally sensitive materials in an array. Each current signal composes the feature of one single
coordinate of a high-dimensional vector for classification. PCA is a linear classifier as it involves only one matrix transformation from a dataset of input vectors to a set of scores. The PCA projects data on a subspace defined by a basis of the covariance-matrix eigenvectors which have the highest eigenvalues. As a consequence, PCA aims in ignoring the contributions of “silent” sensing elements, or the ones which activities that are uncorrelated to one another. Conventional sensors’ FoM do not consider information correlation between sensing elements, as it relates strictly to physical properties of individual elements to be standalone: for instance selectivity, sensitivity or time response.[19] The latter is an important FoM that usually defines if a technology is suitable for online application. If information speed is essential for telecommunication, it is not necessarily the case for the velocity of information carriers themselves. The implementation of recurrent networks for sensing elements can illustrate such fact, where the group dynamic of different slow sensing elements enables recognizing frequency-modulated signals.[20] Homogenizing the physical properties of the population of sensing elements can even yield to degrade the classifier recognition performances, so when the dynamic of sensing devices carries information, long-enough acquisition is crucial for classification.[20] However, time is also a resource to enrich a database to train a system: the slower the hardware the longer the training, which ultimately conditions any classifier’s recognition performance. As a consequence, in case information lies in the dynamic of a hardware,[21] a fair compromise must be found for data acquisition time to optimize training with enough meaningful data but reasonable training duration for environmental pattern recognition.

In the framework of recognizing volatile solvents with doped conducting polymers, we propose in this study to quantify how relevant is the acquisition time as metrological FoM for effective molecular recognition with a conductometric nose using PCA/k-means. By using both static and dynamic descriptors, we show that a steady sensing element response is unnecessary for optimal classification, and that training duration can greatly be shortened for practical inference.
Experimental Section

**Device Fabrication:** The fabrication of sensing elements used in this study has been reported elsewhere.[22] Concentric spiral-shaped Au microelectrodes (channel length L = 400 nm, spiral electrodes length W such as W/L > 10³ in a round cavity of 28 µm in diameter) were micro-fabricated by electron beam lithography in a cleanroom environment. Devices active area features a large W/L over a restricted area to maximize conductance sensitivity while minimizing total area of an array of them for system integration. Contact lines are passivated by a conformal CVD-processed Parylene C passivation layer (2 µm thickness), structured by an O₂ dry-etching to expose pads and devices active area, so each device outputs an individual response for co-integration at small scale. The active materials were subsequently deposited on top of the sensing arrays by drop casting. Formulations of regio-regular P3HT and dopants solutions (composed of Fe(OTf)₃, Bi(OTf)₃, Cu(OTf)₂, In(OTf)₃, Al(OTf)₃, Dy(OTf)₃ and Ce(OTf)₃) were done in two different steps to minimize the device electrical property distribution due to material heterogeneity if deposited with a single formulation.

**Electrical Characterization:** As raw data, the study exploits current versus time measurements of eight populations of materials, composed of three different devices for each material. Each of the 24 current versus time trace sizes 6899 data points per current trace, sampled every second. The current measurements were done with an Agilent 4155 parameter analyzer in an air blow, passing through a 5-mL vial that contains 1 mL of a volatile solvent.[22] The system was calibrated to blow at a rate of 1 mL/s over a sensing array. The control of the vapor exposure was manually operated with pneumatic valves (asynchronous delays up to five seconds can be considered between the actual operation of the valves and the attributions of the labels on the current traces). Elementary sequences of gas exposures were set to three minutes for sensing elements to reach their steady-state. Each volatile solvent exposure is followed by a purging sequence, when the air flow passes through an empty vial, to let the response of the sensing arrays recover their response.
**Data Analysis:** Raw data currents were compiled and treated without filtering. Raw data curves have already been published,[22] and are available alongside on a public repository. Principal component analysis (PCA) was used as a linear classifier, by mean of the online open access tool Clustvis [23]. PCA data was scaled by unit variance and computed by singular value decomposition. All PCA data is available as supplementary information of this article. 95%-confidence ellipsoids were determined by k-means clustering with k=3, from random values for the initial centroids (in each case, k-means was run at least three times to ensure repeatability of the resulted clusters).
Results

Data Collection and Feature Extraction.

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Classifier Structure for Solvent Vapor Recognition | **a**, Data classifier used in this study for unsupervised classification by using principal component analysis and k-means clustering on dynamical datasets generated by sensing elements composed of different materials. **b-d**, Pictures of the data acquisition hardware at different scales: photograph of seven arrays at the scale where each material were deposited (**b**), microscope picture of the active area of an array gathering 16 sensing elements (**c**), microscope picture of a single sensing element featuring a sub-micrometric conducting channel (**d**). **e**, Example of raw current signal output $i(t)$ from one single sensing element coated with one</figcaption></figure>
specific material, exposed to different sequences of solvent vapor exposures. f, Zoom-in on one time sequence when three different vapors affect the sensing elements conductance between purge sequences (zoom-in on fig.1e trace marked by a dashed square window). e-f, Blue, green and red sequences depict the sensing element current output when water, acetone, ethanol vapors respectively are blown on the sensing elements with compressed air. Grey sequences depict the sensing element current output when clean compressed air is blown on the sensing elements. g, Resistance value R(t) calculated from the raw current signal output i(t). Parameters R-R₀ and dR/dt for calculating the information features α₁(t) and α₂(t) are depicted as the absolute resistance modulation and the slope of the resistance variation overtime, extrapolated from the values of ten resistance data points in each case.

To interpret the physical significance of information features for the quality of a recognition, the machine-learning (ML) approach is preferred to the deep-learning (DL) one (see Fig.1 for the overall approach). DL algorithms differentiate from ML ones mostly by their complexity to allow the end-user not to have to identify information descriptors from raw data *a priori*. In our case, we chose specifically to compare two different features α₁ and α₂ that are extensively used in conductometric eNoses as information descriptors: the relative resistance modulation α₁=R/R₀-1 and the drift resistance α₂=R/R, with R the Ohmic resistance of a conductometric sensing element measured at a given time during the exposure to an environment of a specific class, R its first derivative of time and R₀ its value right before it starts to be exposed to an environment of a specific class at t₀. As α₁ compares two states before and after being exposed to a class, this information descriptor is qualified as a "thermodynamic feature" and relates to how much the signal's transport is affected by the presence of a given environment compared to a reference, through a given material. Instead, α₂ uses a first-order derivative of time to quantify how fast or slow the signal decays or amplifies through a given material by the presence of a given environment: this information descriptor is qualified as a "kinetic feature". Each feature is calculated for different scenarios of crossed-coupled environments/devices parameters, with a population of 24 different sensitive devices (composed of eight populations of sensitive materials measured on three different devices), exposed at 18 occurrences to different environments (six alternate permutations of three different solvent vapor exposures). The experimental details are fully described in a first study,[22] where the PCA was used exclusively with the feature α₁ calculated exclusively for the last ten seconds of each three-minute environment exposure. Although the steady-
state response for these materials was relatively fast compared to other materials for comparable
tests,[24-27] the dataset totalizes over 44 hours of time acquisition assisted by manual operations to
expose the different materials on different devices with different environments repeatedly to train the
system. Applying the same approach for exposing n! times the n different vapor classes for three
minutes would yield to over a week, over a month and over seven months of total data acquisition,
without interruption, to recognize respectively four, five and six different classes only. As reducing the
number of materials, devices, or exposures would have consequences to degrade the recognition, it is
straightforward to investigate on decreasing the elementary period for one single environment
exposure to minimize the total acquisition duration. To assess on the classifier performances for a
reduced acquisition time, a same dataset is considered, where data are pruned to ten seconds per
acquisition sequences, after the exposure starts in order to make sure that the recognition
performances do not depend on the acquisition itself, keeping the same number of datapoint to
analyze in each case. Also, PCA/k-means was systematically used on all the different case scenario with
the same normalization and initialization settings to not bias the clustering by user-dependent factors.
The recognition after clustering uses the same rules and thresholds in each case, to assess a data
sample as successfully, uncertainly or unsuccessfully recognized. All the individual PCA analysis are
provided as supplementary information.
Steady-Resistance Modulation as a Thermodynamic Information Feature.

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Analysis of the Relative Resistance Modulation as Information Descriptor | a, Extraction of the R₀ (as average for ten resistance values recorded between 11 and 20 seconds before vapor exposure, starting at t₀) and R (as average for ten resistance values recorded between t and t + 10 seconds every 10 seconds after t₀), to evaluate the feature α₁=(R-R₀)/R₀. b, PC variance for different PCA on α₁, where R is measured at different time interval [t;t+10s] after t₀ (the tricolor gradient shades linearly between the minimum and maximum value of the whole table). c-e, PCA scores on α₁, where R is measured at different time interval [0s;9s] (c), [10s;19s] (d) and [170s;179s] (e) after t₀ (confidence ellipsoids are set to 95% probability). f-g, Squared loadings of PC1 (f) and PC2 (g) for different PCA on α₁, where R is measured at different time interval [t;t+10s] after t₀ (the bicolor gradient shades linearly between the minimum and maximum value of the whole tables).</figcaption></figure>
The first analysis was performed using α₁ as information descriptor (see Fig.2), as in the former studies using the same dataset.[22, 28] The choice for this specific feature was oriented by the hypothesis that the presence of volatile molecules modulates the polymer electrical conductance by interacting with its dopant shift its thermodynamic charge-transfer equilibrium.[22] As a thermodynamic property, the acquisition was set to three minutes per exposure, to provide enough time for all devices to reach a quasi-steady state of conductance (steady output currents at a constant applied voltage). Here from the whole dataset, 18 different sub-datasets were generated by segmenting the data to concatenation of sequences of ten seconds after each exposure starts at t₀ (ten datapoints per exposures, 180 datapoints per sub-datasets – see Fig.2a). For each exposure, the feature α₁ takes into account two values: the resistance measured between the considered time interval and a reference resistance value R₀ measured while unexposed just before gas exposure. In each case, PCA was performed for the different sub-datasets, with the individual variance for each PC of each PCA for the different acquisition time compiled in Fig.2b. From these values, one can observe that most of the variance (>66%) is explained within the first two PC, except for data generated during the first ten seconds after t₀. It is also noticed that the variance for all 24 PC does not change significantly for acquisitions occurring 30 seconds after exposure (Fig.2b, the color shade encodes the variance values in linear scale), suggesting that the feature α₁ is not enriched for the data separation in (PC1;PC2) if exposures last longer than 30 seconds. As shows their scores in the (PC1;PC2) projection (Fig.2c), the PCA on α₁ during the first ten seconds acquisition, noted PCA<sup>α1</sup><sub>0-9</sub>, embeds most variability from acetone exposures on PC1 compared to two other clusters of data for water and ethanol, which are almost entirely contained in the acetone confidence ellipsoid. Already for PCA<sup>α1</sup><sub>10-19</sub> (Fig.2d), acetone is completely separable from the other two gases, and some minor overlap remains between water and ethanol confidence ellipsoids. The final sequence PCA<sup>α1</sup><sub>170-179</sub> in Fig.2e shows an ideal clustering in the (PC1;PC2) projection, that was used in the former study for gas recognition.[22] The loadings of PC1 and PC2 do not significantly depend on the acquisition sequences, except for the first ten seconds (Fig.2f and 2g): Excluding the first ten seconds, deviations overtime are however noticed: for instance the contribution of Ce(OTf)₃-doped
devices increase monotonically in the PC1<sup>a1</sup>, such as the contribution of pristine P3HT in the PC2<sup>a1</sup>. This
suggests that materials that help better for the (PC1;PC2) data separation but expresses slower than
others do not necessarily enhances the clustering. The former study concluded on the exclusive basis
of the PCA<sup>α1</sup><sub>170-179</sub> analysis, two and three materials were preferred for recognizing ethanol from
acetone from water using α<sub>1</sub> as information descriptor.[22] Fig.2f and 2g show that such conclusion is
highly acquisition-dependent (even for the same dataset considered), and that experiments performed
with shorter time exposures might have concluded otherwise for the material selection. As such
dependency on the sample acquisition time greatly conditions the preferred material choice for
classification, it appears legitimate to investigate on a dynamical feature such as α<sub>2</sub> as better
information descriptor for gas recognition with the same raw current data.
Resistance Drift as Kinetic Information Feature.

<figure><img src="image_4.png" /><figcaption>**Figure 3.** Analysis of the Resistance Drift as Information Descriptor | a, Extraction of dR/dt (linear regression over nine resistance values centered on R) and R, to evaluate the feature $\alpha_2=1/R \cdot dR/dt$. b, PC variance for different PCA on $\alpha_2$, where R is measured at different time interval $[t;t+10s]$ after $t_0$ (the tricolor gradient shades linearly between the minimum and maximum value of the whole table). c-e, PCA scores on $\alpha_2$, where R is measured at different time interval $[0s;9s]$ (c), $[10s;19s]$ (d) and $[170s;179s]$ (e) after $t_0$ (confidence ellipsoids are set to 95% probability). f-g, Squared loadings of PC1 (f) and PC2 (g) for different PCA on $\alpha_2$, where R is measured at different time interval $[t;t+10s]$ after $t_0$ (the bicolor gradient shades linearly between the minimum and maximum value of the whole tables).</figcaption></figure>
Drift resistance α₂ in conductometric eNoses is less often used as information descriptor, although it
is worth noticing that most conventional techniques in analytical chemistry use dynamical information
features as information descriptors, such as chromatography (retention time of molecules on a
substrate) or surface-plasmon resonance (absorption/desorption kinetic constants of molecules on a
substrate). In a material, drifts can often be associated to a memory effect that alters reversibly or
irreversibly a structural property,[29-33] rather than the transduction of an environmental information
in a device. In case such drift is irreversible, such behavior appears as a material instability due to
ageing and is often associated as a drawback for the lifetime. However since such reactivity on the
material is environmentally induced *per se*, it is legitimate to wonder whether the information quality
is preserved despite its effect on the information carrier itself. As an example, Kiselev et al. showed
that such resistance drift (two-time median-resistance increase over four years) on conductometric
nose is totally decorrelated from the ability of the classifier to recognize a class, as the strong
correlation between all sensing elements' resistance drift does not bias linear discriminant
analysis.[34] Supposed that part of the useful information to recognize gases lies in the diffusion kinetic
of the volatile molecules through the doped polymers, or the adsorption/desorption kinetic constants
of the molecular gases on the doped materials under flow, the drift-resistance may be a relevant
information descriptor as it takes into account the first derivative of time (Fig.3a). Replicating the same
methodology as previously for relative resistance modulation, PCA on α₂ as information descriptor has
been performed for the 18 different sub-datasets. As for α₁, the evolution of the variance for the
individual PC depends on the acquisition time (Fig.3b), mainly at the beginning of the transient
response (typically the first 20 seconds). A singular difference with α₁ is the impoverishing in data
variance on the first two PC with the increase of the acquisition time (from >66% for PCAα₁₁₇₀-₁₇₉ down
to <28% for PCAα₂₁₇₀-₁₇₉). This was expected as the information becomes more sensitive to the signal's
noise at the reach of the steady-state, and therefore, with the increase of the acquisition time
decreases the contribution of the molecularly-specific response compared in the overall information.
We noticed also in Fig.3b that significantly less variance is contained in PC2α² compared to PC2α¹, and
more importantly that variance for PC2<sup>α2</sup> is far less dependent on the acquisition time than PC2<sup>α1</sup>. This
suggests that despite any quality for the recognition, α<sub>2</sub> seems to separate data mostly because of one
single parameter (if a linear model is considered), while α<sub>1</sub> seems to depend from mostly two
parameters of a linear model. On the quality of the data separation (Fig.3c-e), similar trends for the
(PC1;PC2) diagrams are observed depending on the acquisition time: At the first ten seconds of time
acquisition after t<sub>0</sub>, acetone data are better separated from the rest, followed by the ethanol data.
Confidence ellipsoids seem to be always overlapping one another, however, we observed that even
until three minutes after time t<sub>0</sub> that the three volatile compounds are still recognizable, despite the
residual dynamic of the sensing elements. It is also noticed that PCA<sup>α2</sup><sub>170-179</sub> (Fig.3e) clusters are more
homogeneous than the ones of PCA<sup>α1</sup><sub>170-179</sub> (Fig.2e): smaller clusters within each ellipsoids can be
distinguished in PCA<sup>α1</sup><sub>170-179</sub>, which are attributed to experimental biases of the six repeated exposures
for each classes. The dependency of PCA<sup>α2</sup> loadings with the acquisition time displays different trends
than the one for PCA<sup>α1</sup> loadings (Fig.3g-h). For the first 20 s, all sensing elements except the ones
coated with pristine P3HT and Ce(OTf)<sub>3</sub>-doped P3HT seem to have a comparable contribution in the
PC1<sup>α2</sup> and the Ce(OTf)<sub>3</sub>-doped P3HT coated devices mostly constitute PC2<sup>α2</sup> if t-t<sub>0</sub> < 20 s. From 20 s
after t<sub>0</sub>, Ce(OTf)<sub>3</sub>-doped P3HT coated devices become the most significant in the PC1<sup>α2</sup>, while PC2<sup>α2</sup>
loadings seem highly random if t-t<sub>0</sub> < 20 s. This closer look on the PC1<sup>α2</sup> and PC2<sup>α2</sup> loading highlights
two different dynamics that successfully help in identifying the volatile compounds: before 20 seconds,
a “fast” and a “slow” dynamics yields the array response to recognize all three classes from PC1<sup>α2</sup> and
PC2<sup>α2</sup>. After 20 seconds, the fast dynamic of all sensing elements have faded away, to leave a minor
contribution on the Ce(OTf)<sub>3</sub>-doped P3HT devices exclusively.
Steady vs Dynamical Resistance for Gas Recognition.

