# Oxygen effects on passive film evolution and metastable pitting in 316L stainless steel using SECCM

Leonardo B Coelho, Daniel Torres, Miguel Bernal, Loic Malet, Aleksei Makogon, Frédéric Kanoufi, Viacheslav Shkirskiy, Jon Ustarroz

► To cite this version:

Leonardo B Coelho, Daniel Torres, Miguel Bernal, Loic Malet, Aleksei Makogon, et al.. Oxygen effects on passive film evolution and metastable pitting in 316L stainless steel using SECCM. Corrosion Science, 2026, 263, pp.113708. 10.1016/j.corsci.2026.113708. hal-05519205

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## Oxygen effects on passive film evolution and metastable pitting in 316L stainless steel using SECCM

Leonardo B. Coelho¹,²,³*, Daniel Torres¹, Miguel Bernal¹, Loic Malet⁴, Aleksei Makogon⁵, Frédéric Kanoufi⁵, Viacheslav Shkirskiy⁵, Jon Ustarroz¹,²

¹ChemSIN – Chemistry of Surfaces, Interfaces and Nanomaterials, Université libre de Bruxelles (ULB), Brussels, Belgium

²Sustainable Materials Engineering (SUME), Research Lab of Electrochemical and Surface Engineering (SURF), Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels Belgium

³IAsto Ltda, Taió, 89190-000, Santa Catarina, Brazil

⁴4MAT, Université Libre de Bruxelles, CP194/03, avenue F.D. Roosevelt 50, 1050 Bruxelles, Belgium

⁵Université Paris Cité, ITODYS, CNRS, 15 Rue Jean Antoine de Baïf, Paris, 75013, France

*leonardo.bertolucci.coelho@ulb.be

## ABSTRACT

The corrosion behaviour of industrially-produced 316L stainless steel in 0.01 M NaCl solution was investigated using scanning electrochemical cell microscopy (SECCM) under potentiostatic conditions (+1.2 V vs Ag/AgCl, 60 s). Variations in micro-droplet footprint size during sequential SECCM measurements created an oxygen gradient at the metal-electrolyte interface, enabling systematic investigation of the effect of the oxygen supply rate, or aeration, on both general corrosion and metastable pitting kinetics. Chronoamperometric data from 205 individual measurements were analysed using custom quantitative descriptors and Linear Discriminant Analysis (LDA) of EBSD-derived crystallographic features. Manual classification of 91 transient events into two distinct categories – “peak” (metastable pitting) and “belly” (passive film thinning) - combined with data-driven analysis revealed previously hidden correlations. Higher aeration conditions (smaller droplets, 2–3 µm) produced 20% higher oxygen flux compared to lower aeration (larger droplets, ~8 µm), leading to increased initial dissolution currents and faster passive film formation. However, these rapidly-formed films exhibited persistently elevated passive current densities, while higher aeration conditions produced longer-duration belly-type transient events, revealing a trade-off between
passive film protectiveness and repassivation capability. Strong correlations between early-
time electrochemical descriptors and total corrosion charge suggest potential for rapid, non-
destructive corrosion susceptibility assessment. These findings reveal distinct relationships
between oxygen availability and corrosion behaviour.

Keywords

316L stainless steel; SECCM; Passive film; Metastable pitting; EBSD; Localized corrosion

INTRODUCTION

Stainless steel 316L is a widely utilised material in various corrosive environments owing to
its inherent ability to form a self-healing passive film that provides protection against
degradation[1]. This corrosion resistance is primarily attributed to the formation of a thin,
chromium-rich oxide layer on the steel's surface, which acts as a barrier against the diffusion
of ions between the alloy and the surrounding environment[2,3]. The passive film on stainless
steel typically exhibits a duplex structure, comprising an inner layer rich in chromium oxide
and an outer layer composed of iron oxides/hydroxides[4]. Despite this inherent protection,
localised corrosion, particularly the initiation and propagation of metastable pitting, remains a
significant concern for the long-term performance of 316L stainless steel components in
chloride-containing media[5,6].

The overall corrosion behaviour of 316L in sodium chloride solutions encompasses both a
general uniform dissolution process and the more localised phenomenon of pitting corrosion,
with the specific mechanisms and rates being influenced by factors such as chloride
concentration, temperature, pH, and the presence of dissolved oxygen[7]. Oxygen plays a
multifaceted role in the corrosion of 316L stainless steel in chloride solutions, acting both as a
promoter and inhibitor depending on specific conditions[4,7–11]. In open, aerated
environments, oxygen functions as a cathodic depolariser, facilitating the cathodic reduction
reactions that accompany anodic metal dissolution[8,9], while simultaneously being crucial
for the formation and maintenance of the protective chromium oxide layer that ensures
corrosion resistance[4,8,11]. The level of oxygen availability significantly impacts both the
composition and protective qualities of this passive film[12], leading to variations in oxide
and hydroxide content compared to de-aerated conditions[13]. Research has shown complex
relationships between oxygen concentration and electrochemical behaviour[4]: dissolved O₂
can facilitate re-oxidation of the metal surface and support passive film growth[14], but under
certain conditions it can potentially enhance oxidative dissolution under anodic polarisation.