<figure><img src="image_5.png" /><figcaption>**Figure 4.** Comparison of Both Descriptors | a-b, Scree plots for the PCA performed with different acquisition times, from the first ten seconds after $t_0$ ("0-9" in blue) to the last ten seconds after $t_0$ ("170-179" in red), performed with either the relative resistance modulation - $\alpha_1$ (a) or the drift resistance - $\alpha_2$ (b) as information descriptor. c, Explained variance for PC1 and PC2 with PCA performed for different acquisition time with either $\alpha_1$ or $\alpha_2$. d-f, Overlap area of the three 95%-confidence ellipsoids in (PC1,PC2), for PCA performed for different acquisition time with either $\alpha_1$ or $\alpha_2$. The area were estimated by discretizing the different regions where the three ellipsoids are segmented in the smallest square of 500x500 pixels. In figure d is displayed the example for the PCA with $\alpha_2$ for the "0-9" time acquisition and the histograms of the ellipsoids relative area as a function of the acquisition time for $\alpha_1$ and for $\alpha_2$ are respectively displayed in figure e and figure f. g-i, Detection tests of the PCA scores, for PCA performed for different acquisition time with either $\alpha_1$ or $\alpha_2$. The test is considered positive, uncertain and negative, respectively when scores are exclusively inside, not-exclusively inside and outside the corresponding 95%-confidence ellipsoid. In figure g is displayed the example for the PCA</figcaption></figure>
with α₂ for the “0-9” time acquisition and the graphs of the detection rate as a function of the
acquisition time for α₁ and for α₂ are respectively displayed in figure h and figure i.

To qualitatively compare the total variance distributions over the different PC with either α₁ or α₂ as
information descriptor, the Scree plots are displayed on two different graphs and shows the effect of
the acquisition time after t₀ for both descriptors (Fig.4a and Fig.4b). Both of them confirm the trend of
the variance with the acquisition time, for all principal components (not PC1 and PC2 exclusively). As
Fig.4a shows, the increase of the acquisition time after exposure at t₀ tends to diminish the variance
contained at the lowest PC in case of considering α₁ as information descriptor, while it tends to increase
the variance of the lower PC in case of considering α₂ as information descriptor in Fig.4b. We observed
that this diminishing rate is different for both descriptors as displayed in Fig.4a and Fig.4b, which might
come from bias between the linear *ad hoc* model computed by the PCA and the actual physical model
that governs the dependency of the information descriptors on the different sensing elements. The
position of the elbows on both plots (indicated by arrows on the Scree plots in Fig.4a and Fig.4b)
confirms that most of the useful variance for PCA<sup>α1</sup> is contained within the first three PC, while only
the first two for PCA<sup>α2</sup>. Particularly for the drift resistance α₂, this elbow is highly acquisition
dependent, and one may appreciate a richer model with more PC to account for shorter acquisition
time. The variance analysis on PC1 and PC2 for both PCA<sup>α1</sup> and PCA<sup>α2</sup> shows an acquisition time optimal
to contain most of the data variance in both PC1 and PC2 independently from the chosen information
descriptor (Fig.4c). The histogram displayed in Fig.4c shows that if the acquisition time is set at the
transient between 10 and 19 s after t₀, both information features can gather at least 70% of the total
variance in two different biparametric linear models. As both information features show opposed
trends in the PC1+PC2 variance evolution with the acquisition time, it was expected to observe an
acquisition-time optimum for both PCA, independently from the quality of the data clustering. To
quantify the recognition performances of the sensing array with PCA<sup>α1</sup> and PCA<sup>α2</sup>, the confidence
ellipsoids determined by k-means clustering are used as a threshold to declare whether the given data
belong to a given class of not, to define seven classes (as displayed in Fig.4d). The relative area of the
overlap for the confidence ellipsoids has been evaluated for the different acquisition time and
displayed in two different histograms for each descriptor in Fig.4d-f. For α₁ (Fig.4e), one can observe
that ellipsoids do not overlap from t-t₀ > 20 s, while in the case of α₂ (Fig.4f), confidence ellipsoids
always overlap one another at any time for the acquisition. However, it should be noticed that the
overlap in each case is only moderate: with less than 20% of the total area of the ellipsoids. This
suggests that recognition tests may be higher in case α₁ is used as information descriptor, than for α₂.
To verify this, recognition tests have been performed on the same sub-datasets, using the 95%-confidence ellipsoids as threshold for classification (Fig.4g-i). Detection is considered as “positive” if the actual class of the PCA score is exclusively contained within the corresponding confidence ellipsoid, “uncertain” if the actual class of the PCA score is not exclusively contained within the corresponding confidence ellipsoid, and “negative” if the actual class of the PCA score is excluded from the corresponding confidence ellipsoid. On the one hand for α₁ (Fig.4h), one can observe that the positive detection rate 99.4±0.6% for acquisition time higher than 20 s after t₀. On the one hand for α₂ (Fig.4i), one can observe that the positive detection rate >80% for acquisition times higher between 20 s and 149 s after t₀. However, we noticed that using α₂ as information descriptor enables substantially better recognition than α₁ for short acquisition time below 10 s after t₀: typically, 36% positive and 38% uncertain using α₂, while 14% positive and 53% uncertain using α₁. Overall, this statistical study support that the different materials used in the array allow recognizing volatile organic compounds because of the relative resistance modulation (α₁), rather than the resistance drift (α₂), and points out the thermodynamic of the current modulations rather than the kinetic involved in the process.
Discussion

Although this study focuses specifically on doped conducting polymers, the approach is generic to any conductometric eNose and could have been addressed to different class of conducting materials used as transducers in an array. Despite the fact that α₁ and α₂ are frequently used as information descriptors for such application, our study proposes to investigate on which feature gather most of the useful information for an eNose. However, we want to highlight that for practical applications, such a choice may be conditioned rather by the application than the quality of the data *per se*. Specifically for the resistance modulation α₁, the feature needs to routinely set a reference resistance R₀, when the environment is considered “neutral” between each data sampling. This requires therefore to have a hand on the sample exposure to periodically regenerate the device from a stimuli: it is therefore practically more convenient to use α₁ as information descriptor in cases where analytes can be exposed to a system in a supervised manner (to evaluate fragrance batch quality in an analytical laboratory for instances). Such thermodynamic feature seems practically inadequate to extract for environment recognition in case the system is permanently exposed without periodic regeneration. For the drift resistance α₂, the feature does not depend on a buffered reference. However, a steady value of the sampling period must be set to measure the dynamic: it is therefore a more adequate feature for online inference for environment recognition, at the condition that the sampling period is set *a posteriori* given the dynamic of the pattern to recognize. It is therefore a more adequate feature to recognize stimuli that are governed by a rhythm (for instance, pollution cycles in urban environments for instance). Here in this study, the selection of these features α₁ and α₂ is not based on the practical convenience to compute them in a conductometric eNose application, but on the quality of the data they output for conducting polymers used in a recognition tests that conveniently enables computing both of them at will.

In such framework, PCA confirmed that the useful information in the current response of conducting polymer materials, doped with different metal triflates and exposed to different volatile
organic compounds, lies specifically in the thermodynamic of the device resistance modulation and
not its kinetic. The analysis of the relative resistance modulation α₁ is more robust overtime when
analyzed 20 seconds after each exposures at t₀: the first two PC explains at least two third of the data
variance and allows an optimal clustering without overlap and enables 99.4±0.6% recognition.
Considering that all devices have the same geometry, mechanisms responsible for the modulation of
the electrical resistivity are pointed out. As different doping effects were assessed by varying the
nature of the dopant,[22] modulation of the doping yield, by the interaction of different volatile
molecules as electron-donating ligands on an electrophilic Lewis acid,[35-37] is not invalidated by this
analysis. The statistical approach does not allow discriminating between the effect on the charge
carrier density or the charge carrier mobility for the different materials exposed to different gases.
Despite the fact that materials' conductivity is function of two independent physical properties (the
charge carrier density and the charge carrier mobility), one cannot conclude that any of these
properties are purely marking PC1α¹ and PC2α¹, since conductivity is the product of both and PCA scores
are linear combinations of PC. Such investigation could be carried out using the logarithmic value of
the resistance modulation α₃=log(R/R₀) as information descriptor for the PCA, in order to assess the
individual contributions of the carrier mobility "μ" and carrier density "n" as principal components,
since α₃ = log(n₀/n) + log(μ₀/μ). Such validation would have to be confirmed by correlating the PCα³
loadings with systematic study of carrier density and mobility assessment, in another study that
couples this approach with further material characterizations.
Conclusion

By statistical analysis, we evidenced that the relative resistance modulation of a doped
conducting polymer array is a better information descriptor than the drift resistance, to recognize
volatile organic compounds with different metal-triflate doped polythiophene sensing elements. This
shows that the information carrier for the molecular recognition using these materials in an electronic
nose is rather linked to the thermodynamic equilibrium that yields the doped conducting polymers'
conductivity with its environment, rather than kinetic limitations which would select molecular targets'
based on their drift diffusion through different doped polymers. Despite this result suggesting to wait
for a complete steady state for each sensing element response, we evidenced that the thermodynamic
properties to recognize molecular gases lies already in the transience of the material response. Despite
materials reach a fast quasi-steady response within three minutes of environment exposures, principal
component analysis and k-means clustering show that the recognition is already higher than 99% after
only 20 seconds of gas exposures. This suggests that training time to recognize molecular environment
can greatly be optimized for practical applications, even in the case conducting materials in an
electronics nose show slow response. Applied for practical recognition with an eNose, this approach
to find a preliminary optimum for data acquisition before test shall greatly increase information
throughputs for using eNose for chemical quality assessments by reducing the training duration while
preserving most of the data quality to train an eNose to recognize various volatile molecular patterns.
## Supporting Information

Supplementary information is available in the online version of the paper.

## Acknowledgements

SP thanks the French Research Agency (ANR) for funding the "Sensation" project (Grant # ANR-22-CE24-0001-01). The authors thank the French Nanofabrication Network RENATECH for financial support of the IEMN cleanroom.

## Competing Interests

The authors declare no competing interests.
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Supporting Information for:

Steady vs. Dynamic Contributions of Different Doped Conducting Polymers in the Principal Components of an Electronic Nose's Response

Wiem Haj Ammarª, Aicha Boujnahª, Aimen Boubakerª Adel Kalboussiª, Kamal Lmimouniᵇ & Sébastien Pecqueurᵇ*,

Email: [sebastien.pecqueur@iemn.fr](mailto:sebastien.pecqueur@iemn.fr)

PCA scores (a), variance for the different PC (b) and their loadings (c) are organized in the different supporting figures by acquisition time for both information descriptors α₁ and α₂ such as:

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Descriptors</th></tr><tr><th>α₁</th><th>α₂</th></tr></thead><tbody><tr><td rowspan="18">Acquisition Time Interval (in seconds)</td><td>[0;9]</td><td>Figure S1</td><td>Figure S19</td></tr><tr><td>[10;19]</td><td>Figure S2</td><td>Figure S20</td></tr><tr><td>[20;29]</td><td>Figure S3</td><td>Figure S21</td></tr><tr><td>[30;39]</td><td>Figure S4</td><td>Figure S22</td></tr><tr><td>[40;49]</td><td>Figure S5</td><td>Figure S23</td></tr><tr><td>[50;59]</td><td>Figure S6</td><td>Figure S24</td></tr><tr><td>[60;69]</td><td>Figure S7</td><td>Figure S25</td></tr><tr><td>[70;79]</td><td>Figure S8</td><td>Figure S26</td></tr><tr><td>[80;89]</td><td>Figure S9</td><td>Figure S27</td></tr><tr><td>[90;99]</td><td>Figure S10</td><td>Figure S28</td></tr><tr><td>[100;109]</td><td>Figure S11</td><td>Figure S29</td></tr><tr><td>[110;119]</td><td>Figure S12</td><td>Figure S30</td></tr><tr><td>[120;129]</td><td>Figure S13</td><td>Figure S31</td></tr><tr><td>[130;139]</td><td>Figure S14</td><td>Figure S32</td></tr><tr><td>[140;149]</td><td>Figure S15</td><td>Figure S33</td></tr><tr><td>[150;159]</td><td>Figure S16</td><td>Figure S34</td></tr><tr><td>[160;169]</td><td>Figure S17</td><td>Figure S35</td></tr><tr><td>[170;179]</td><td>Figure S18</td><td>Figure S36</td></tr></tbody></table>

<footnote>ªDepartment of Physics, University of Monastir Tunisia.</footnote>

<footnote>ᵇUniv. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520 - IEMN, F-59000 Lille, France.</footnote>
a)

<figure><img src="image_6.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>32.9%</td></tr><tr><td>PC2</td><td>12.0%</td></tr><tr><td>PC3</td><td>7.6%</td></tr><tr><td>PC4</td><td>6.1%</td></tr><tr><td>PC5</td><td>5.2%</td></tr><tr><td>PC6</td><td>4.7%</td></tr><tr><td>PC7</td><td>4.3%</td></tr><tr><td>PC8</td><td>3.9%</td></tr><tr><td>PC9</td><td>3.6%</td></tr><tr><td>PC10</td><td>3.3%</td></tr><tr><td>PC11</td><td>2.8%</td></tr><tr><td>PC12</td><td>2.4%</td></tr><tr><td>PC13</td><td>1.9%</td></tr><tr><td>PC14</td><td>1.8%</td></tr><tr><td>PC15</td><td>1.5%</td></tr><tr><td>PC16</td><td>1.3%</td></tr><tr><td>PC17</td><td>1.1%</td></tr><tr><td>PC18</td><td>0.9%</td></tr><tr><td>PC19</td><td>0.7%</td></tr><tr><td>PC20</td><td>0.6%</td></tr><tr><td>PC21</td><td>0.5%</td></tr><tr><td>PC22</td><td>0.4%</td></tr><tr><td>PC23</td><td>0.3%</td></tr><tr><td>PC24</td><td>0.2%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.32</td><td>0.08</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.32</td><td>#3 0.04</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.23</td><td>#3 0.15</td></tr><tr><td>#1</td><td>-0.05</td><td>-0.28</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.12</td><td>#3 -0.12</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.22</td><td>#3 -0.05</td></tr><tr><td>#1</td><td>-0.11</td><td>0.27</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 -0.27</td><td>#3 0.13</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 -0.28</td><td>#3 0.24</td></tr><tr><td>#1</td><td>-0.26</td><td>0.01</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.30</td><td>#3 0.06</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.31</td><td>#3 0.05</td></tr><tr><td>#1</td><td>-0.03</td><td>-0.02</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.11</td><td>#3 -0.03</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.19</td><td>#3 -0.08</td></tr><tr><td>#1</td><td>-0.20</td><td>-0.19</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.30</td><td>#3 -0.05</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.14</td><td>#3 0.28</td></tr><tr><td>#1</td><td>-0.12</td><td>-0.27</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.10</td><td>#3 -0.43</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.10</td><td>#3 -0.50</td></tr><tr><td>#1</td><td>0.08</td><td>-0.21</td></tr><tr><td>no triflate</td><td>#2 0.15</td><td>#3 0.02</td></tr><tr><td>no triflate</td><td>#3 -0.03</td><td>0.20</td></tr></tbody></table>

**Figure S1.** PCA on α₁, for R is measured at different time interval [0s;9s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_7.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>57.0%</td></tr><tr><td>PC2</td><td>15.0%</td></tr><tr><td>PC3</td><td>6.1%</td></tr><tr><td>PC4</td><td>4.4%</td></tr><tr><td>PC5</td><td>2.9%</td></tr><tr><td>PC6</td><td>2.6%</td></tr><tr><td>PC7</td><td>2.0%</td></tr><tr><td>PC8</td><td>1.8%</td></tr><tr><td>PC9</td><td>1.6%</td></tr><tr><td>PC10</td><td>1.1%</td></tr><tr><td>PC11</td><td>0.9%</td></tr><tr><td>PC12</td><td>0.8%</td></tr><tr><td>PC13</td><td>0.7%</td></tr><tr><td>PC14</td><td>0.6%</td></tr><tr><td>PC15</td><td>0.5%</td></tr><tr><td>PC16</td><td>0.4%</td></tr><tr><td>PC17</td><td>0.4%</td></tr><tr><td>PC18</td><td>0.3%</td></tr><tr><td>PC19</td><td>0.2%</td></tr><tr><td>PC20</td><td>0.2%</td></tr><tr><td>PC21</td><td>0.2%</td></tr><tr><td>PC22</td><td>0.1%</td></tr><tr><td>PC23</td><td>0.1%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.20</td><td>-0.16</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.24</td><td>#3 -0.14</td></tr><tr><td>#1</td><td>-0.19</td><td>0.19</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.24</td><td>#3 -0.11</td></tr><tr><td>#1</td><td>-0.18</td><td>-0.33</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 -0.21</td><td>#3 -0.18</td></tr><tr><td>#1</td><td>-0.23</td><td>0.08</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.25</td><td>#3 -0.26</td></tr><tr><td>#1</td><td>-0.22</td><td>0.11</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.25</td><td>#3 -0.21</td></tr><tr><td>#1</td><td>-0.20</td><td>0.23</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.24</td><td>#3 -0.19</td></tr><tr><td>#1</td><td>-0.13</td><td>0.39</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.07</td><td>#3 -0.17</td></tr><tr><td>#1</td><td>0.13</td><td>0.31</td></tr><tr><td>no triflate</td><td>#2 0.23</td><td>#3 -0.06</td></tr></tbody></table>

**Figure S2.** PCA on $\alpha_1$, for R is measured at different time interval [10s;19s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

b)

a)

b)

a)

b)

a)

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>50.3%</td></tr><tr><td>PC2</td><td>21.7%</td></tr><tr><td>PC3</td><td>8.0%</td></tr><tr><td>PC4</td><td>3.4%</td></tr><tr><td>PC5</td><td>3.2%</td></tr><tr><td>PC6</td><td>2.7%</td></tr><tr><td>PC7</td><td>2.6%</td></tr><tr><td>PC8</td><td>1.8%</td></tr><tr><td>PC9</td><td>1.5%</td></tr><tr><td>PC10</td><td>1.1%</td></tr><tr><td>PC11</td><td>1.0%</td></tr><tr><td>PC12</td><td>0.7%</td></tr><tr><td>PC13</td><td>0.4%</td></tr><tr><td>PC14</td><td>0.3%</td></tr><tr><td>PC15</td><td>0.3%</td></tr><tr><td>PC16</td><td>0.2%</td></tr><tr><td>PC17</td><td>0.2%</td></tr><tr><td>PC18</td><td>0.2%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.1%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

c)

<table><thead><tr><th></th><th colspan="2">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.16</td><td>0.21</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.22</td><td>#3 0.24</td></tr><tr><td></td><td>#1 -0.17</td><td>#2 -0.22</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.26</td><td>#3 -0.07</td></tr><tr><td></td><td>#1 -0.17</td><td>#2 -0.19</td></tr><tr><td></td><td>#1 -0.14</td><td>#2 0.32</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 -0.24</td><td>#3 -0.05</td></tr><tr><td></td><td>#1 -0.24</td><td>#2 0.33</td></tr><tr><td></td><td>#1 -0.25</td><td>#2 -0.09</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.27</td><td>#3 -0.10</td></tr><tr><td></td><td>#1 -0.28</td><td>#2 0.05</td></tr><tr><td></td><td>#1 -0.23</td><td>#2 -0.02</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.27</td><td>#3 0.02</td></tr><tr><td></td><td>#1 -0.24</td><td>#2 0.00</td></tr><tr><td></td><td>#1 -0.22</td><td>#2 -0.23</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.25</td><td>#3 -0.14</td></tr><tr><td></td><td>#1 -0.21</td><td>#2 0.18</td></tr><tr><td></td><td>#1 -0.19</td><td>#2 -0.30</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.07</td><td>#3 -0.29</td></tr><tr><td></td><td>#1 -0.16</td><td>#2 -0.29</td></tr><tr><td></td><td>#1 0.11</td><td>#2 -0.28</td></tr><tr><td>no triflate</td><td>#2 0.21</td><td>#3 -0.26</td></tr><tr><td></td><td>#1 0.04</td><td>#2 -0.14</td></tr></tbody></table>

**Figure S3.** PCA on $\alpha_1$, for R is measured at different time interval [20s;29s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

b)

a)

b)

a)

b)

a)

b)

<table><caption>Figure S4. PCA on α₁, for R is measured at different time interval [30s;39s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.</caption><thead><tr><th></th><th colspan="2">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.12</td><td>0.25</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.20</td><td>#3 0.25</td></tr><tr><td>#1</td><td>-0.16</td><td>-0.21</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.26</td><td>#3 -0.10</td></tr><tr><td>#1</td><td>-0.06</td><td>0.30</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 -0.26</td><td>#3 0.01</td></tr><tr><td>#1</td><td>-0.27</td><td>-0.08</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.28</td><td>#3 0.11</td></tr><tr><td>#1</td><td>-0.21</td><td>-0.02</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.28</td><td>#3 0.06</td></tr><tr><td>#1</td><td>-0.24</td><td>-0.02</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.26</td><td>#3 -0.14</td></tr><tr><td>#1</td><td>-0.22</td><td>-0.25</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.03</td><td>#3 -0.27</td></tr><tr><td>#1</td><td>0.14</td><td>-0.28</td></tr><tr><td>no triflate</td><td>#2 0.20</td><td>#3 -0.26</td></tr><tr><td>#1</td><td>0.17</td><td>-0.21</td></tr></tbody></table>

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>44.1%</td></tr><tr><td>PC2</td><td>28.4%</td></tr><tr><td>PC3</td><td>7.5%</td></tr><tr><td>PC4</td><td>4.3%</td></tr><tr><td>PC5</td><td>3.3%</td></tr><tr><td>PC6</td><td>2.6%</td></tr><tr><td>PC7</td><td>2.2%</td></tr><tr><td>PC8</td><td>1.9%</td></tr><tr><td>PC9</td><td>1.7%</td></tr><tr><td>PC10</td><td>0.9%</td></tr><tr><td>PC11</td><td>0.8%</td></tr><tr><td>PC12</td><td>0.5%</td></tr><tr><td>PC13</td><td>0.5%</td></tr><tr><td>PC14</td><td>0.4%</td></tr><tr><td>PC15</td><td>0.3%</td></tr><tr><td>PC16</td><td>0.2%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.1%</td></tr><tr><td>PC21</td><td>0.1%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>
a)

<figure><img src="image_8.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>41.7%</td></tr><tr><td>PC2</td><td>31.5%</td></tr><tr><td>PC3</td><td>7.2%</td></tr><tr><td>PC4</td><td>4.2%</td></tr><tr><td>PC5</td><td>3.6%</td></tr><tr><td>PC6</td><td>2.6%</td></tr><tr><td>PC7</td><td>2.2%</td></tr><tr><td>PC8</td><td>1.9%</td></tr><tr><td>PC9</td><td>1.4%</td></tr><tr><td>PC10</td><td>0.8%</td></tr><tr><td>PC11</td><td>0.8%</td></tr><tr><td>PC12</td><td>0.5%</td></tr><tr><td>PC13</td><td>0.4%</td></tr><tr><td>PC14</td><td>0.3%</td></tr><tr><td>PC15</td><td>0.2%</td></tr><tr><td>PC16</td><td>0.2%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.1%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.07</td><td>0.26</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.15</td><td>0.28</td></tr><tr><td></td><td>#3 -0.05</td><td>0.31</td></tr><tr><td></td><td>#1 -0.17</td><td>-0.21</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.28</td><td>-0.07</td></tr><tr><td></td><td>#3 -0.13</td><td>-0.21</td></tr><tr><td></td><td>#1 0.03</td><td>0.27</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 -0.26</td><td>0.08</td></tr><tr><td></td><td>#3 0.10</td><td>0.25</td></tr><tr><td></td><td>#1 -0.28</td><td>-0.04</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.29</td><td>-0.09</td></tr><tr><td></td><td>#3 -0.29</td><td>0.07</td></tr><tr><td></td><td>#1 -0.24</td><td>0.01</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.28</td><td>0.11</td></tr><tr><td></td><td>#3 -0.25</td><td>0.00</td></tr><tr><td></td><td>#1 -0.27</td><td>-0.16</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.28</td><td>-0.09</td></tr><tr><td></td><td>#3 -0.18</td><td>0.21</td></tr><tr><td></td><td>#1 -0.25</td><td>-0.19</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.05</td><td>-0.27</td></tr><tr><td></td><td>#3 -0.12</td><td>-0.25</td></tr><tr><td></td><td>#1 0.12</td><td>-0.30</td></tr><tr><td>no triflate</td><td>#2 0.16</td><td>-0.29</td></tr><tr><td></td><td>#3 0.16</td><td>-0.26</td></tr></tbody></table>

**Figure S5.** PCA on $\alpha_1$, for R is measured at different time interval [40s;49s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_9.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>41.4%</td></tr><tr><td>PC2</td><td>31.9%</td></tr><tr><td>PC3</td><td>6.4%</td></tr><tr><td>PC4</td><td>4.5%</td></tr><tr><td>PC5</td><td>3.3%</td></tr><tr><td>PC6</td><td>2.5%</td></tr><tr><td>PC7</td><td>2.4%</td></tr><tr><td>PC8</td><td>2.1%</td></tr><tr><td>PC9</td><td>1.4%</td></tr><tr><td>PC10</td><td>1.0%</td></tr><tr><td>PC11</td><td>0.9%</td></tr><tr><td>PC12</td><td>0.6%</td></tr><tr><td>PC13</td><td>0.4%</td></tr><tr><td>PC14</td><td>0.4%</td></tr><tr><td>PC15</td><td>0.2%</td></tr><tr><td>PC16</td><td>0.2%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.0%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.03</td><td>0.26</td></tr><tr><td>Fe(OTf)<sub>3</sub> #2</td><td>-0.09</td><td>0.30</td></tr><tr><td>#3</td><td>0.02</td><td>0.30</td></tr><tr><td>#1</td><td>-0.19</td><td>-0.18</td></tr><tr><td>Cu(OTf)<sub>2</sub> #2</td><td>-0.30</td><td>-0.01</td></tr><tr><td>#3</td><td>-0.16</td><td>-0.22</td></tr><tr><td>#1</td><td>0.10</td><td>0.24</td></tr><tr><td>Bi(OTf)<sub>3</sub> #2</td><td>-0.25</td><td>0.14</td></tr><tr><td>#3</td><td>0.17</td><td>0.20</td></tr><tr><td>#1</td><td>-0.28</td><td>0.01</td></tr><tr><td>Al(OTf)<sub>3</sub> #2</td><td>-0.29</td><td>-0.07</td></tr><tr><td>#3</td><td>-0.28</td><td>0.10</td></tr><tr><td>#1</td><td>-0.20</td><td>0.05</td></tr><tr><td>In(OTf)<sub>3</sub> #2</td><td>-0.26</td><td>0.14</td></tr><tr><td>#3</td><td>-0.25</td><td>0.03</td></tr><tr><td>#1</td><td>-0.29</td><td>-0.11</td></tr><tr><td>Dy(OTf)<sub>3</sub> #2</td><td>-0.29</td><td>-0.04</td></tr><tr><td>#3</td><td>-0.15</td><td>0.24</td></tr><tr><td>#1</td><td>-0.27</td><td>-0.15</td></tr><tr><td>Ce(OTf)<sub>3</sub> #2</td><td>-0.08</td><td>-0.28</td></tr><tr><td>#3</td><td>-0.14</td><td>-0.24</td></tr><tr><td>#1</td><td>0.08</td><td>-0.32</td></tr><tr><td>no triflate #2</td><td>0.11</td><td>-0.32</td></tr><tr><td>#3</td><td>0.14</td><td>-0.29</td></tr></tbody></table>

**Figure S6.** PCA on $\alpha_1$, for R is measured at different time interval [50s;59s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_10.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>41.5%</td></tr><tr><td>PC2</td><td>31.1%</td></tr><tr><td>PC3</td><td>6.4%</td></tr><tr><td>PC4</td><td>5.0%</td></tr><tr><td>PC5</td><td>3.2%</td></tr><tr><td>PC6</td><td>2.8%</td></tr><tr><td>PC7</td><td>2.4%</td></tr><tr><td>PC8</td><td>2.3%</td></tr><tr><td>PC9</td><td>1.3%</td></tr><tr><td>PC10</td><td>1.1%</td></tr><tr><td>PC11</td><td>0.8%</td></tr><tr><td>PC12</td><td>0.5%</td></tr><tr><td>PC13</td><td>0.4%</td></tr><tr><td>PC14</td><td>0.3%</td></tr><tr><td>PC15</td><td>0.3%</td></tr><tr><td>PC16</td><td>0.2%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.1%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.01</td><td>-0.26</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 0.07</td><td>#3 -0.30</td></tr><tr><td></td><td>#2 -0.05</td><td>#3 -0.29</td></tr><tr><td></td><td>#1 0.20</td><td>#2 0.15</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 0.29</td><td>#3 -0.05</td></tr><tr><td></td><td>#3 0.17</td><td>#2 0.21</td></tr><tr><td></td><td>#1 -0.16</td><td>#2 -0.19</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 0.22</td><td>#3 -0.20</td></tr><tr><td></td><td>#3 -0.21</td><td>#2 -0.15</td></tr><tr><td></td><td>#1 0.28</td><td>#2 -0.04</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 0.29</td><td>#3 0.07</td></tr><tr><td></td><td>#3 0.28</td><td>#2 -0.11</td></tr><tr><td></td><td>#1 0.18</td><td>#2 -0.08</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 0.25</td><td>#3 -0.17</td></tr><tr><td></td><td>#3 0.25</td><td>#2 -0.05</td></tr><tr><td></td><td>#1 0.30</td><td>#2 0.06</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 0.29</td><td>#3 0.01</td></tr><tr><td></td><td>#3 0.13</td><td>#2 -0.25</td></tr><tr><td></td><td>#1 0.28</td><td>#2 0.13</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 0.10</td><td>#3 0.27</td></tr><tr><td></td><td>#3 0.16</td><td>#2 0.24</td></tr><tr><td></td><td>#1 -0.04</td><td>#2 0.34</td></tr><tr><td>no triflate</td><td>#2 -0.08</td><td>#3 0.33</td></tr><tr><td></td><td>#3 -0.10</td><td>#2 0.32</td></tr></tbody></table>