Metastable pitting is recognised as a crucial early stage in the localised corrosion of passive
metals when exposed to aggressive environments such as chloride-containing
solutions[6,15,16]. This phenomenon involves the spontaneous nucleation and subsequent
growth, often followed by repassivation, of small corrosion pits on the metal surface. The
occurrence and characteristics of metastable pitting are known to be influenced by several
factors, including chloride concentration. Studies on other stainless steel grades have shown
that the frequency of metastable pitting events tends to decrease as the chloride concentration
is reduced, suggesting that the availability of aggressive ions plays a significant role in the
initiation process. Oxygen availability is another environmental parameter affecting
metastable pitting. Some research indicates that increased dissolved oxygen levels can lead to
a reduction in the frequency of metastable pitting events, although the precise mechanisms
behind this effect are not fully understood. Conversely, other studies suggest that oxygen can
promote the growth rate of these transient pits[17].

At later stages, when pits fail to repassivate, they transition into stable, propagating pits that
lead to material degradation[6,15]. Higher O₂ concentrations have been observed to increase
the pitting initiation potential but concurrently to decrease the repassivation potential (as
determined by cyclic voltammetry)[18,19]. This indicates that oxygen promotes passive film
formation but simultaneously impairs the ability of pits to heal. Indeed, a lower repassivation
potential implies that once initiated, pits can remain active at more cathodic conditions,
making them harder to heal. In confined or low-oxygen environments, stainless steels often
experience different corrosion behaviours, such as acidification leading to stable pitting[20].
The kinetics of the repassivation process - the rate at which a damaged passive film reforms
after local damage - are crucial in determining whether a transient pit will heal or evolve into
a stable one[6,18], and these dynamics are significantly influenced by oxygen
availability[11,17,21].

The material's microstructure also plays a significant role in influencing the corrosion
susceptibility of 316L stainless steel, particularly pitting[22-26]. Techniques like Electron
Backscatter Diffraction (EBSD) are instrumental in characterising the microstructural features
of the material, such as grain size, grain orientation, and the nature of grain boundaries, and in
correlating these features with localised corrosion behaviour[22-25]. Grain boundaries, for
instance, are often considered as potential sites for corrosion initiation due to their higher
energy and the possible segregation of alloying elements or impurities. Similarly, the
crystallographic orientation of individual grains can influence their electrochemical reactivity
and thus their corrosion behaviour [25,27]. At the microscale, specific structural features
create heterogeneities that significantly impact localised corrosion behaviour. It has been
demonstrated that different crystallographic orientations of austenitic stainless steel exhibit
different anodic dissolution rates under passive conditions[27]. For instance, using SECCM,
Yule et al. showed that for low-index planes of steel, the (101) grain facets had higher passive
current density (i.e. were more susceptible to corrosion) compared to (100) or (111) facets
[27]. Grain boundaries and inclusions (such as MnS in 316L[28]) can serve as initiation sites
for pitting as well, due to local galvanic or stress concentrations. Conversely, a microstructure
with larger grains (hence fewer grain boundaries) has been associated with improved pitting
resistance[12]. For example, additively manufactured 316L with large columnar grains showed significantly higher pitting potentials than conventional fine-grained 316L[12], presumably because the paucity of grain boundaries and the uniform orientation reduced vulnerable sites. These studies underline that correlating microstructure (e.g. via EBSD) with corrosion behaviour is essential for a mechanistic understanding of pitting susceptibility.

Scanning Electrochemical Cell Microscopy (SECCM) is a powerful and versatile micro-electrochemical technique that has emerged as a valuable tool for investigating localised electrochemical phenomena in corrosion science[27,29–33]. Unlike traditional bulk electrochemical methods that provide averaged information over a large surface area, SECCM allows for probing electrochemical activity with high spatial resolution, typically at the micro-or nanoscale[27,29,30]. The technique involves using a micro- or nanopipette filled with electrolyte solution, brought into contact with the sample surface to form a small, confined electrochemical cell defined by the meniscus of the electrolyte droplet at the pipette tip. This enables the measurement of current-voltage relationships or other electrochemical parameters from a very small area, providing insights into surface reactivity heterogeneity. SECCM allows hundreds of spatially resolved polarisation experiments on a metallographic surface, which can subsequently be mapped by EBSD to identify the crystallography at each tested location. A significant advantage is its ability to control the local environment within the micro-droplet, making it particularly well-suited for studying the effects of parameters like oxygen availability on corrosion processes[34]. This approach yields high-throughput data on how local surface properties (grain orientation, inclusions, etc.) affect corrosion metrics such as passive current density, metastable pit frequency, and pit stability[35]. In our previous work, we employed SECCM on 316L to generate large datasets of pit nucleation and growth behaviour, which were analysed statistically and with machine learning (ML) to extract characteristic “pitting descriptors”[36].
In this context, the present work employs SECCM to investigate the corrosion performance of
316L stainless steel in 0.01 M NaCl solution under varying oxygen supply rate (aeration)
conditions resulting from the sequence-dependent changes in micro-droplet footprint size
during sequential SECCM measurements (Fig. 1). This unintended variation allowed us to
investigate different passivation and repassivation scenarios and to gain a deeper
understanding of how oxygen availability influences both general dissolution processes and
the occurrence and kinetics of transient events associated with metastable pitting.
<figure><img src="image_2.png" /><figcaption>Fig. 1. Schematic illustration of oxygen availability modulation in SECCM measurements. Smaller droplets (~3 µm, right) have higher surface-to-volume ratios than larger droplets (~8 µm, left), enhancing oxygen mass transfer from ambient air to the metal-electrolyte interface and increasing local oxygen flux at the passive film. This geometric effect modulates the aeration conditions during electrochemical testing of 316L stainless steel.</figcaption></figure>

Subsequent EBSD mapping established a correlative link between the corrosion footprints and the local microstructural features, allowing for a direct investigation into how the material's inherent heterogeneity at the microscale contributes to variations in its corrosion susceptibility at the local level probed by SECCM.