**Figure S7.** PCA on $\alpha_1$, for R is measured at different time interval [60s;69s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_11.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>42.2%</td></tr><tr><td>PC2</td><td>30.6%</td></tr><tr><td>PC3</td><td>6.2%</td></tr><tr><td>PC4</td><td>5.2%</td></tr><tr><td>PC5</td><td>3.1%</td></tr><tr><td>PC6</td><td>2.9%</td></tr><tr><td>PC7</td><td>2.5%</td></tr><tr><td>PC8</td><td>2.1%</td></tr><tr><td>PC9</td><td>1.3%</td></tr><tr><td>PC10</td><td>0.9%</td></tr><tr><td>PC11</td><td>0.9%</td></tr><tr><td>PC12</td><td>0.6%</td></tr><tr><td>PC13</td><td>0.3%</td></tr><tr><td>PC14</td><td>0.2%</td></tr><tr><td>PC15</td><td>0.2%</td></tr><tr><td>PC16</td><td>0.2%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.0%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.04</td><td>-0.25</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>0.06</td><td>-0.30</td></tr><tr><td>#3</td><td>-0.06</td><td>-0.27</td></tr><tr><td>#1</td><td>0.22</td><td>0.12</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>0.29</td><td>-0.06</td></tr><tr><td>#3</td><td>0.19</td><td>0.20</td></tr><tr><td>#1</td><td>-0.20</td><td>-0.12</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>0.19</td><td>-0.23</td></tr><tr><td>#3</td><td>-0.23</td><td>-0.11</td></tr><tr><td>#1</td><td>0.28</td><td>-0.05</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>0.28</td><td>0.06</td></tr><tr><td>#3</td><td>0.27</td><td>-0.10</td></tr><tr><td>#1</td><td>0.18</td><td>-0.06</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>0.23</td><td>-0.19</td></tr><tr><td>#3</td><td>0.24</td><td>-0.07</td></tr><tr><td>#1</td><td>0.30</td><td>0.03</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>0.29</td><td>-0.02</td></tr><tr><td>#3</td><td>0.11</td><td>-0.27</td></tr><tr><td>#1</td><td>0.28</td><td>0.11</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>0.12</td><td>0.27</td></tr><tr><td>#3</td><td>0.18</td><td>0.25</td></tr><tr><td>#1</td><td>-0.01</td><td>0.34</td></tr><tr><td>no triflate</td><td>-0.04</td><td>0.34</td></tr><tr><td>#3</td><td>-0.07</td><td>0.33</td></tr></tbody></table>

**Figure S8.** PCA on $\alpha_1$, for R is measured at different time interval [70s;79s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_12.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>42.4%</td></tr><tr><td>PC2</td><td>29.9%</td></tr><tr><td>PC3</td><td>6.6%</td></tr><tr><td>PC4</td><td>5.4%</td></tr><tr><td>PC5</td><td>3.3%</td></tr><tr><td>PC6</td><td>2.7%</td></tr><tr><td>PC7</td><td>2.5%</td></tr><tr><td>PC8</td><td>2.4%</td></tr><tr><td>PC9</td><td>1.3%</td></tr><tr><td>PC10</td><td>0.9%</td></tr><tr><td>PC11</td><td>0.8%</td></tr><tr><td>PC12</td><td>0.6%</td></tr><tr><td>PC13</td><td>0.4%</td></tr><tr><td>PC14</td><td>0.2%</td></tr><tr><td>PC15</td><td>0.2%</td></tr><tr><td>PC16</td><td>0.2%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.0%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.07</td><td>0.24</td></tr><tr><td>Fe(OTf)<sub>3</sub> #2</td><td>0.04</td><td>0.31</td></tr><tr><td>#3</td><td>-0.07</td><td>0.26</td></tr><tr><td>#1</td><td>0.23</td><td>-0.14</td></tr><tr><td>Cu(OTf)<sub>2</sub> #2</td><td>0.29</td><td>0.07</td></tr><tr><td>#3</td><td>0.22</td><td>-0.14</td></tr><tr><td>#1</td><td>-0.22</td><td>0.04</td></tr><tr><td>Bi(OTf)<sub>3</sub> #2</td><td>0.16</td><td>0.27</td></tr><tr><td>#3</td><td>-0.25</td><td>0.06</td></tr><tr><td>#1</td><td>0.27</td><td>0.09</td></tr><tr><td>Al(OTf)<sub>3</sub> #2</td><td>0.28</td><td>-0.03</td></tr><tr><td>#3</td><td>0.27</td><td>0.09</td></tr><tr><td>#1</td><td>0.17</td><td>0.07</td></tr><tr><td>In(OTf)<sub>3</sub> #2</td><td>0.21</td><td>0.21</td></tr><tr><td>#3</td><td>0.23</td><td>0.09</td></tr><tr><td>#1</td><td>0.30</td><td>0.01</td></tr><tr><td>Dy(OTf)<sub>3</sub> #2</td><td>0.29</td><td>0.05</td></tr><tr><td>#3</td><td>0.06</td><td>0.28</td></tr><tr><td>#1</td><td>0.28</td><td>-0.10</td></tr><tr><td>Ce(OTf)<sub>3</sub> #2</td><td>0.14</td><td>-0.26</td></tr><tr><td>#3</td><td>0.19</td><td>-0.25</td></tr><tr><td>#1</td><td>0.03</td><td>-0.35</td></tr><tr><td>no triflate #2</td><td>-0.01</td><td>-0.35</td></tr><tr><td>#3</td><td>-0.04</td><td>-0.35</td></tr></tbody></table>

**Figure S9.** PCA on $\alpha_1$, for R is measured at different time interval [80s;89s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_13.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>40.7%</td></tr><tr><td>PC2</td><td>30.0%</td></tr><tr><td>PC3</td><td>6.9%</td></tr><tr><td>PC4</td><td>5.9%</td></tr><tr><td>PC5</td><td>3.7%</td></tr><tr><td>PC6</td><td>3.0%</td></tr><tr><td>PC7</td><td>2.8%</td></tr><tr><td>PC8</td><td>2.3%</td></tr><tr><td>PC9</td><td>1.1%</td></tr><tr><td>PC10</td><td>0.9%</td></tr><tr><td>PC11</td><td>0.7%</td></tr><tr><td>PC12</td><td>0.5%</td></tr><tr><td>PC13</td><td>0.4%</td></tr><tr><td>PC14</td><td>0.3%</td></tr><tr><td>PC15</td><td>0.2%</td></tr><tr><td>PC16</td><td>0.2%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.0%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.11</td><td>0.22</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>0.02</td><td>0.30</td></tr><tr><td>#3</td><td>-0.08</td><td>0.24</td></tr><tr><td>#1</td><td>0.23</td><td>-0.11</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>0.28</td><td>0.10</td></tr><tr><td>#3</td><td>0.20</td><td>-0.16</td></tr><tr><td>#1</td><td>-0.22</td><td>-0.04</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>0.14</td><td>0.29</td></tr><tr><td>#3</td><td>-0.26</td><td>0.01</td></tr><tr><td>#1</td><td>0.26</td><td>0.11</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>0.28</td><td>-0.02</td></tr><tr><td>#3</td><td>0.27</td><td>0.08</td></tr><tr><td>#1</td><td>0.15</td><td>0.06</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>0.18</td><td>0.24</td></tr><tr><td>#3</td><td>0.22</td><td>0.11</td></tr><tr><td>#1</td><td>0.30</td><td>0.05</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>0.29</td><td>0.09</td></tr><tr><td>#3</td><td>0.03</td><td>0.29</td></tr><tr><td>#1</td><td>0.29</td><td>-0.08</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>0.16</td><td>-0.24</td></tr><tr><td>#3</td><td>0.22</td><td>-0.23</td></tr><tr><td>#1</td><td>0.05</td><td>-0.35</td></tr><tr><td>no triflate</td><td>0.04</td><td>-0.35</td></tr><tr><td>#3</td><td>0.01</td><td>-0.35</td></tr></tbody></table>

**Figure S10.** PCA on $\alpha_1$, for R is measured at different time interval [90s;99s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_14.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>40.0%</td></tr><tr><td>PC2</td><td>29.5%</td></tr><tr><td>PC3</td><td>7.8%</td></tr><tr><td>PC4</td><td>6.0%</td></tr><tr><td>PC5</td><td>4.1%</td></tr><tr><td>PC6</td><td>3.1%</td></tr><tr><td>PC7</td><td>2.7%</td></tr><tr><td>PC8</td><td>2.3%</td></tr><tr><td>PC9</td><td>1.1%</td></tr><tr><td>PC10</td><td>0.9%</td></tr><tr><td>PC11</td><td>0.7%</td></tr><tr><td>PC12</td><td>0.5%</td></tr><tr><td>PC13</td><td>0.4%</td></tr><tr><td>PC14</td><td>0.3%</td></tr><tr><td>PC15</td><td>0.2%</td></tr><tr><td>PC16</td><td>0.1%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.0%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.14</td><td>0.19</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.01</td><td>#3 0.29</td></tr><tr><td></td><td>#2 -0.11</td><td>#3 0.22</td></tr><tr><td>#1</td><td>0.24</td><td>-0.09</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 0.28</td><td>#3 0.11</td></tr><tr><td></td><td>#2 0.20</td><td>#3 -0.14</td></tr><tr><td>#1</td><td>-0.21</td><td>-0.11</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 0.10</td><td>#3 0.32</td></tr><tr><td></td><td>#3 -0.26</td><td>-0.05</td></tr><tr><td>#1</td><td>0.25</td><td>0.14</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 0.27</td><td>#3 -0.02</td></tr><tr><td></td><td>#2 0.27</td><td>#3 0.09</td></tr><tr><td>#1</td><td>0.14</td><td>0.04</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 0.16</td><td>#3 0.26</td></tr><tr><td></td><td>#2 0.21</td><td>#3 0.13</td></tr><tr><td>#1</td><td>0.30</td><td>0.09</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 0.27</td><td>#3 0.14</td></tr><tr><td></td><td>#2 0.27</td><td>#3 0.29</td></tr><tr><td>#1</td><td>0.30</td><td>-0.05</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 0.19</td><td>#3 -0.22</td></tr><tr><td></td><td>#2 0.24</td><td>#3 -0.21</td></tr><tr><td>#1</td><td>0.09</td><td>-0.34</td></tr><tr><td>no triflate</td><td>#2 0.09</td><td>#3 -0.34</td></tr><tr><td></td><td>#2 0.09</td><td>#3 -0.35</td></tr></tbody></table>

**Figure S11.** PCA on $\alpha_1$, for R is measured at different time interval [100s;109s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
<figure><img src="image_15.png" /></figure>

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>40.5%</td></tr><tr><td>PC2</td><td>29.0%</td></tr><tr><td>PC3</td><td>8.2%</td></tr><tr><td>PC4</td><td>6.0%</td></tr><tr><td>PC5</td><td>3.8%</td></tr><tr><td>PC6</td><td>3.0%</td></tr><tr><td>PC7</td><td>2.6%</td></tr><tr><td>PC8</td><td>2.1%</td></tr><tr><td>PC9</td><td>1.2%</td></tr><tr><td>PC10</td><td>1.0%</td></tr><tr><td>PC11</td><td>0.7%</td></tr><tr><td>PC12</td><td>0.5%</td></tr><tr><td>PC13</td><td>0.4%</td></tr><tr><td>PC14</td><td>0.3%</td></tr><tr><td>PC15</td><td>0.2%</td></tr><tr><td>PC16</td><td>0.1%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.0%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

<table><thead><tr><th></th><th colspan="2">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.15</td><td>0.16</td></tr><tr><td>Fe(OTf)<sub>3</sub> #2</td><td>-0.02</td><td>0.28</td></tr><tr><td>#3</td><td>-0.12</td><td>0.19</td></tr><tr><td>#1</td><td>0.24</td><td>-0.07</td></tr><tr><td>Cu(OTf)<sub>2</sub> #2</td><td>0.27</td><td>0.12</td></tr><tr><td>#3</td><td>0.23</td><td>-0.13</td></tr><tr><td>#1</td><td>-0.19</td><td>-0.16</td></tr><tr><td>Bi(OTf)<sub>3</sub> #2</td><td>0.07</td><td>0.33</td></tr><tr><td>#3</td><td>-0.25</td><td>-0.10</td></tr><tr><td>#1</td><td>0.24</td><td>0.15</td></tr><tr><td>Al(OTf)<sub>3</sub> #2</td><td>0.27</td><td>-0.01</td></tr><tr><td>#3</td><td>0.27</td><td>0.09</td></tr><tr><td>#1</td><td>0.16</td><td>-0.01</td></tr><tr><td>In(OTf)<sub>3</sub> #2</td><td>0.16</td><td>0.25</td></tr><tr><td>#3</td><td>0.20</td><td>0.15</td></tr><tr><td>#1</td><td>0.29</td><td>0.12</td></tr><tr><td>Dy(OTf)<sub>3</sub> #2</td><td>0.26</td><td>0.17</td></tr><tr><td>#3</td><td>-0.03</td><td>0.30</td></tr><tr><td>#1</td><td>0.30</td><td>-0.04</td></tr><tr><td>Ce(OTf)<sub>3</sub> #2</td><td>0.20</td><td>-0.21</td></tr><tr><td>#3</td><td>0.25</td><td>-0.19</td></tr><tr><td>#1</td><td>0.11</td><td>-0.33</td></tr><tr><td>no triflate #2</td><td>0.11</td><td>-0.33</td></tr><tr><td>#3</td><td>0.08</td><td>-0.35</td></tr></tbody></table>

Figure S12. PCA on α₁, for R is measured at different time interval [110s;119s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_16.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>39.7%</td></tr><tr><td>PC2</td><td>28.3%</td></tr><tr><td>PC3</td><td>8.6%</td></tr><tr><td>PC4</td><td>6.2%</td></tr><tr><td>PC5</td><td>4.3%</td></tr><tr><td>PC6</td><td>3.0%</td></tr><tr><td>PC7</td><td>2.8%</td></tr><tr><td>PC8</td><td>1.9%</td></tr><tr><td>PC9</td><td>1.4%</td></tr><tr><td>PC10</td><td>1.3%</td></tr><tr><td>PC11</td><td>0.7%</td></tr><tr><td>PC12</td><td>0.5%</td></tr><tr><td>PC13</td><td>0.4%</td></tr><tr><td>PC14</td><td>0.2%</td></tr><tr><td>PC15</td><td>0.2%</td></tr><tr><td>PC16</td><td>0.1%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.0%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.14</td><td>0.14</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.03</td><td>#3 0.27</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.13</td><td>#3 0.16</td></tr><tr><td>#1</td><td>0.25</td><td>-0.06</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 0.25</td><td>#3 0.14</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 0.24</td><td>#3 -0.08</td></tr><tr><td>#1</td><td>-0.17</td><td>-0.20</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 0.05</td><td>#3 0.34</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#3 -0.24</td><td>#3 -0.14</td></tr><tr><td>#1</td><td>0.24</td><td>0.16</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 0.26</td><td>#3 -0.03</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#3 0.27</td><td>#3 0.08</td></tr><tr><td>#1</td><td>0.15</td><td>-0.05</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 0.15</td><td>#3 0.26</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#3 0.20</td><td>#3 0.15</td></tr><tr><td>#1</td><td>0.29</td><td>0.14</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 0.25</td><td>#3 0.20</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#3 -0.04</td><td>#3 0.30</td></tr><tr><td>#1</td><td>0.31</td><td>-0.04</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 0.22</td><td>#3 -0.20</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#3 0.26</td><td>#3 -0.18</td></tr><tr><td>#1</td><td>0.13</td><td>-0.33</td></tr><tr><td>no triflate</td><td>#2 0.13</td><td>#3 -0.33</td></tr><tr><td>no triflate</td><td>#3 0.10</td><td>#3 -0.35</td></tr></tbody></table>