We specifically focus on classifying the types of metastable pitting transients observed and analysing their kinetics (nucleation, growth, and repassivation rates) under different levels of aeration, using a data-driven framework that combines quantitative descriptors with expert classification. This approach aims to provide a refined understanding of how oxygen availability and passive film evolution interact to affect metastable pit behaviour on 316L, and how this interplay is modulated by the underlying microstructure. The analysis employs
Linear Discriminant Analysis (LDA)[37–42], a supervised machine learning algorithm used for classification and dimensionality reduction. LDA identifies a linear combination of features that best separates different classes of data by maximising the distance between class means while minimising within-class variance. In electrochemical corrosion studies, LDA can identify patterns and correlations within complex datasets and classify different types of corrosion behaviour based on extracted features[39].

# METHODS

## Materials

The specimens used in this study were 316L stainless steel plates supplied by Aperam (France) and surface treated by Packo Inox (Belgium). The industrial treatment involved electropolishing (bath temperature ≈ 65 °C, current densities between 20 and 40 A/dm²) followed by passivation and water rinsing[43]. Squared specimens (~5×5 cm²) were cut from the same plate (same industrial batch) using a lever cutter and then degreased in acetone under ultrasonic agitation for 10 min, followed by rinsing in demineralised water. The resulting surface finishes have been thoroughly investigated previously [43], demonstrating high homogeneity and reproducibility at the microscale.

## Localised Electrochemical Testing

Localised electrochemical measurements were performed using a scanning electrochemical cell microscopy (SECCM) setup. In SECCM, a droplet meniscus at the end of a capillary tip serves as a miniaturised electrochemical cell confined to a small area on the sample. Pulled borosilicate glass capillaries (with an Ag/AgCl quasi-reference counter electrode (QRCE) inserted) were filled with the test electrolyte (0.01 M NaCl solution). All experiments were conducted at room temperature (~25°C) in an unbuffered 0.01 M NaCl solution prepared with deionised water (18.2 MΩhm). This dilute chloride concentration was chosen to promote
metastable pitting events while avoiding immediate stable pit growth, thus allowing
observation of repassivation behaviour.

The SECCM experiments were carried out using a custom-built workstation. The sample
(WE) was mounted on a motorised XY stage, which enabled precise positioning (Nano-M250,
MadCityLabs), with the pipette mounted on a holder with an independent Z-direction
piezoelectric positioner (Nano-MET10, MadCityLabs). The position of the pipette relative to
the substrate was closely monitored with an optical camera. Electrochemical signals were
acquired with a high-sensitivity electrometer (1 nA/V), integrated with FPGA-based data
acquisition (PCIe-7856R FPGA card, National Instruments), and controlled through the
Warwick Electrochemical Scanning Probe Microscopy software (WEC-SPM,
www.Warwick.ac.UK/electrochemistry). To minimise perturbations from acoustic,
vibrational, thermal drift and electrical noise, the setup was installed on an optical table with
active vibration isolation and enclosed within a Faraday cage equipped with acoustic
insulation panels and aluminium heat sinks.

The droplet (meniscus) contact radius on the surface (the “footprint” area) was on the order of
a few tens of micrometres squared (a ~2–5 µm droplet diameter). The probe was rastered in a
grid pattern to test many distinct locations on the steel surface (i.e., hopping mode). At each
location, a potentiostatic polarisation was applied: the steel substrate was held at +1.20 V
versus Ag/AgCl (3 M KCl) for 60 s while current was recorded at a 10 kHz acquisition rate.
This potential was selected based on preliminary work[44] to be within the passivity range but
close to the breakdown potential of 316L stainless steel in 0.01 M NaCl. After each 60 s test,
the probe was lifted and moved to the next site.

All current measurements were normalised by the respective droplet footprint area to obtain
current densities (µA/cm²), enabling direct comparison across measurements despite the
variation in droplet size during sequential SECCM testing.
Electrochemical data analysis and descriptor extraction

The chronoamperometric (CA) responses were analysed using a data-driven approach to
quantify general corrosion behaviour and localised transient events (Fig. 2; see Fig. S1 for a
schematic overview of extracted parameters and event classifications). The current-time
response from each SECCM site was recorded. From these curves, we extracted several key
descriptors (illustrated schematically in Fig. S1): the initial current density peak upon stepping
to +1.20 V (associated with charging and oxidising the pre-existing film); the passive current
density (steady-state or baseline current at the end of 60 s, associated with general uniform
dissolution); and any transient current spikes corresponding to metastable localised corrosion
events.

All measured current densities represent the net result of competing dissolution (j_dissol) and
repassivation (j_repass) processes, where j_net = j_dissol - j_repass. While our approach cannot separate
these individual contributions, this framework is essential for interpreting current-time
behaviors.

Fig. 2 illustrates our analysis workflow and classification scheme: panel (a) shows representative examples of classified transient event types, panel (b) maps the spatial distribution of these events across the microstructure, and panel (c) demonstrates the signal processing approach used for quantitative analysis. The electrochemical characteristics of these different curve types are analysed in detail in the Results section.
<figure><img src="image_3.png" /><figcaption>a)</figcaption></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>b)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>c)</figcaption></figure>

<figure><img src="image_8.png" /></figure>
Fig. 2. Examples of chronoamperometric curves and classified events. (a) Representative chronoamperometric curves showing manually classified categories: prior_decay_activity, meta_decay, meta, belly (the latter three being transient events), and meta_major. Current densities are normalised by respective droplet footprint areas. (b) The spatial distribution of these manually classified event types across the analysed microstructure, with colored circles indicating event classifications at each SECCM footprint location. (c) Signal processing approach for transient charge quantification: (blue) raw original signal; (orange) smoothed signal using Savitzky-Golay filter; (green) baseline-subtracted signal with detected transient event; (magenta line) local baseline defined by connecting the flanking points around the transient; (purple shaded area) integral charge area of the transient.

We quantified several key descriptors from each chronoamperometric curve, with all current values normalised by footprint area to report current densities. For simplicity, all charge-related parameters (which represent charge density (µC/cm²) due to the area normalisation) will be referred to as “charge” throughout this manuscript.

**Charge component segregation:** the total anodic charge passed during each 60 s test was partitioned into three components: decay charge, transient event charges, and generalised corrosion charge. The decay charge was calculated as the integrated area between the initial current spike and a baseline extrapolated from the steady-state region (t > 0.4 s). Individual transient event charges were computed by integrating the current above local baselines for each detected event. The generalised corrosion charge was then obtained by subtracting both the decay charge and sum of transient charges from the total charge (see Fig. S2 for visual representation). Detailed algorithms for baseline determination and charge calculations are provided in the Supplementary Materials (see section “Detailed methodology for charge partitioning”).
**General dissolution current/charge:** we computed the average current density (or charge) associated with general uniform dissolution over the test duration. First, the total charge metrics was calculated as the total anodic charge passed in 60 s (integral of current vs time). Next, once the charges associated with the transient events and the decay charge were calculated, these were subtracted from the total charge to yield the “Charge – generalised corrosion” descriptor.

**Initial decay characteristics:** the very early portion of the current-time curve (first few seconds) shows a rapid decay from an initial current peak as the droplet establishes contact with the surface. We extracted the first current density measured immediately after test starting (initial j), the maximum current density (decay max j), and the decay charge (integral of current jump and decay). These capture how the surface initially responds - a combination of passive film formation and initial dissolution. A high decay max j (or large decay charge) indicates a highly reactive or easily oxidised surface with poorly protective initial film, whereas lower values indicate an already robust passive film that limits initial oxidation currents[45].

**Transient detection:** fluctuations or rises in current above the baseline passive current were identified as metastable pitting transients. We implemented algorithms to detect such events, characterised by a rapid current increase followed by a decay bringing the current back to the baseline (repassivation). Each transient was parameterised by its relative peak current (peak height, belly height), its duration in sec (peak width, belly width), the time in which the event displayed the maximum current (peak max position, belly max position), and the charge of the transient (integral of the current above baseline for that event). A transient persists while $j_{dissol} > j_{repass}$ and ends when this relationship reverses.

**Supervised event classification:** in addition to quantitative descriptors, we leveraged domain expertise to categorise transients into distinct classes based on their shape and timing (Fig. 2,
see also Fig. S1 in Supplementary Materials for a schematic representation of the different
event types). Out of the total dataset, we identified and analysed over 91 transient events
categorised into three distinct classes: meta_decay (metastable pitting during the jump/decay
phase), meta (metastable pitting after decay stabilisation), belly (passive film thinning events
occurring later in the test). Additionally, one non-transient category was defined:
prior_decay_activity (baseline activity before/during the initial decay). In seven cases, curves
combined prior_decay_activity with a transient event. Sites without significant transients were
labelled as “default”.

The main classification groups peak-type transients (meta and meta_decay: 21 events total) and belly-type transients (belly: 70 events). Peak-type and belly-type transients are mutually exclusive. Peak-type transients (categorised as “meta” when occurring after the initial decay period and “meta_decay” when occurring during the initial decay) are sharp, spike-like current surges with a clear single apex and short width (fast dropout). These represent the same process (true metastable pitting events) differing only in their timing relative to the testing duration. Unlike peak transients, belly transients do not represent discrete metastable pitting events but rather indicate passive film thinning or degradation (a pre-pit state), like the pitting-susceptible zones identified through optical techniques in stainless steel surfaces[46]. During potentiostatic anodic polarisation, these broader transients likely reflect areas where the passive oxide film begins to dissolve quasi-uniformly, with a gradual thinning process driven by increasing defect density within the film[47]. This defect-driven thinning enhances ionic transport through the film (metal cations out, chloride/oxygen anions in), effectively undermining the barrier properties over time without immediate pit formation.

Additional classes included “meta_major” (especially large transient approaching stable
pitting), and “prior_decay_activity” (curves where baseline current appeared before the
typical initial jump/decay feature, likely indicating surfaces with higher initial resistance to
establishing electrochemical contact, resulting in delayed response). Notably, test site 113 (depicted in Fig. 2 (a)) displayed a meta event followed by a meta_major event, representing the only case in our dataset where two distinct transient events occurred on the same site. The initial meta event on this site was already significantly larger than typical metastable events observed on other curves, suggesting that this site had exceptional susceptibility to pitting even before the major metastable breakdown occurred.

Correlation and statistical analysis: we examined pairwise correlations (Pearson and Spearman rank) between all measured descriptors across the dataset, with the comparison between linear and rank-based correlations helping validate the appropriateness of subsequent linear modelling approaches.

## Surface characterisation and correlative analysis

After electrochemical testing, the steel surface was examined using scanning electron microscopy (SEM) to locate the droplet footprints (corrosion spots) left by the micro-droplets at each testing location (Fig. 3 (a)). Fig. S3 shows the complete surface with all footprints and their corresponding numbered maps (1–205).