**Figure S13.** PCA on $\alpha_1$, for R is measured at different time interval [120s;129s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_17.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>40.5%</td></tr><tr><td>PC2</td><td>27.8%</td></tr><tr><td>PC3</td><td>8.9%</td></tr><tr><td>PC4</td><td>6.5%</td></tr><tr><td>PC5</td><td>3.9%</td></tr><tr><td>PC6</td><td>3.0%</td></tr><tr><td>PC7</td><td>2.6%</td></tr><tr><td>PC8</td><td>1.7%</td></tr><tr><td>PC9</td><td>1.5%</td></tr><tr><td>PC10</td><td>1.1%</td></tr><tr><td>PC11</td><td>0.7%</td></tr><tr><td>PC12</td><td>0.5%</td></tr><tr><td>PC13</td><td>0.3%</td></tr><tr><td>PC14</td><td>0.2%</td></tr><tr><td>PC15</td><td>0.2%</td></tr><tr><td>PC16</td><td>0.1%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.0%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.14</td><td>0.14</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>-0.05</td><td>0.25</td></tr><tr><td>#3</td><td>-0.14</td><td>0.15</td></tr><tr><td>#1</td><td>0.25</td><td>-0.03</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>0.25</td><td>0.13</td></tr><tr><td>#3</td><td>0.25</td><td>-0.06</td></tr><tr><td>#1</td><td>-0.14</td><td>-0.23</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>0.03</td><td>0.35</td></tr><tr><td>#3</td><td>-0.23</td><td>-0.17</td></tr><tr><td>#1</td><td>0.24</td><td>0.18</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>0.26</td><td>-0.03</td></tr><tr><td>#3</td><td>0.28</td><td>0.07</td></tr><tr><td>#1</td><td>0.16</td><td>-0.03</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>0.14</td><td>0.25</td></tr><tr><td>#3</td><td>0.19</td><td>0.17</td></tr><tr><td>#1</td><td>0.28</td><td>0.16</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>0.24</td><td>0.22</td></tr><tr><td>#3</td><td>-0.07</td><td>0.30</td></tr><tr><td>#1</td><td>0.30</td><td>-0.03</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>0.22</td><td>-0.18</td></tr><tr><td>#3</td><td>0.27</td><td>-0.16</td></tr><tr><td>#1</td><td>0.16</td><td>-0.31</td></tr><tr><td>no triflate</td><td>0.16</td><td>-0.32</td></tr><tr><td>#3</td><td>0.13</td><td>-0.34</td></tr></tbody></table>

**Figure S14.** PCA on $\alpha_1$, for R is measured at different time interval [130s;139s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
<figure><img src="image_18.png" /><figcaption>b)</figcaption></figure>

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>40.2%</td></tr><tr><td>PC2</td><td>27.1%</td></tr><tr><td>PC3</td><td>8.9%</td></tr><tr><td>PC4</td><td>6.6%</td></tr><tr><td>PC5</td><td>4.1%</td></tr><tr><td>PC6</td><td>3.3%</td></tr><tr><td>PC7</td><td>2.6%</td></tr><tr><td>PC8</td><td>2.1%</td></tr><tr><td>PC9</td><td>1.7%</td></tr><tr><td>PC10</td><td>1.1%</td></tr><tr><td>PC11</td><td>0.7%</td></tr><tr><td>PC12</td><td>0.4%</td></tr><tr><td>PC13</td><td>0.4%</td></tr><tr><td>PC14</td><td>0.3%</td></tr><tr><td>PC15</td><td>0.2%</td></tr><tr><td>PC16</td><td>0.1%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.1%</td></tr><tr><td>PC20</td><td>0.0%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.15</td><td>0.11</td></tr><tr><td>Fe(OTf)<sub>3</sub> #2</td><td>-0.07</td><td>0.22</td></tr><tr><td>#3</td><td>-0.16</td><td>0.11</td></tr><tr><td>#1</td><td>0.25</td><td>-0.01</td></tr><tr><td>Cu(OTf)<sub>2</sub> #2</td><td>0.23</td><td>0.16</td></tr><tr><td>#3</td><td>0.25</td><td>-0.04</td></tr><tr><td>#1</td><td>-0.11</td><td>-0.26</td></tr><tr><td>Bi(OTf)<sub>3</sub> #2</td><td>-0.01</td><td>0.36</td></tr><tr><td>#3</td><td>-0.20</td><td>-0.22</td></tr><tr><td>#1</td><td>0.23</td><td>0.19</td></tr><tr><td>Al(OTf)<sub>3</sub> #2</td><td>0.25</td><td>-0.01</td></tr><tr><td>#3</td><td>0.27</td><td>0.08</td></tr><tr><td>#1</td><td>0.18</td><td>-0.07</td></tr><tr><td>In(OTf)<sub>3</sub> #2</td><td>0.12</td><td>0.26</td></tr><tr><td>#3</td><td>0.18</td><td>0.18</td></tr><tr><td>#1</td><td>0.26</td><td>0.21</td></tr><tr><td>Dy(OTf)<sub>3</sub> #2</td><td>0.22</td><td>0.26</td></tr><tr><td>#3</td><td>-0.11</td><td>0.28</td></tr><tr><td>#1</td><td>0.30</td><td>0.00</td></tr><tr><td>Ce(OTf)<sub>3</sub> #2</td><td>0.23</td><td>-0.15</td></tr><tr><td>#3</td><td>0.28</td><td>-0.12</td></tr><tr><td>#1</td><td>0.19</td><td>-0.29</td></tr><tr><td>no triflate #2</td><td>0.19</td><td>-0.30</td></tr><tr><td>#3</td><td>0.17</td><td>-0.32</td></tr></tbody></table>

**Figure S15.** PCA on $\alpha_1$, for R is measured at different time interval [140s;149s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
<figure><img src="image_19.png" /></figure>

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>39.9%</td></tr><tr><td>PC2</td><td>27.0%</td></tr><tr><td>PC3</td><td>8.8%</td></tr><tr><td>PC4</td><td>6.4%</td></tr><tr><td>PC5</td><td>4.4%</td></tr><tr><td>PC6</td><td>3.4%</td></tr><tr><td>PC7</td><td>2.9%</td></tr><tr><td>PC8</td><td>2.1%</td></tr><tr><td>PC9</td><td>1.8%</td></tr><tr><td>PC10</td><td>1.2%</td></tr><tr><td>PC11</td><td>0.7%</td></tr><tr><td>PC12</td><td>0.4%</td></tr><tr><td>PC13</td><td>0.4%</td></tr><tr><td>PC14</td><td>0.3%</td></tr><tr><td>PC15</td><td>0.2%</td></tr><tr><td>PC16</td><td>0.1%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.0%</td></tr><tr><td>PC20</td><td>0.0%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.14</td><td>0.10</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>-0.09</td><td>0.19</td></tr><tr><td>#3</td><td>-0.16</td><td>0.09</td></tr><tr><td>#1</td><td>0.25</td><td>-0.01</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>0.24</td><td>0.15</td></tr><tr><td>#3</td><td>0.24</td><td>-0.08</td></tr><tr><td>#1</td><td>-0.09</td><td>-0.28</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>-0.03</td><td>0.35</td></tr><tr><td>#3</td><td>-0.18</td><td>-0.25</td></tr><tr><td>#1</td><td>0.22</td><td>0.19</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>0.25</td><td>-0.02</td></tr><tr><td>#3</td><td>0.28</td><td>0.08</td></tr><tr><td>#1</td><td>0.18</td><td>-0.04</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>0.11</td><td>0.28</td></tr><tr><td>#3</td><td>0.18</td><td>0.20</td></tr><tr><td>#1</td><td>0.25</td><td>0.22</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>0.20</td><td>0.28</td></tr><tr><td>#3</td><td>-0.10</td><td>0.27</td></tr><tr><td>#1</td><td>0.31</td><td>0.00</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>0.24</td><td>-0.14</td></tr><tr><td>#3</td><td>0.29</td><td>-0.11</td></tr><tr><td>#1</td><td>0.21</td><td>-0.28</td></tr><tr><td>no triflate</td><td>0.20</td><td>-0.28</td></tr><tr><td>#3</td><td>0.19</td><td>-0.31</td></tr></tbody></table>

Figure S16. PCA on α₁, for R is measured at different time interval [150s;159s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
<figure><img src="image_20.png" /></figure>

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>39.9%</td></tr><tr><td>PC2</td><td>26.8%</td></tr><tr><td>PC3</td><td>8.9%</td></tr><tr><td>PC4</td><td>6.3%</td></tr><tr><td>PC5</td><td>4.3%</td></tr><tr><td>PC6</td><td>3.4%</td></tr><tr><td>PC7</td><td>2.9%</td></tr><tr><td>PC8</td><td>2.0%</td></tr><tr><td>PC9</td><td>1.7%</td></tr><tr><td>PC10</td><td>1.4%</td></tr><tr><td>PC11</td><td>0.7%</td></tr><tr><td>PC12</td><td>0.5%</td></tr><tr><td>PC13</td><td>0.4%</td></tr><tr><td>PC14</td><td>0.3%</td></tr><tr><td>PC15</td><td>0.2%</td></tr><tr><td>PC16</td><td>0.1%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.0%</td></tr><tr><td>PC20</td><td>0.0%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

<table><thead><tr><th></th><th colspan="2">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.14</td><td>0.07</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.09</td><td>#3 0.18</td></tr><tr><td></td><td>#2 -0.17</td><td>#3 0.07</td></tr><tr><td></td><td>#1 0.25</td><td>#2 -0.01</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 0.23</td><td>#3 0.17</td></tr><tr><td></td><td>#2 0.24</td><td>#3 -0.09</td></tr><tr><td></td><td>#1 -0.07</td><td>#2 -0.29</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 -0.03</td><td>#3 0.36</td></tr><tr><td></td><td>#3 -0.17</td><td>#2 -0.27</td></tr><tr><td></td><td>#1 0.22</td><td>#2 0.18</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 0.24</td><td>#3 -0.02</td></tr><tr><td></td><td>#2 0.28</td><td>#3 0.10</td></tr><tr><td></td><td>#1 0.17</td><td>#2 -0.03</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 0.12</td><td>#3 0.27</td></tr><tr><td></td><td>#2 0.18</td><td>#3 0.20</td></tr><tr><td></td><td>#1 0.25</td><td>#2 0.23</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 0.20</td><td>#3 0.28</td></tr><tr><td></td><td>#2 0.26</td><td>#3 -0.12</td></tr><tr><td></td><td>#1 0.31</td><td>#2 -0.01</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 0.24</td><td>#3 -0.13</td></tr><tr><td></td><td>#2 0.29</td><td>#3 -0.11</td></tr><tr><td></td><td>#1 0.21</td><td>#2 -0.28</td></tr><tr><td>no triflate</td><td>#2 0.21</td><td>#3 -0.28</td></tr><tr><td></td><td>#2 0.19</td><td>#3 -0.31</td></tr></tbody></table>

Figure S17. PCA on α₁, for R is measured at different time interval [160s;169s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
<figure><img src="image_21.png" /></figure>

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>39.7%</td></tr><tr><td>PC2</td><td>26.7%</td></tr><tr><td>PC3</td><td>9.2%</td></tr><tr><td>PC4</td><td>6.5%</td></tr><tr><td>PC5</td><td>4.5%</td></tr><tr><td>PC6</td><td>3.2%</td></tr><tr><td>PC7</td><td>2.9%</td></tr><tr><td>PC8</td><td>1.9%</td></tr><tr><td>PC9</td><td>1.7%</td></tr><tr><td>PC10</td><td>1.3%</td></tr><tr><td>PC11</td><td>0.7%</td></tr><tr><td>PC12</td><td>0.4%</td></tr><tr><td>PC13</td><td>0.3%</td></tr><tr><td>PC14</td><td>0.2%</td></tr><tr><td>PC15</td><td>0.1%</td></tr><tr><td>PC16</td><td>0.1%</td></tr><tr><td>PC17</td><td>0.1%</td></tr><tr><td>PC18</td><td>0.1%</td></tr><tr><td>PC19</td><td>0.0%</td></tr><tr><td>PC20</td><td>0.0%</td></tr><tr><td>PC21</td><td>0.0%</td></tr><tr><td>PC22</td><td>0.0%</td></tr><tr><td>PC23</td><td>0.0%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

<table><thead><tr><th></th><th colspan="2">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.13</td><td>0.04</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.09</td><td>#3 0.16</td></tr><tr><td></td><td>#2 -0.16</td><td>#3 0.05</td></tr><tr><td></td><td>#1 0.25</td><td>#3 0.00</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 0.22</td><td>#3 0.21</td></tr><tr><td></td><td>#2 0.24</td><td>#3 -0.10</td></tr><tr><td></td><td>#1 -0.05</td><td>#3 -0.30</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 -0.05</td><td>#3 0.35</td></tr><tr><td></td><td>#3 -0.15</td><td>#3 -0.28</td></tr><tr><td></td><td>#1 0.21</td><td>#3 0.20</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 0.24</td><td>#3 -0.03</td></tr><tr><td></td><td>#3 0.28</td><td>#3 0.10</td></tr><tr><td></td><td>#1 0.18</td><td>#3 -0.06</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 0.11</td><td>#3 0.26</td></tr><tr><td></td><td>#3 0.18</td><td>#3 0.21</td></tr><tr><td></td><td>#1 0.24</td><td>#3 0.25</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 0.19</td><td>#3 0.30</td></tr><tr><td></td><td>#3 -0.11</td><td>#3 0.26</td></tr><tr><td></td><td>#1 0.31</td><td>#3 0.00</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 0.25</td><td>#3 -0.12</td></tr><tr><td></td><td>#3 0.29</td><td>#3 -0.10</td></tr><tr><td></td><td>#1 0.23</td><td>#3 -0.25</td></tr><tr><td>no triflate</td><td>#2 0.23</td><td>#3 -0.26</td></tr><tr><td></td><td>#3 0.21</td><td>#3 -0.29</td></tr></tbody></table>

**Figure S18.** PCA on $\alpha_1$, for R is measured at different time interval [170s;179s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
<figure><img src="image_22.png" /></figure>

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>48.6%</td></tr><tr><td>PC2</td><td>11.2%</td></tr><tr><td>PC3</td><td>6.8%</td></tr><tr><td>PC4</td><td>6.3%</td></tr><tr><td>PC5</td><td>4.3%</td></tr><tr><td>PC6</td><td>4.1%</td></tr><tr><td>PC7</td><td>3.6%</td></tr><tr><td>PC8</td><td>2.9%</td></tr><tr><td>PC9</td><td>2.4%</td></tr><tr><td>PC10</td><td>1.8%</td></tr><tr><td>PC11</td><td>1.5%</td></tr><tr><td>PC12</td><td>1.3%</td></tr><tr><td>PC13</td><td>0.9%</td></tr><tr><td>PC14</td><td>0.9%</td></tr><tr><td>PC15</td><td>0.7%</td></tr><tr><td>PC16</td><td>0.7%</td></tr><tr><td>PC17</td><td>0.6%</td></tr><tr><td>PC18</td><td>0.3%</td></tr><tr><td>PC19</td><td>0.3%</td></tr><tr><td>PC20</td><td>0.2%</td></tr><tr><td>PC21</td><td>0.2%</td></tr><tr><td>PC22</td><td>0.1%</td></tr><tr><td>PC23</td><td>0.1%</td></tr><tr><td>PC24</td><td>0.1%</td></tr></tbody></table>

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>0.24</td><td>0.10</td></tr><tr><td>Fe(OTf)<sub>3</sub> #2</td><td>0.27</td><td>0.07</td></tr><tr><td>#3</td><td>0.25</td><td>0.03</td></tr><tr><td>#1</td><td>0.19</td><td>-0.13</td></tr><tr><td>Cu(OTf)<sub>2</sub> #2</td><td>0.24</td><td>0.08</td></tr><tr><td>#3</td><td>0.23</td><td>0.21</td></tr><tr><td>#1</td><td>0.18</td><td>0.36</td></tr><tr><td>Bi(OTf)<sub>3</sub> #2</td><td>0.25</td><td>-0.19</td></tr><tr><td>#3</td><td>0.23</td><td>0.22</td></tr><tr><td>#1</td><td>0.25</td><td>-0.08</td></tr><tr><td>Al(OTf)<sub>3</sub> #2</td><td>0.18</td><td>-0.03</td></tr><tr><td>#3</td><td>0.26</td><td>0.06</td></tr><tr><td>#1</td><td>0.21</td><td>-0.09</td></tr><tr><td>ln(OTf)<sub>3</sub> #2</td><td>0.21</td><td>0.09</td></tr><tr><td>#3</td><td>0.21</td><td>0.01</td></tr><tr><td>#1</td><td>0.18</td><td>-0.19</td></tr><tr><td>Dy(OTf)<sub>3</sub> #2</td><td>0.23</td><td>-0.13</td></tr><tr><td>#3</td><td>0.18</td><td>0.14</td></tr><tr><td>#1</td><td>0.09</td><td>-0.44</td></tr><tr><td>Ce(OTf)<sub>3</sub> #2</td><td>0.07</td><td>-0.43</td></tr><tr><td>#3</td><td>0.20</td><td>-0.35</td></tr><tr><td>#1</td><td>-0.12</td><td>-0.27</td></tr><tr><td>no triflate #2</td><td>-0.20</td><td>-0.01</td></tr><tr><td>#3</td><td>0.07</td><td>-0.15</td></tr></tbody></table>