Electron backscatter diffraction (EBSD) analysis was then performed to map the crystallographic orientations and microstructural features at each test site. EBSD post-corrosion analysis confirmed an austenitic microstructure with fine duplex grains typical of 316L (Fig. 3 (b)). This correlative SEM-EBSD approach allowed us to assign each electrochemical event to a specific grain orientation or microstructural feature.
<figure><img src="image_9.png" /><figcaption>Fig. 3. Correlative SEM-EBSD analysis of SECCM testing sites on 316L stainless steel. (a) SEM image of the sample surface after SECCM testing, showing the footprints left by the micro-droplets at each testing location. (b) The same SEM image with superimposed EBSD data at each footprint location, revealing the underlying grain orientations. Red circles indicate Site 113 (outlier). Fig. S4 shows the original EBSD image from the entire surface.</figcaption></figure>

EBSD data processing and Linear Discriminant Analysis

For quantitative analysis of crystallographic orientations, we extracted three indices (ind1, ind2, ind3) from the EBSD data at each pixel location. These indices were derived by transforming the surface normal to the crystal reference frame using the measured Bunge Euler angles and mapping to the cubic inverse-pole-figure (IPF) triangle. The three normalised triangle weights (IPF colour channels) were recorded as ind1–ind3 (dimensionless, range 0–1), quantifying the proximity of the local surface normal to the <001>, <101>, and <111> crystallographic directions, respectively. These indices capture the out-of-plane crystallographic orientation and were used as the sole EBSD inputs for subsequent analysis.
To explore relationships between crystallographic features and electrochemical behaviour, we employed LDA as a supervised dimensionality reduction technique. The analysis proceeded as follows:

1. For each SECCM footprint, all EBSD pixels within the droplet contact area were identified, and their ind1–ind3 values were extracted.

2. Each footprint was assigned a categorical label based on the dominant transient event type observed in its chronoamperometric curve, using the manual classification system described in the electrochemical data analysis section.

3. LDA was performed with `n_components=1` to obtain a one-dimensional projection (LDA1) that maximally separates the different electrochemical behaviour classes based on their crystallographic features.

4. For visualisation purposes, LDA1 scores were computed at the pixel level and paired with footprint-level electrochemical descriptors. Specifically, for each footprint, the single electrochemical descriptor value (e.g., decay max j, peak height) was replicated across all pixels belonging to that footprint. This pixel-wise approach was used solely for visualisation in scatter plots, creating vertical bands where the electrochemical descriptor (y-axis) remains constant within a footprint while the LDA1 score (x-axis) varies across pixels.

## Data and code availability

The complete dataset including all chronoamperometric curves, extracted descriptors, EBSD measurements, and classification labels can be downloaded at https://doi.org/10.5281/zenodo.18668632. The entire codebase for data analysis, descriptor extraction, statistical analysis, and visualisation was developed in Python 3.8 and is available on GitHub https://doi.org/10.5281/zenodo.18671042. The analysis workflow utilised several
Python libraries including NumPy, SciPy, Pandas, Scikit-learn, Matplotlib, Seaborn and custom algorithms for transient detection and charge partitioning.

## Droplet footprint characterisation and oxygen transport modelling

During the SECCM measurements, 205 sequential droplet landings were performed over the metal surface using the same pipette under ambient atmospheric conditions (no external purging or dearation). Post-mortem imaging by SEM revealed that the droplet footprint area decreased progressively across the measurement sequence, from approximately 8 µm² in the earliest tests to approximately 3 µm² in the final tests (Fig. S5), primarily due to gradual solvent evaporation from the pipette over the multi-hour experimental duration. This sequential variation in footprint size (a commonly observed feature in SECCM experiments on metals[48]) created different aeration conditions across the dataset, with smaller droplets having higher surface-to-volume ratios and thus enhanced oxygen mass transfer from ambient air to the metal-electrolyte interface.

The wetted area at each test location was quantified post-mortem from SEM images of the droplet footprints. Recent work demonstrates that the wetted area during SECCM can be larger than the nominal pipette aperture and depends on landing conditions and withdrawal rates[48]. Each individual measurement consisted of a 60 s potentiostatic hold during which the current response was monitored continuously.

To verify the plausibility of oxygen gradient effects arising from droplet geometry variations, finite-element modelling (FEM) was performed using COMSOL Multiphysics. The model geometry and meshing are shown in Fig. S6 (SI). The model was built around the transport of diluted species module with Fick's diffusion-driven oxygen transport, with equilibrium at the water-air interface governed by Henry's law (solubility: 1.3 mM O₂ at 25°C). Boundary conditions included: (i) constant oxygen concentration at the air-droplet interface (1.3 mM), (ii) oxygen consumption at the substrate (flux boundary condition based on cathodic current),
and (iii) axisymmetric geometry representing droplet diameters from 2 to 8 µm. Complete boundary conditions are summarised in Table S1 (SI). This model served as a confirmatory check of the oxygen flux variation magnitude rather than a predictive tool. Details of boundary conditions, mesh specifications, and sensitivity analysis are provided in Supplementary Materials.

In total, 205 individual CA tests were carried out to gather a statistically significant dataset across varying aeration conditions and microstructural features.

## RESULTS

### Influence of oxygen availability on passive film formation and general dissolution

A feature of the SECCM experiments is the progressive variation in droplet footprint size observed during the sequence of measurements (Fig. 3 and Fig. S5). As the experiment proceeds, solvent evaporation causes the droplets to become progressively smaller. This evolution effectively modulates the local oxygen availability in each droplet, and particularly at the centre of the droplet (or the centre axis of the pipette). Smaller droplets, with larger perimeter-to-area ratio, promote faster oxygen transfer from the surrounding air, while larger droplets develop oxygen concentration gradients with less-aerated central regions. Because all measurements were performed under ambient air, the oxygen concentration in the electrolyte is governed by this droplet geometry and exposure time. Rather than viewing this footprint variation as an experimental drawback, we leverage it as an intrinsic variable to investigate how oxygen availability or aeration affects passive film formation and local dissolution of 316L stainless steel. Each SECCM landing involved a 60 s potentiostatic hold, allowing us to monitor the time-dependent current response associated with oxide film growth and stabilisation at that site.
Fig. 2 shows representative CA curves obtained during these potentiostatic polarisations, including examples of different types of transient events that will be discussed in detail in the following section. In brief, different behaviours, classified and identified by supervised machine learning (see Methods and further discussion), are observed. They generally consist of an initial current response upon landing, overall current decay, and stochastic appearance of current transients, each being addressed successively.

Upon droplet contact, the CA curves show an initial current response followed by rapid decay to baseline levels, both occurring within the first ~0.4 seconds of contact (Fig. 2a). This behaviour is characteristic of passive film formation on stainless steel under anodic polarisation: the initial response corresponds to charging and oxidation of the surface (including dissolution and growth of the oxide film), and the subsequent decay reflects film development which reduces the dissolution rate[49]. It should be acknowledged that this initial electrochemical transient inherently involves some degree of surface conditioning, as any applied potential during droplet contact will influence the surface state prior to the main measurement period[50].

The magnitude of this initial decay varied notably with the droplet footprint (aeration).

Throughout this paper, when we refer to the “protectiveness” of the passive film, we are specifically describing its ability to prevent generalised corrosion (uniform dissolution) of the underlying metal. It’s important to note that this protective quality against general corrosion does not necessarily correlate with localised corrosion resistance, as will be discussed later. Even though 316L surfaces are pre-passivated, the fresh sites likely had only a native oxide; the potentiostatic hold significantly increases the oxide protectiveness (increased thickness and/or density).

Sites tested with smaller droplet footprints exhibited significantly higher baseline currents and greater total anodic charge compared to sites with larger droplets, as shown in Fig. 4a. The
colour-mapping of curves by footprint size clearly demonstrates this area effect: decreasing footprint area (increased aeration) consistently results in higher surface activity [14] By examining extreme cases (curves with the lowest and highest activities, see inset of Fig. 4a), we observed that surfaces under higher aeration conditions displayed steeper slopes (indicating faster kinetics of passive film formation) and more noise, indicative of higher surface activity. This is confirmed over the whole set of measurements as seen in Fig. 4 (b) and (c).

<figure><img src="image_10.png" /><figcaption>Fig. 4. (a) CA curves population showing the effects of footprint area (aeration) and polarisation time on current density. The main panel shows all curves colour-mapped by footprint area. Inset: representative extreme cases highlighting area effect. Relationship between footprint area and (b) noise, where each point represents each curve's average noise, and (c) slope, where each point represents each curve's slope parameter.</figcaption></figure>

To validate that the observed footprint-dependent effects could arise from oxygen availability differences, we performed finite element modelling (COMSOL) of oxygen transport in the SECCM configuration. Fig. 5 shows the calculated average oxygen flux at the substrate
surface for droplet diameters ranging from 2 to 5 µm. The model predicts a monotonic increase in oxygen flux with decreasing droplet size, from ~2.8×10⁻⁵ mol/(m²·s) at 5 µm diameter to ~3.35×10⁻⁵ mol/(m²·s) at 2 µm diameter (~20% increase).

Additional COMSOL outputs (Fig. S6 (c), SI) indicate that the oxygen flux at the metal interface is spatially non-uniform within the droplet (maximal near the droplet centre, especially for smaller radii) and that the oxygen field approaches a quasi-steady distribution on sub-millisecond timescales.

<figure><img src="image_11.png" /><figcaption>Fig. 5. Finite element analysis of oxygen flux variation with droplet size in SECCM. Calculated average oxygen flux at the substrate surface beneath a 1 µm aperture pipette as a function of droplet diameter. Modelling parameters and boundary conditions are described in the SI.</figcaption></figure>

To interpret these oxygen flux variations in terms of passive film defect chemistry, we consider the Point Defect Model (PDM) framework[51,52] as an illustrative mechanism. In
the PDM, higher local O₂ concentration at the film/solution interface enhances vacancy annihilation, resulting in increased vacancy flux through the film and a more defective (n-type) passive layer with greater ionic transport[53] - hence higher passive current. Our observations are consistent with this: smaller droplets (more aerated) produce less resistive films with higher steady-state currents, while larger droplets (less aerated) yield more protective films with lower currents. The detailed mechanistic interpretation, including how oxygen modulates film defectiveness without being electrochemically reduced at our anodic potentials, will be elaborated in the Discussion section.

Fig. 6 quantifies the relationship between footprint area and key electrochemical parameters. A clear negative correlation exists between footprint area and total charge due to uniform corrosion (Fig. 6a): smaller footprints (higher aeration) correspond to significantly higher baseline charges. Similarly, under higher aeration conditions, both the initial current spike and decay charge were significantly higher compared to lower aeration tests (Fig. 6b). A complete list of the droplet footprint area and the key descriptors and event class for all 205 SECCM sites is provided in the SI (Table S2).