Figure S19. PCA on α₂, for R is measured at different time interval [1s;9s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_23.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>57.9%</td></tr><tr><td>PC2</td><td>13.8%</td></tr><tr><td>PC3</td><td>6.7%</td></tr><tr><td>PC4</td><td>4.9%</td></tr><tr><td>PC5</td><td>2.9%</td></tr><tr><td>PC6</td><td>2.6%</td></tr><tr><td>PC7</td><td>2.1%</td></tr><tr><td>PC8</td><td>1.9%</td></tr><tr><td>PC9</td><td>1.5%</td></tr><tr><td>PC10</td><td>1.1%</td></tr><tr><td>PC11</td><td>1.0%</td></tr><tr><td>PC12</td><td>0.7%</td></tr><tr><td>PC13</td><td>0.6%</td></tr><tr><td>PC14</td><td>0.5%</td></tr><tr><td>PC15</td><td>0.4%</td></tr><tr><td>PC16</td><td>0.4%</td></tr><tr><td>PC17</td><td>0.3%</td></tr><tr><td>PC18</td><td>0.3%</td></tr><tr><td>PC19</td><td>0.2%</td></tr><tr><td>PC20</td><td>0.1%</td></tr><tr><td>PC21</td><td>0.1%</td></tr><tr><td>PC22</td><td>0.1%</td></tr><tr><td>PC23</td><td>0.1%</td></tr><tr><td>PC24</td><td>0.0%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>0.20</td><td>0.15</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>0.24</td><td>0.14</td></tr><tr><td>#3</td><td>0.23</td><td>0.14</td></tr><tr><td>#1</td><td>0.20</td><td>-0.18</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>0.24</td><td>0.02</td></tr><tr><td>#3</td><td>0.21</td><td>0.12</td></tr><tr><td>#1</td><td>0.18</td><td>0.35</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>0.21</td><td>-0.15</td></tr><tr><td>#3</td><td>0.18</td><td>0.30</td></tr><tr><td>#1</td><td>0.25</td><td>-0.06</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>0.24</td><td>-0.06</td></tr><tr><td>#3</td><td>0.26</td><td>0.04</td></tr><tr><td>#1</td><td>0.21</td><td>-0.11</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>0.25</td><td>0.00</td></tr><tr><td>#3</td><td>0.21</td><td>0.02</td></tr><tr><td>#1</td><td>0.20</td><td>-0.20</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>0.24</td><td>-0.12</td></tr><tr><td>#3</td><td>0.17</td><td>0.15</td></tr><tr><td>#1</td><td>0.15</td><td>-0.38</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>0.09</td><td>-0.37</td></tr><tr><td>#3</td><td>0.19</td><td>-0.33</td></tr><tr><td>#1</td><td>-0.12</td><td>-0.33</td></tr><tr><td>no triflate</td><td>-0.23</td><td>-0.18</td></tr><tr><td>#3</td><td>0.06</td><td>-0.17</td></tr></tbody></table>

**Figure S20.** PCA on $\alpha_2$, for R is measured at different time interval [10s;19s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_24.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>28.6%</td></tr><tr><td>PC2</td><td>16.9%</td></tr><tr><td>PC3</td><td>7.3%</td></tr><tr><td>PC4</td><td>5.9%</td></tr><tr><td>PC5</td><td>5.3%</td></tr><tr><td>PC6</td><td>5.1%</td></tr><tr><td>PC7</td><td>4.7%</td></tr><tr><td>PC8</td><td>4.1%</td></tr><tr><td>PC9</td><td>3.0%</td></tr><tr><td>PC10</td><td>2.7%</td></tr><tr><td>PC11</td><td>2.6%</td></tr><tr><td>PC12</td><td>2.3%</td></tr><tr><td>PC13</td><td>2.1%</td></tr><tr><td>PC14</td><td>1.8%</td></tr><tr><td>PC15</td><td>1.6%</td></tr><tr><td>PC16</td><td>1.5%</td></tr><tr><td>PC17</td><td>1.3%</td></tr><tr><td>PC18</td><td>1.0%</td></tr><tr><td>PC19</td><td>0.8%</td></tr><tr><td>PC20</td><td>0.5%</td></tr><tr><td>PC21</td><td>0.3%</td></tr><tr><td>PC22</td><td>0.3%</td></tr><tr><td>PC23</td><td>0.2%</td></tr><tr><td>PC24</td><td>0.1%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.11</td><td>0.33</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.17</td><td>0.29</td></tr><tr><td></td><td>#3 -0.21</td><td>0.25</td></tr><tr><td></td><td>#1 0.26</td><td>-0.01</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 0.24</td><td>0.09</td></tr><tr><td></td><td>#3 0.14</td><td>0.06</td></tr><tr><td></td><td>#1 0.04</td><td>0.39</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 0.05</td><td>-0.06</td></tr><tr><td></td><td>#3 -0.13</td><td>0.40</td></tr><tr><td></td><td>#1 0.24</td><td>0.09</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 0.19</td><td>0.13</td></tr><tr><td></td><td>#3 0.26</td><td>0.20</td></tr><tr><td></td><td>#1 0.07</td><td>0.18</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 0.07</td><td>0.34</td></tr><tr><td></td><td>#3 0.13</td><td>-0.12</td></tr><tr><td></td><td>#1 0.35</td><td>0.05</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 0.33</td><td>-0.01</td></tr><tr><td></td><td>#3 0.18</td><td>0.06</td></tr><tr><td></td><td>#1 0.24</td><td>-0.27</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 0.28</td><td>-0.05</td></tr><tr><td></td><td>#3 0.24</td><td>-0.01</td></tr><tr><td></td><td>#1 0.16</td><td>0.13</td></tr><tr><td>no triflate</td><td>#2 0.24</td><td>0.16</td></tr><tr><td></td><td>#3 0.12</td><td>0.24</td></tr></tbody></table>

**Figure S21.** PCA on $\alpha_2$, for R is measured at different time interval [20s;29s] | **a**, PCA scores with 95% confidence ellipsoids. **b**, Individual variance for the different PC. **c**, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_25.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>27.1%</td></tr><tr><td>PC2</td><td>18.6%</td></tr><tr><td>PC3</td><td>6.9%</td></tr><tr><td>PC4</td><td>6.0%</td></tr><tr><td>PC5</td><td>5.7%</td></tr><tr><td>PC6</td><td>4.8%</td></tr><tr><td>PC7</td><td>4.0%</td></tr><tr><td>PC8</td><td>3.6%</td></tr><tr><td>PC9</td><td>3.4%</td></tr><tr><td>PC10</td><td>2.9%</td></tr><tr><td>PC11</td><td>2.8%</td></tr><tr><td>PC12</td><td>2.5%</td></tr><tr><td>PC13</td><td>2.1%</td></tr><tr><td>PC14</td><td>1.9%</td></tr><tr><td>PC15</td><td>1.6%</td></tr><tr><td>PC16</td><td>1.4%</td></tr><tr><td>PC17</td><td>1.1%</td></tr><tr><td>PC18</td><td>0.8%</td></tr><tr><td>PC19</td><td>0.7%</td></tr><tr><td>PC20</td><td>0.6%</td></tr><tr><td>PC21</td><td>0.5%</td></tr><tr><td>PC22</td><td>0.4%</td></tr><tr><td>PC23</td><td>0.3%</td></tr><tr><td>PC24</td><td>0.2%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.10</td><td>0.34</td></tr><tr><td>Fe(OTf)<sub>3</sub> #2</td><td>-0.05</td><td>0.36</td></tr><tr><td>#3</td><td>-0.08</td><td>0.39</td></tr><tr><td>#1</td><td>-0.23</td><td>-0.09</td></tr><tr><td>Cu(OTf)<sub>2</sub> #2</td><td>-0.17</td><td>0.03</td></tr><tr><td>#3</td><td>-0.16</td><td>0.13</td></tr><tr><td>#1</td><td>-0.19</td><td>0.24</td></tr><tr><td>Bi(OTf)<sub>3</sub> #2</td><td>0.07</td><td>0.17</td></tr><tr><td>#3</td><td>-0.09</td><td>0.39</td></tr><tr><td>#1</td><td>-0.24</td><td>0.04</td></tr><tr><td>Al(OTf)<sub>3</sub> #2</td><td>-0.28</td><td>0.13</td></tr><tr><td>#3</td><td>-0.24</td><td>-0.08</td></tr><tr><td>#1</td><td>-0.06</td><td>0.16</td></tr><tr><td>In(OTf)<sub>3</sub> #2</td><td>-0.11</td><td>0.21</td></tr><tr><td>#3</td><td>-0.23</td><td>-0.02</td></tr><tr><td>#1</td><td>-0.30</td><td>-0.18</td></tr><tr><td>Dy(OTf)<sub>3</sub> #2</td><td>-0.21</td><td>-0.23</td></tr><tr><td>#3</td><td>-0.12</td><td>0.03</td></tr><tr><td>#1</td><td>-0.20</td><td>-0.31</td></tr><tr><td>Ce(OTf)<sub>3</sub> #2</td><td>-0.30</td><td>-0.20</td></tr><tr><td>#3</td><td>-0.28</td><td>-0.08</td></tr><tr><td>#1</td><td>-0.24</td><td>0.03</td></tr><tr><td>no triflate #2</td><td>-0.25</td><td>-0.02</td></tr><tr><td>#3</td><td>-0.28</td><td>0.11</td></tr></tbody></table>

**Figure S22.** PCA on $\alpha_2$, for R is measured at different time interval [30s;39s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_26.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>29.5%</td></tr><tr><td>PC2</td><td>12.5%</td></tr><tr><td>PC3</td><td>8.7%</td></tr><tr><td>PC4</td><td>6.0%</td></tr><tr><td>PC5</td><td>5.9%</td></tr><tr><td>PC6</td><td>5.2%</td></tr><tr><td>PC7</td><td>4.9%</td></tr><tr><td>PC8</td><td>3.6%</td></tr><tr><td>PC9</td><td>3.2%</td></tr><tr><td>PC10</td><td>3.1%</td></tr><tr><td>PC11</td><td>2.5%</td></tr><tr><td>PC12</td><td>2.1%</td></tr><tr><td>PC13</td><td>1.8%</td></tr><tr><td>PC14</td><td>1.7%</td></tr><tr><td>PC15</td><td>1.6%</td></tr><tr><td>PC16</td><td>1.4%</td></tr><tr><td>PC17</td><td>1.3%</td></tr><tr><td>PC18</td><td>1.0%</td></tr><tr><td>PC19</td><td>0.9%</td></tr><tr><td>PC20</td><td>0.8%</td></tr><tr><td>PC21</td><td>0.7%</td></tr><tr><td>PC22</td><td>0.6%</td></tr><tr><td>PC23</td><td>0.4%</td></tr><tr><td>PC24</td><td>0.3%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.18</td><td>0.14</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.24</td><td>#3 -0.20</td></tr><tr><td>#1</td><td>-0.14</td><td>0.16</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.09</td><td>#3 -0.12</td></tr><tr><td>#1</td><td>-0.23</td><td>-0.31</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 0.17</td><td>#3 -0.13</td></tr><tr><td>#1</td><td>-0.16</td><td>-0.14</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.26</td><td>#3 -0.22</td></tr><tr><td>#1</td><td>-0.01</td><td>0.13</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.18</td><td>#3 -0.12</td></tr><tr><td>#1</td><td>-0.25</td><td>0.20</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.14</td><td>#3 -0.01</td></tr><tr><td>#1</td><td>-0.24</td><td>0.25</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.31</td><td>#3 -0.28</td></tr><tr><td>#1</td><td>-0.25</td><td>-0.05</td></tr><tr><td>no triflate</td><td>#2 -0.29</td><td>#3 -0.27</td></tr></tbody></table>

**Figure S23.** PCA on $\alpha_2$, for R is measured at different time interval [40s;49s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_27.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>31.0%</td></tr><tr><td>PC2</td><td>9.1%</td></tr><tr><td>PC3</td><td>6.8%</td></tr><tr><td>PC4</td><td>6.3%</td></tr><tr><td>PC5</td><td>5.7%</td></tr><tr><td>PC6</td><td>5.2%</td></tr><tr><td>PC7</td><td>4.8%</td></tr><tr><td>PC8</td><td>4.1%</td></tr><tr><td>PC9</td><td>3.9%</td></tr><tr><td>PC10</td><td>3.1%</td></tr><tr><td>PC11</td><td>2.8%</td></tr><tr><td>PC12</td><td>2.6%</td></tr><tr><td>PC13</td><td>2.3%</td></tr><tr><td>PC14</td><td>2.2%</td></tr><tr><td>PC15</td><td>2.1%</td></tr><tr><td>PC16</td><td>1.7%</td></tr><tr><td>PC17</td><td>1.4%</td></tr><tr><td>PC18</td><td>1.2%</td></tr><tr><td>PC19</td><td>0.9%</td></tr><tr><td>PC20</td><td>0.8%</td></tr><tr><td>PC21</td><td>0.8%</td></tr><tr><td>PC22</td><td>0.5%</td></tr><tr><td>PC23</td><td>0.4%</td></tr><tr><td>PC24</td><td>0.3%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.18</td><td>0.09</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.32</td><td>#3 -0.19</td></tr><tr><td>#1</td><td>-0.07</td><td>0.08</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 0.01</td><td>#3 -0.09</td></tr><tr><td>#1</td><td>-0.21</td><td>0.44</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 0.21</td><td>#3 -0.15</td></tr><tr><td>#1</td><td>-0.21</td><td>-0.06</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.19</td><td>#3 -0.23</td></tr><tr><td>#1</td><td>-0.13</td><td>0.14</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.17</td><td>#3 -0.17</td></tr><tr><td>#1</td><td>-0.22</td><td>-0.24</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.14</td><td>#3 -0.11</td></tr><tr><td>#1</td><td>-0.24</td><td>-0.22</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.31</td><td>#3 -0.27</td></tr><tr><td>#1</td><td>-0.23</td><td>0.06</td></tr><tr><td>no triflate</td><td>#2 -0.27</td><td>#3 -0.26</td></tr></tbody></table>

Figure S24. PCA on α₂, for R is measured at different time interval [50s;59s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_28.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>27.7%</td></tr><tr><td>PC2</td><td>11.9%</td></tr><tr><td>PC3</td><td>7.0%</td></tr><tr><td>PC4</td><td>6.1%</td></tr><tr><td>PC5</td><td>5.8%</td></tr><tr><td>PC6</td><td>5.0%</td></tr><tr><td>PC7</td><td>4.3%</td></tr><tr><td>PC8</td><td>4.1%</td></tr><tr><td>PC9</td><td>3.7%</td></tr><tr><td>PC10</td><td>3.4%</td></tr><tr><td>PC11</td><td>3.2%</td></tr><tr><td>PC12</td><td>2.9%</td></tr><tr><td>PC13</td><td>2.7%</td></tr><tr><td>PC14</td><td>2.4%</td></tr><tr><td>PC15</td><td>1.9%</td></tr><tr><td>PC16</td><td>1.8%</td></tr><tr><td>PC17</td><td>1.3%</td></tr><tr><td>PC18</td><td>1.3%</td></tr><tr><td>PC19</td><td>1.0%</td></tr><tr><td>PC20</td><td>0.9%</td></tr><tr><td>PC21</td><td>0.6%</td></tr><tr><td>PC22</td><td>0.5%</td></tr><tr><td>PC23</td><td>0.4%</td></tr><tr><td>PC24</td><td>0.2%</td></tr></tbody></table>

c)

<table><thead><tr><th></th><th colspan="2">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.24</td><td>0.01</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.32</td><td>#3 -0.11</td></tr><tr><td>#1</td><td>-0.06</td><td>-0.10</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.12</td><td>#3 -0.06</td></tr><tr><td>#1</td><td>-0.22</td><td>0.42</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 0.23</td><td>#3 -0.14</td></tr><tr><td>#1</td><td>-0.13</td><td>-0.21</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.18</td><td>#3 -0.21</td></tr><tr><td>#1</td><td>-0.04</td><td>0.07</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.06</td><td>#3 -0.19</td></tr><tr><td>#1</td><td>-0.21</td><td>-0.27</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.18</td><td>#3 -0.05</td></tr><tr><td>#1</td><td>-0.31</td><td>-0.15</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.34</td><td>#3 -0.29</td></tr><tr><td>#1</td><td>-0.26</td><td>0.04</td></tr><tr><td>no triflate</td><td>#2 -0.27</td><td>#3 -0.20</td></tr></tbody></table>

**Figure S25.** PCA on $\alpha_2$, for R is measured at different time interval [60s;69s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_29.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>26.1%</td></tr><tr><td>PC2</td><td>10.5%</td></tr><tr><td>PC3</td><td>7.0%</td></tr><tr><td>PC4</td><td>6.4%</td></tr><tr><td>PC5</td><td>6.0%</td></tr><tr><td>PC6</td><td>5.6%</td></tr><tr><td>PC7</td><td>4.7%</td></tr><tr><td>PC8</td><td>4.6%</td></tr><tr><td>PC9</td><td>4.4%</td></tr><tr><td>PC10</td><td>3.7%</td></tr><tr><td>PC11</td><td>3.0%</td></tr><tr><td>PC12</td><td>2.6%</td></tr><tr><td>PC13</td><td>2.5%</td></tr><tr><td>PC14</td><td>2.2%</td></tr><tr><td>PC15</td><td>1.8%</td></tr><tr><td>PC16</td><td>1.7%</td></tr><tr><td>PC17</td><td>1.5%</td></tr><tr><td>PC18</td><td>1.4%</td></tr><tr><td>PC19</td><td>1.2%</td></tr><tr><td>PC20</td><td>0.9%</td></tr><tr><td>PC21</td><td>0.7%</td></tr><tr><td>PC22</td><td>0.7%</td></tr><tr><td>PC23</td><td>0.6%</td></tr><tr><td>PC24</td><td>0.2%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.19</td><td>0.33</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.25</td><td>#3 -0.07</td></tr><tr><td></td><td>#2 -0.01</td><td>#3 -0.31</td></tr><tr><td></td><td>#3 -0.17</td><td>#3 0.14</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.14</td><td>#3 -0.03</td></tr><tr><td></td><td>#2 -0.03</td><td>#3 0.00</td></tr><tr><td></td><td>#3 -0.29</td><td>#3 0.34</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 0.12</td><td>#3 -0.19</td></tr><tr><td></td><td>#3 -0.19</td><td>#3 0.28</td></tr><tr><td></td><td>#3 -0.14</td><td>#3 -0.21</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.18</td><td>#3 -0.29</td></tr><tr><td></td><td>#2 -0.29</td><td>#3 0.09</td></tr><tr><td></td><td>#3 -0.02</td><td>#3 0.26</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.05</td><td>#3 -0.21</td></tr><tr><td></td><td>#2 -0.21</td><td>#3 0.06</td></tr><tr><td></td><td>#3 -0.17</td><td>#3 -0.36</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.05</td><td>#3 -0.11</td></tr><tr><td></td><td>#2 -0.11</td><td>#3 -0.06</td></tr><tr><td></td><td>#3 -0.34</td><td>#3 -0.15</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.33</td><td>#3 -0.29</td></tr><tr><td></td><td>#2 -0.29</td><td>#3 -0.30</td></tr><tr><td></td><td>#3 -0.30</td><td>#3 0.06</td></tr><tr><td>no triflate</td><td>#2 -0.17</td><td>#3 -0.25</td></tr><tr><td></td><td>#2 -0.25</td><td>#3 0.12</td></tr></tbody></table>

**Figure S27.** PCA on $\alpha_2$, for R is measured at different time interval [80s;89s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
<figure><img src="image_30.png" /></figure>

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>23.6%</td></tr><tr><td>PC2</td><td>9.0%</td></tr><tr><td>PC3</td><td>8.2%</td></tr><tr><td>PC4</td><td>6.7%</td></tr><tr><td>PC5</td><td>6.0%</td></tr><tr><td>PC6</td><td>5.6%</td></tr><tr><td>PC7</td><td>5.1%</td></tr><tr><td>PC8</td><td>4.9%</td></tr><tr><td>PC9</td><td>3.9%</td></tr><tr><td>PC10</td><td>3.7%</td></tr><tr><td>PC11</td><td>3.4%</td></tr><tr><td>PC12</td><td>2.9%</td></tr><tr><td>PC13</td><td>2.8%</td></tr><tr><td>PC14</td><td>2.4%</td></tr><tr><td>PC15</td><td>2.0%</td></tr><tr><td>PC16</td><td>1.8%</td></tr><tr><td>PC17</td><td>1.5%</td></tr><tr><td>PC18</td><td>1.5%</td></tr><tr><td>PC19</td><td>1.3%</td></tr><tr><td>PC20</td><td>1.0%</td></tr><tr><td>PC21</td><td>1.0%</td></tr><tr><td>PC22</td><td>0.6%</td></tr><tr><td>PC23</td><td>0.6%</td></tr><tr><td>PC24</td><td>0.2%</td></tr></tbody></table>

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.21</td><td>-0.30</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.25</td><td>#3 0.01</td></tr><tr><td></td><td>#2 -0.19</td><td>#3 0.20</td></tr><tr><td></td><td>#3 -0.07</td><td>#3 -0.11</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.01</td><td>#3 -0.06</td></tr><tr><td></td><td>#3 -0.14</td><td>#3 -0.34</td></tr><tr><td></td><td>#3 -0.29</td><td>#3 -0.32</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 0.12</td><td>#3 -0.22</td></tr><tr><td></td><td>#3 -0.29</td><td>#3 -0.31</td></tr><tr><td></td><td>#3 -0.19</td><td>#3 -0.13</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.17</td><td>#3 -0.21</td></tr><tr><td></td><td>#3 -0.21</td><td>#3 0.01</td></tr><tr><td></td><td>#3 -0.03</td><td>#3 0.06</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.02</td><td>#3 -0.26</td></tr><tr><td></td><td>#3 -0.14</td><td>#3 -0.04</td></tr><tr><td></td><td>#3 -0.17</td><td>#3 0.07</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.09</td><td>#3 -0.07</td></tr><tr><td></td><td>#3 0.00</td><td>#3 -0.20</td></tr><tr><td></td><td>#3 -0.36</td><td>#3 0.15</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.35</td><td>#3 0.28</td></tr><tr><td></td><td>#3 -0.31</td><td>#3 0.27</td></tr><tr><td></td><td>#3 -0.25</td><td>#3 0.00</td></tr><tr><td>no triflate</td><td>#2 -0.25</td><td>#3 0.11</td></tr><tr><td></td><td>#3 -0.08</td><td>#3 0.36</td></tr></tbody></table>

**Figure S28.** PCA on $\alpha_2$, for R is measured at different time interval [90s;99s] | **a**, PCA scores with 95% confidence ellipsoids. **b**, Individual variance for the different PC. **c**, PCA loadings of the different sensing elements' response for PC1 and PC2.
<figure><img src="image_31.png" /></figure>

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>27.9%</td></tr><tr><td>PC2</td><td>8.7%</td></tr><tr><td>PC3</td><td>7.6%</td></tr><tr><td>PC4</td><td>6.2%</td></tr><tr><td>PC5</td><td>6.0%</td></tr><tr><td>PC6</td><td>5.3%</td></tr><tr><td>PC7</td><td>4.8%</td></tr><tr><td>PC8</td><td>4.3%</td></tr><tr><td>PC9</td><td>3.5%</td></tr><tr><td>PC10</td><td>3.4%</td></tr><tr><td>PC11</td><td>3.2%</td></tr><tr><td>PC12</td><td>3.1%</td></tr><tr><td>PC13</td><td>2.7%</td></tr><tr><td>PC14</td><td>2.4%</td></tr><tr><td>PC15</td><td>2.0%</td></tr><tr><td>PC16</td><td>1.6%</td></tr><tr><td>PC17</td><td>1.6%</td></tr><tr><td>PC18</td><td>1.5%</td></tr><tr><td>PC19</td><td>1.2%</td></tr><tr><td>PC20</td><td>1.1%</td></tr><tr><td>PC21</td><td>0.7%</td></tr><tr><td>PC22</td><td>0.6%</td></tr><tr><td>PC23</td><td>0.4%</td></tr><tr><td>PC24</td><td>0.1%</td></tr></tbody></table>

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.29</td><td>0.13</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.24</td><td>#3 0.03</td></tr><tr><td></td><td>#2 -0.20</td><td>#3 -0.31</td></tr><tr><td></td><td>#1 -0.06</td><td>#3 0.26</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.18</td><td>#3 0.10</td></tr><tr><td></td><td>#2 0.10</td><td>#3 -0.26</td></tr><tr><td></td><td>#1 -0.28</td><td>#3 0.17</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 0.17</td><td>#3 -0.31</td></tr><tr><td></td><td>#3 -0.27</td><td>#3 -0.03</td></tr><tr><td></td><td>#1 -0.16</td><td>#3 -0.24</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.17</td><td>#3 0.26</td></tr><tr><td></td><td>#2 -0.10</td><td>#3 0.19</td></tr><tr><td></td><td>#1 -0.08</td><td>#3 0.12</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 0.01</td><td>#3 -0.44</td></tr><tr><td></td><td>#2 -0.19</td><td>#3 0.15</td></tr><tr><td></td><td>#1 -0.21</td><td>#3 0.00</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.02</td><td>#3 -0.16</td></tr><tr><td></td><td>#2 0.04</td><td>#3 -0.20</td></tr><tr><td></td><td>#1 -0.32</td><td>#3 -0.16</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.31</td><td>#3 -0.21</td></tr><tr><td></td><td>#2 -0.28</td><td>#3 -0.26</td></tr><tr><td></td><td>#1 -0.28</td><td>#3 -0.10</td></tr><tr><td>no triflate</td><td>#2 -0.18</td><td>#3 -0.06</td></tr><tr><td></td><td>#2 -0.23</td><td>#3 0.09</td></tr></tbody></table>

Figure S29. PCA on $\alpha_2$, for R is measured at different time interval [100s;109s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_32.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>25.7%</td></tr><tr><td>PC2</td><td>10.1%</td></tr><tr><td>PC3</td><td>8.3%</td></tr><tr><td>PC4</td><td>6.3%</td></tr><tr><td>PC5</td><td>5.7%</td></tr><tr><td>PC6</td><td>5.0%</td></tr><tr><td>PC7</td><td>4.8%</td></tr><tr><td>PC8</td><td>4.4%</td></tr><tr><td>PC9</td><td>3.6%</td></tr><tr><td>PC10</td><td>3.6%</td></tr><tr><td>PC11</td><td>3.2%</td></tr><tr><td>PC12</td><td>3.0%</td></tr><tr><td>PC13</td><td>2.9%</td></tr><tr><td>PC14</td><td>2.5%</td></tr><tr><td>PC15</td><td>2.0%</td></tr><tr><td>PC16</td><td>1.9%</td></tr><tr><td>PC17</td><td>1.5%</td></tr><tr><td>PC18</td><td>1.4%</td></tr><tr><td>PC19</td><td>1.2%</td></tr><tr><td>PC20</td><td>1.1%</td></tr><tr><td>PC21</td><td>0.8%</td></tr><tr><td>PC22</td><td>0.5%</td></tr><tr><td>PC23</td><td>0.4%</td></tr><tr><td>PC24</td><td>0.1%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.16</td><td>0.21</td></tr><tr><td>Fe(OTf)<sub>3</sub> #2</td><td>-0.20</td><td>-0.09</td></tr><tr><td>#3</td><td>-0.05</td><td>0.16</td></tr><tr><td>#1</td><td>0.04</td><td>-0.03</td></tr><tr><td>Cu(OTf)<sub>2</sub> #2</td><td>-0.01</td><td>-0.22</td></tr><tr><td>#3</td><td>-0.30</td><td>0.03</td></tr><tr><td>#1</td><td>0.22</td><td>-0.31</td></tr><tr><td>Bi(OTf)<sub>3</sub> #2</td><td>-0.24</td><td>0.37</td></tr><tr><td>#3</td><td>-0.22</td><td>-0.36</td></tr><tr><td>#1</td><td>-0.23</td><td>0.02</td></tr><tr><td>Al(OTf)<sub>3</sub> #2</td><td>-0.18</td><td>-0.07</td></tr><tr><td>#3</td><td>-0.13</td><td>0.01</td></tr><tr><td>#1</td><td>-0.16</td><td>-0.13</td></tr><tr><td>In(OTf)<sub>3</sub> #2</td><td>-0.07</td><td>-0.03</td></tr><tr><td>#3</td><td>-0.01</td><td>0.10</td></tr><tr><td>#1</td><td>-0.03</td><td>0.46</td></tr><tr><td>Dy(OTf)<sub>3</sub> #2</td><td>-0.06</td><td>0.07</td></tr><tr><td>#3</td><td>-0.37</td><td>0.05</td></tr><tr><td>#1</td><td>-0.34</td><td>0.11</td></tr><tr><td>Ce(OTf)<sub>3</sub> #2</td><td>-0.30</td><td>0.26</td></tr><tr><td>#3</td><td>-0.27</td><td>0.25</td></tr><tr><td>#1</td><td>-0.17</td><td>0.22</td></tr><tr><td>no triflate #2</td><td>-0.20</td><td>-0.21</td></tr><tr><td>#3</td><td>-0.19</td><td>-0.16</td></tr></tbody></table>

**Figure S30.** PCA on $\alpha_2$, for R is measured at different time interval [110s;119s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_33.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>21.3%</td></tr><tr><td>PC2</td><td>9.9%</td></tr><tr><td>PC3</td><td>7.8%</td></tr><tr><td>PC4</td><td>7.0%</td></tr><tr><td>PC5</td><td>6.6%</td></tr><tr><td>PC6</td><td>5.6%</td></tr><tr><td>PC7</td><td>5.1%</td></tr><tr><td>PC8</td><td>4.6%</td></tr><tr><td>PC9</td><td>4.2%</td></tr><tr><td>PC10</td><td>3.8%</td></tr><tr><td>PC11</td><td>3.3%</td></tr><tr><td>PC12</td><td>3.1%</td></tr><tr><td>PC13</td><td>3.0%</td></tr><tr><td>PC14</td><td>2.6%</td></tr><tr><td>PC15</td><td>2.2%</td></tr><tr><td>PC16</td><td>2.1%</td></tr><tr><td>PC17</td><td>1.8%</td></tr><tr><td>PC18</td><td>1.8%</td></tr><tr><td>PC19</td><td>1.4%</td></tr><tr><td>PC20</td><td>1.0%</td></tr><tr><td>PC21</td><td>0.8%</td></tr><tr><td>PC22</td><td>0.4%</td></tr><tr><td>PC23</td><td>0.4%</td></tr><tr><td>PC24</td><td>0.1%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.10</td><td>0.38</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.15</td><td>#3 0.01</td></tr><tr><td></td><td>-0.27</td><td>0.04</td></tr><tr><td>#1</td><td>-0.05</td><td>0.29</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.10</td><td>#3 -0.06</td></tr><tr><td></td><td>-0.02</td><td>0.12</td></tr><tr><td>#1</td><td>-0.32</td><td>-0.27</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 0.22</td><td>#3 -0.02</td></tr><tr><td></td><td>-0.23</td><td>-0.45</td></tr><tr><td>#1</td><td>-0.07</td><td>-0.27</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.08</td><td>#3 -0.06</td></tr><tr><td></td><td>-0.13</td><td>-0.25</td></tr><tr><td>#1</td><td>-0.10</td><td>0.18</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.02</td><td>#3 -0.06</td></tr><tr><td></td><td>-0.24</td><td>0.15</td></tr><tr><td>#1</td><td>-0.13</td><td>0.44</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.08</td><td>#3 0.18</td></tr><tr><td></td><td>0.03</td><td>-0.13</td></tr><tr><td>#1</td><td>-0.40</td><td>0.05</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.38</td><td>#3 0.10</td></tr><tr><td></td><td>-0.31</td><td>0.13</td></tr><tr><td>#1</td><td>-0.20</td><td>-0.04</td></tr><tr><td>no triflate</td><td>#2 -0.30</td><td>#3 -0.09</td></tr><tr><td></td><td>-0.15</td><td>-0.05</td></tr></tbody></table>