The average decay max j (maximum current density during the jump/decay, see Fig. S1) for the smallest droplets was roughly double that for the largest droplets, indicating that higher aeration conditions shift the balance between dissolution current density (j_dissol) and repassivation current density (j_repass) toward dissolution-dominated conditions during initial film formation.
<figure><img src="image_12.png" /><figcaption>Fig. 6. Relationship between footprint area and key electrochemical parameters. (a) Total charge versus footprint area across all test sites, showing a clear inverse relationship where smaller footprints (higher aeration) correspond to significantly higher baseline charges. (b) Decay max j versus footprint area, demonstrating how smaller droplets (higher aeration) consistently produce higher initial reactivity, indicative of more aggressive but less protective passive film formation. In both panels, coloured markers indicate different types of manually classified transient events, with grey circles representing sites without classified transients. Marker size is proportional to footprint area for grey circles, and proportional to event charge for coloured markers (except meta_major in panel (a), where the bar would be excessively large for the plot scale).</figcaption></figure>

Notably, meta and meta decay events (blue and orange points in Fig 6) show similar correlation patterns in terms of total charge, which is expected since these events represent the same underlying process of metastable pitting, differing only in whether they occur during or after the initial current decay. The observed vertical dispersion in the data points reflects differences in surface reactivity, which we will address later when discussing microstructural influences on corrosion susceptibility.

During many of the 60 s potentiostatic tests, we detected transient current spikes superimposed on the passive current baseline (as illustrated in Fig. 2). Based on their characteristic shapes and timing, these transients were classified into distinct categories (see
Methods for detailed classification scheme): peak-type transients (meta and meta_decay) represent sharp, spike-like metastable pitting events with rapid repassivation; belly-type transients represent broader, longer-duration events associated with passive film thinning or degradation; and in one exceptional case (site 113, the red meta_major point in Fig. 6a), an exceptionally large transient approaching stable pitting was observed. Not every test showed transients; many sites exhibited only smooth, gradually decreasing baseline current curves without localised breakdown events.

The incidence and characteristics of these transient events were strongly affected by aeration conditions. Our analysis shows that belly-type events (green points in Fig. 6) occur more frequently under high aeration conditions (smaller footprints), particularly on surfaces that otherwise showed relatively higher resistivity. This suggests that while higher oxygen availability generally promotes transient events, the nature of these events (peak vs. belly) depends on the interplay between aeration and the surface's initial passive film characteristics. Conversely, peak-type metastable pitting events (meta and meta_decay) show less dependence on footprint size, with these events distributed across the full range of droplet areas. The role of microstructural factors in these events will be examined in detail in later sections.

## Characterisation and behaviour of transient events

### Classification and key parameters

Peak and belly transients showed distinct quantitative characteristics: peak events typically lasted ~0.1 sec with current densities around 1000 µA/cm² above baseline, while belly events lasted 1–10 sec with moderate peaks (~100 µA/cm² above baseline).

Meta_decay events occurred during initial decay, while meta events occurred after baseline stabilisation.

### Aeration effects on transient events
Fig. 7 shows the relationships between footprint area (aeration) and transient event parameters: (a) charge, (b) width, and (c) height. The data reveals distinct aeration-dependent patterns for different transient types.

For belly events (green circles), decreasing footprint area (higher aeration) correlates with significant increases in both charge and width. Peak-type events (blue and orange markers) show a different response: meta and meta_decay events maintain consistent widths but show increased heights under higher aeration, indicating that oxygen availability primarily affects dissolution intensity rather than repassivation kinetics.

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

Fig. 7. Effects of aeration on transient event characteristics. Bivariate plots showing the relationship between footprint area (aeration) and transient parameters: (a) transient charge; (b) transient width; and (c) transient current density (height). Colours represent different transient classifications. The plots reveal distinct patterns for different transient types: belly events (green) show significant increases in both charge and width with decreasing footprint area, while peak-type events (blue/orange) maintain consistent widths but show increased heights. The size of grey circles is proportional to footprint area. The size of coloured markers is proportional to the charge of the event. Red arrows indicate Site 113 (outlier).

For belly events (green points), increased aeration (smaller footprint) correlated strongly with larger transient charge, resulting from both increased height and width measurements. The effect on transient width was particularly pronounced for these belly-type events.

Peak-type events (blue and orange points) responded differently to aeration conditions. With higher aeration, peak heights increased, yet peak widths remained remarkably consistent
across the range of footprint sizes. This created a different pattern than observed with belly
events. The net charge in peak transients showed a slight increase with aeration (primarily due
to height contribution), but this correlation was notably weaker than for belly transients.

Quantitatively, the data shows that aeration impacts the two transient classes through different
parameters: primarily affecting duration (width) for belly events while predominantly
influencing intensity (height) for peak events. These distinct response patterns create clear
data clusters in the bivariate analysis shown in Fig. 7.

Passive film evolution and electrochemical correlations

Time-dependent changes in transient behaviour

The influence of testing time (as a proxy for passive film development) on transients provides additional insights. Fig. 8 shows a clear general trend: as transient max position increases (events occurring later in the test), the transient width increases while the height decreases. This observation of increased width but decreased height for later-occurring events suggests a fundamental trade-off between passive film protectiveness and repassivation capability, which will be explored further in the Discussion.