**Figure S31.** PCA on $\alpha_2$, for R is measured at different time interval [120s;129s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_34.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>20.1%</td></tr><tr><td>PC2</td><td>8.5%</td></tr><tr><td>PC3</td><td>7.9%</td></tr><tr><td>PC4</td><td>6.6%</td></tr><tr><td>PC5</td><td>6.4%</td></tr><tr><td>PC6</td><td>6.1%</td></tr><tr><td>PC7</td><td>5.4%</td></tr><tr><td>PC8</td><td>4.9%</td></tr><tr><td>PC9</td><td>4.3%</td></tr><tr><td>PC10</td><td>4.1%</td></tr><tr><td>PC11</td><td>3.8%</td></tr><tr><td>PC12</td><td>3.4%</td></tr><tr><td>PC13</td><td>3.1%</td></tr><tr><td>PC14</td><td>2.6%</td></tr><tr><td>PC15</td><td>2.5%</td></tr><tr><td>PC16</td><td>2.1%</td></tr><tr><td>PC17</td><td>1.7%</td></tr><tr><td>PC18</td><td>1.6%</td></tr><tr><td>PC19</td><td>1.4%</td></tr><tr><td>PC20</td><td>1.2%</td></tr><tr><td>PC21</td><td>1.0%</td></tr><tr><td>PC22</td><td>0.7%</td></tr><tr><td>PC23</td><td>0.4%</td></tr><tr><td>PC24</td><td>0.1%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.07</td><td>0.35</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>-0.22</td><td>-0.37</td></tr><tr><td>#3</td><td>-0.10</td><td>-0.16</td></tr><tr><td>#1</td><td>-0.01</td><td>-0.21</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>-0.02</td><td>-0.01</td></tr><tr><td>#3</td><td>-0.08</td><td>0.14</td></tr><tr><td>#1</td><td>-0.29</td><td>-0.37</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>0.02</td><td>0.30</td></tr><tr><td>#3</td><td>-0.28</td><td>-0.30</td></tr><tr><td>#1</td><td>-0.12</td><td>0.03</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>-0.16</td><td>-0.20</td></tr><tr><td>#3</td><td>-0.14</td><td>-0.08</td></tr><tr><td>#1</td><td>-0.08</td><td>0.29</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>-0.12</td><td>0.02</td></tr><tr><td>#3</td><td>-0.12</td><td>0.04</td></tr><tr><td>#1</td><td>-0.04</td><td>0.26</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>-0.11</td><td>-0.07</td></tr><tr><td>#3</td><td>-0.03</td><td>0.04</td></tr><tr><td>#1</td><td>-0.42</td><td>0.09</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>-0.41</td><td>0.17</td></tr><tr><td>#3</td><td>-0.40</td><td>0.12</td></tr><tr><td>#1</td><td>-0.18</td><td>0.21</td></tr><tr><td>no triflate</td><td>-0.29</td><td>0.14</td></tr><tr><td>#3</td><td>-0.19</td><td>0.13</td></tr></tbody></table>

**Figure S32.** PCA on $\alpha_2$, for R is measured at different time interval [130s;139s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_35.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>25.5%</td></tr><tr><td>PC2</td><td>9.1%</td></tr><tr><td>PC3</td><td>8.0%</td></tr><tr><td>PC4</td><td>7.3%</td></tr><tr><td>PC5</td><td>5.8%</td></tr><tr><td>PC6</td><td>5.4%</td></tr><tr><td>PC7</td><td>4.6%</td></tr><tr><td>PC8</td><td>4.1%</td></tr><tr><td>PC9</td><td>3.8%</td></tr><tr><td>PC10</td><td>3.5%</td></tr><tr><td>PC11</td><td>3.2%</td></tr><tr><td>PC12</td><td>2.8%</td></tr><tr><td>PC13</td><td>2.7%</td></tr><tr><td>PC14</td><td>2.5%</td></tr><tr><td>PC15</td><td>2.4%</td></tr><tr><td>PC16</td><td>1.9%</td></tr><tr><td>PC17</td><td>1.6%</td></tr><tr><td>PC18</td><td>1.5%</td></tr><tr><td>PC19</td><td>1.4%</td></tr><tr><td>PC20</td><td>1.2%</td></tr><tr><td>PC21</td><td>0.7%</td></tr><tr><td>PC22</td><td>0.4%</td></tr><tr><td>PC23</td><td>0.4%</td></tr><tr><td>PC24</td><td>0.1%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.20</td><td>0.33</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.11</td><td>#3 0.01</td></tr><tr><td></td><td>#3 -0.10</td><td>-0.35</td></tr><tr><td></td><td>#1 -0.17</td><td>-0.14</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.12</td><td>#3 -0.10</td></tr><tr><td></td><td>#3 -0.09</td><td>0.26</td></tr><tr><td></td><td>#1 -0.31</td><td>-0.02</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 -0.03</td><td>-0.42</td></tr><tr><td></td><td>#3 -0.29</td><td>0.00</td></tr><tr><td></td><td>#1 -0.25</td><td>-0.09</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.19</td><td>#3 0.15</td></tr><tr><td></td><td>#3 -0.19</td><td>0.22</td></tr><tr><td></td><td>#1 -0.07</td><td>-0.01</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 0.06</td><td>#3 0.28</td></tr><tr><td></td><td>#3 -0.13</td><td>-0.26</td></tr><tr><td></td><td>#1 -0.07</td><td>0.41</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 0.07</td><td>-0.04</td></tr><tr><td></td><td>#3 -0.13</td><td>-0.27</td></tr><tr><td></td><td>#1 -0.35</td><td>0.04</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.33</td><td>#3 0.06</td></tr><tr><td></td><td>#3 -0.35</td><td>0.07</td></tr><tr><td></td><td>#1 -0.26</td><td>-0.11</td></tr><tr><td>no triflate</td><td>#2 -0.23</td><td>-0.06</td></tr><tr><td></td><td>#3 -0.20</td><td>-0.06</td></tr></tbody></table>

**Figure S33.** PCA on $\alpha_2$, for R is measured at different time interval [140s;149s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_36.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>21.8%</td></tr><tr><td>PC2</td><td>9.1%</td></tr><tr><td>PC3</td><td>8.3%</td></tr><tr><td>PC4</td><td>7.2%</td></tr><tr><td>PC5</td><td>6.0%</td></tr><tr><td>PC6</td><td>5.5%</td></tr><tr><td>PC7</td><td>4.9%</td></tr><tr><td>PC8</td><td>4.7%</td></tr><tr><td>PC9</td><td>4.2%</td></tr><tr><td>PC10</td><td>3.9%</td></tr><tr><td>PC11</td><td>3.7%</td></tr><tr><td>PC12</td><td>3.5%</td></tr><tr><td>PC13</td><td>2.8%</td></tr><tr><td>PC14</td><td>2.4%</td></tr><tr><td>PC15</td><td>2.1%</td></tr><tr><td>PC16</td><td>1.8%</td></tr><tr><td>PC17</td><td>1.7%</td></tr><tr><td>PC18</td><td>1.6%</td></tr><tr><td>PC19</td><td>1.5%</td></tr><tr><td>PC20</td><td>1.3%</td></tr><tr><td>PC21</td><td>1.0%</td></tr><tr><td>PC22</td><td>0.5%</td></tr><tr><td>PC23</td><td>0.4%</td></tr><tr><td>PC24</td><td>0.2%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.29</td><td>0.06</td></tr><tr><td>Fe(OTf)<sub>3</sub> #2</td><td>-0.23</td><td>-0.26</td></tr><tr><td>#3</td><td>-0.17</td><td>-0.36</td></tr><tr><td>#1</td><td>-0.16</td><td>-0.03</td></tr><tr><td>Cu(OTf)<sub>2</sub> #2</td><td>0.02</td><td>0.09</td></tr><tr><td>#3</td><td>-0.10</td><td>-0.01</td></tr><tr><td>#1</td><td>-0.33</td><td>-0.28</td></tr><tr><td>Bi(OTf)<sub>3</sub> #2</td><td>-0.04</td><td>-0.12</td></tr><tr><td>#3</td><td>-0.33</td><td>-0.24</td></tr><tr><td>#1</td><td>-0.10</td><td>-0.08</td></tr><tr><td>Al(OTf)<sub>3</sub> #2</td><td>-0.22</td><td>-0.14</td></tr><tr><td>#3</td><td>0.05</td><td>0.16</td></tr><tr><td>#1</td><td>0.03</td><td>0.06</td></tr><tr><td>In(OTf)<sub>3</sub> #2</td><td>-0.09</td><td>-0.20</td></tr><tr><td>#3</td><td>-0.03</td><td>-0.06</td></tr><tr><td>#1</td><td>-0.11</td><td>0.29</td></tr><tr><td>Dy(OTf)<sub>3</sub> #2</td><td>0.04</td><td>0.31</td></tr><tr><td>#3</td><td>-0.08</td><td>-0.18</td></tr><tr><td>#1</td><td>-0.36</td><td>0.22</td></tr><tr><td>Ce(OTf)<sub>3</sub> #2</td><td>-0.33</td><td>0.36</td></tr><tr><td>#3</td><td>-0.39</td><td>0.18</td></tr><tr><td>#1</td><td>-0.22</td><td>0.18</td></tr><tr><td>no triflate #2</td><td>-0.14</td><td>0.31</td></tr><tr><td>#3</td><td>-0.19</td><td>-0.01</td></tr></tbody></table>

**Figure S34.** PCA on $\alpha_2$, for R is measured at different time interval [150s;159s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_37.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>20.6%</td></tr><tr><td>PC2</td><td>8.4%</td></tr><tr><td>PC3</td><td>8.2%</td></tr><tr><td>PC4</td><td>7.0%</td></tr><tr><td>PC5</td><td>6.0%</td></tr><tr><td>PC6</td><td>5.4%</td></tr><tr><td>PC7</td><td>5.2%</td></tr><tr><td>PC8</td><td>5.0%</td></tr><tr><td>PC9</td><td>4.3%</td></tr><tr><td>PC10</td><td>3.8%</td></tr><tr><td>PC11</td><td>3.5%</td></tr><tr><td>PC12</td><td>3.1%</td></tr><tr><td>PC13</td><td>3.0%</td></tr><tr><td>PC14</td><td>2.6%</td></tr><tr><td>PC15</td><td>2.3%</td></tr><tr><td>PC16</td><td>2.2%</td></tr><tr><td>PC17</td><td>2.1%</td></tr><tr><td>PC18</td><td>1.8%</td></tr><tr><td>PC19</td><td>1.6%</td></tr><tr><td>PC20</td><td>1.4%</td></tr><tr><td>PC21</td><td>1.2%</td></tr><tr><td>PC22</td><td>0.6%</td></tr><tr><td>PC23</td><td>0.4%</td></tr><tr><td>PC24</td><td>0.3%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>0.18</td><td>-0.21</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>0.16</td><td>0.14</td></tr><tr><td>#3</td><td>0.23</td><td>0.30</td></tr><tr><td>#1</td><td>0.08</td><td>0.34</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>-0.09</td><td>-0.30</td></tr><tr><td>#3</td><td>0.13</td><td>-0.07</td></tr><tr><td>#1</td><td>0.34</td><td>0.01</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>0.09</td><td>0.12</td></tr><tr><td>#3</td><td>0.30</td><td>-0.08</td></tr><tr><td>#1</td><td>0.10</td><td>-0.32</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>0.17</td><td>-0.16</td></tr><tr><td>#3</td><td>0.15</td><td>0.18</td></tr><tr><td>#1</td><td>0.08</td><td>-0.04</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>0.08</td><td>-0.40</td></tr><tr><td>#3</td><td>0.13</td><td>-0.07</td></tr><tr><td>#1</td><td>0.07</td><td>-0.04</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>0.06</td><td>-0.20</td></tr><tr><td>#3</td><td>-0.02</td><td>-0.20</td></tr><tr><td>#1</td><td>0.39</td><td>-0.06</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>0.38</td><td>-0.14</td></tr><tr><td>#3</td><td>0.39</td><td>-0.10</td></tr><tr><td>#1</td><td>0.17</td><td>0.01</td></tr><tr><td>no triflate</td><td>0.20</td><td>0.37</td></tr><tr><td>#3</td><td>0.18</td><td>0.19</td></tr></tbody></table>

Figure S35. PCA on $\alpha_2$, for R is measured at different time interval [160s;169s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.
a)

<figure><img src="image_38.png" /></figure>

b)

<table><thead><tr><th></th><th>Variance</th></tr></thead><tbody><tr><td>PC1</td><td>18.9%</td></tr><tr><td>PC2</td><td>9.3%</td></tr><tr><td>PC3</td><td>8.0%</td></tr><tr><td>PC4</td><td>6.8%</td></tr><tr><td>PC5</td><td>6.6%</td></tr><tr><td>PC6</td><td>6.0%</td></tr><tr><td>PC7</td><td>5.2%</td></tr><tr><td>PC8</td><td>5.1%</td></tr><tr><td>PC9</td><td>4.3%</td></tr><tr><td>PC10</td><td>4.1%</td></tr><tr><td>PC11</td><td>3.9%</td></tr><tr><td>PC12</td><td>3.3%</td></tr><tr><td>PC13</td><td>3.1%</td></tr><tr><td>PC14</td><td>2.6%</td></tr><tr><td>PC15</td><td>2.2%</td></tr><tr><td>PC16</td><td>2.0%</td></tr><tr><td>PC17</td><td>1.8%</td></tr><tr><td>PC18</td><td>1.7%</td></tr><tr><td>PC19</td><td>1.4%</td></tr><tr><td>PC20</td><td>1.2%</td></tr><tr><td>PC21</td><td>1.0%</td></tr><tr><td>PC22</td><td>0.6%</td></tr><tr><td>PC23</td><td>0.5%</td></tr><tr><td>PC24</td><td>0.3%</td></tr></tbody></table>

c)

<table><thead><tr><th colspan="3">Loadings</th></tr><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>#1</td><td>-0.20</td><td>-0.06</td></tr><tr><td>Fe(OTf)<sub>3</sub></td><td>#2 -0.10</td><td>#3 0.40</td></tr><tr><td></td><td>#2 -0.17</td><td>#3 0.16</td></tr><tr><td></td><td>#1 -0.12</td><td>#3 0.33</td></tr><tr><td>Cu(OTf)<sub>2</sub></td><td>#2 -0.07</td><td>#3 -0.11</td></tr><tr><td></td><td>#3 -0.11</td><td>#3 0.02</td></tr><tr><td></td><td>#1 -0.33</td><td>#3 -0.18</td></tr><tr><td>Bi(OTf)<sub>3</sub></td><td>#2 -0.18</td><td>#3 -0.32</td></tr><tr><td></td><td>#3 -0.35</td><td>#3 -0.12</td></tr><tr><td></td><td>#1 -0.03</td><td>#3 -0.13</td></tr><tr><td>Al(OTf)<sub>3</sub></td><td>#2 -0.06</td><td>#3 -0.28</td></tr><tr><td></td><td>#3 -0.11</td><td>#3 -0.32</td></tr><tr><td></td><td>#1 0.03</td><td>#3 0.27</td></tr><tr><td>In(OTf)<sub>3</sub></td><td>#2 -0.10</td><td>#3 0.00</td></tr><tr><td></td><td>#3 0.01</td><td>#3 -0.33</td></tr><tr><td></td><td>#1 -0.14</td><td>#3 0.09</td></tr><tr><td>Dy(OTf)<sub>3</sub></td><td>#2 -0.14</td><td>#3 0.02</td></tr><tr><td></td><td>#3 0.07</td><td>#3 0.02</td></tr><tr><td></td><td>#1 -0.42</td><td>#3 0.02</td></tr><tr><td>Ce(OTf)<sub>3</sub></td><td>#2 -0.42</td><td>#3 0.11</td></tr><tr><td></td><td>#3 -0.40</td><td>#3 0.00</td></tr><tr><td></td><td>#1 -0.04</td><td>#3 -0.15</td></tr><tr><td>no triflate</td><td>#2 -0.06</td><td>#3 0.08</td></tr><tr><td></td><td>#3 -0.18</td><td>#3 0.34</td></tr></tbody></table>

**Figure S36.** PCA on $\alpha_2$, for R is measured at different time interval [170s;179s] | a, PCA scores with 95% confidence ellipsoids. b, Individual variance for the different PC. c, PCA loadings of the different sensing elements' response for PC1 and PC2.