Different transient classes show distinctive temporal behaviours (Fig. 8): meta_decay events
occur earliest (0.01–0.4 s), followed by meta events (0.4–2 s), with belly events occurring
much later (1–10 s). These previously described transient types form distinct clusters in the
time-domain, with meta decay and meta events showing characteristic height-width patterns,
while belly events show more variation but follow the general trend of lower heights and
longer durations. Although meta decay and meta are essentially the same underlying process
(metastable pitting events), the sharp separation between these classes in terms of width can
be explained by the surface charging occurring during the initial current decay. This charging
during the jump/decay phase (during the early stages of passive film development) appears to
induce faster repassivation for meta_decay events.

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

Fig. 8. Impact of testing time on transient event characteristics. Bivariate plots showing relationships between transient max position (timing of event during test) and transient parameters: (a) transient width; and (b) transient height. Colours indicate different event classifications. The plots reveal distinct time-dependent behaviour: early events (meta_decay, orange) with short widths and high heights; intermediate events (meta, blue) with moderate widths and heights; late events (belly, green) with much longer widths but lower heights. The size of grey circles is proportional to footprint area. The size of coloured markers is proportional to the charge of the event. Red arrows indicate Site 113 (outlier).

Fig. 9 provides a complementary view by plotting transient charge versus max position. This relationship reveals that early-occurring events (mostly meta_decay) show decreasing charge with increasing max position, as the decreasing dissolution current dominates. For intermediate events (meta), dissolution and repassivation decrease at similar rates, resulting in relatively stable charges. For late-occurring events (belly and outlier 113), the significant decline in repassivation capability leads to dramatically increased charges despite lower dissolution rates.
<figure><img src="image_15.png" /></figure>

Fig. 9. Relationship between event timing and transient charge. Transient charge versus transient max position (event timing), with coloured markers indicating event classifications. During transient events, net current ($j_{dissol}$ - $j_{repass}$) produces charge accumulation. The timing-dependent patterns reflect how passive film development affects these competing processes. Early events (meta_decay, orange): decreasing charge with time as rapid passive film formation strongly reduces $j_{dissol}$ ($\Delta j_{dissol} < 0$). Intermediate events (meta, blue): stable charges as $j_{dissol}$ and $j_{repass}$ decrease similarly through the maturing film ($\Delta j_{dissol} \approx \Delta j_{repass}$). Late events (belly, green; meta_major, red): increasing charges as mature passive films severely impair repassivation ($\Delta j_{repass} < 0$), sustaining high net currents. Gray circle size is proportional to footprint area; coloured marker size is proportional to event charge. Red arrow indicates site 113 (meta_major event).

Statistical analysis across all sites showed that meta_decay events tended to have smaller charges and shorter durations than meta events, implying that metastable pits nucleating on freshly forming passive films repassivated more readily. This is reasonable because initially the metal surface is highly active but also easily covered by new oxide. A burst of dissolution in the first seconds likely raises local pH or depletes chloride enough that the pit cannot
sustain itself, and the surrounding area has not yet developed significant oxide resistance to
impede repassivation.

By contrast, a meta event that initiates later (say 10 s into polarisation) must breach a more
resistive passive layer. Site 113 exemplifies this timing effect with its exceptionally late
occurrence and high charge. Interestingly, what makes Site 113 an outlier is primarily its
exceptional timing for a metastable pitting event. While classified as a meta transient based on
its characteristic current surge, it occurred much later (~20 sec) than typical meta events, in a
timeframe normally associated with belly-class events. Despite its exceptional transient
behaviour, site 113's generalised corrosion charge remained within the normal range (Fig.
S7), indicating that its susceptibility to severe localised attack was not accompanied by
elevated uniform dissolution. This late occurrence, when the passive film had become more
protective but also more resistant to repair, resulted in an unusually long duration transient
despite its meta-like initiation characteristics (see Fig. S8 in Supplementary Materials).

The aspect ratio (height-to-width ratio) of transients proves to be one of the most effective
parameters for distinguishing between different transient types. As shown in Fig. S8, plotting
the transient aspect ratio against transient max position reveals remarkably clear clustering of
the different transient classes. This post-hoc quantitative validation confirms that our expert-
based classification captures genuine physical distinctions between event types. This finding
is particularly promising for future development of unsupervised classification methods to
automatically detect and categorise different types of transient events in electrochemical data.

## Correlations between electrochemical parameters

Fig. 10 summarises the top features influencing generalised corrosion. The decay parameters (decay max j, decay charge) show strong positive correlations with total charge, indicating that higher initial reactivity leads to greater overall corrosion. Conversely, footprint area demonstrates a strong negative correlation, confirming that increased aeration (smaller
footprint) significantly enhances general dissolution. Peak height also shows a moderate positive correlation with corrosion charge, suggesting that surfaces prone to more intense metastable pitting also experience greater uniform dissolution. Peak position and width show weaker negative correlations with generalised corrosion charge, suggesting that these localised corrosion features (timing and duration of transients) have less correlation with the uniform dissolution behaviour than the initial surface reactivity and aeration conditions.

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

Fig. 10. Correlation analysis showing the relationship between key electrochemical features and generalised corrosion charge. Both Pearson (linear, blue) and Spearman (rank, orange) correlation coefficients are displayed. Positive values indicate that parameters increase with corrosion charge; negative values show inverse relationships.

Fig. 11 extends this analysis to transient event characteristics, showing how different features influence peak and belly transients distinctively. For peak-type transients (top panels), decay parameters show negative correlations with peak position and width, indicating that surfaces with higher initial reactivity tend to experience earlier and shorter transients.
The footprint area demonstrates positive correlations with peak width and position but negative correlation with peak height, revealing that under less aerated conditions (larger droplets), metastable pits tend to occur later, lasting longer but with lower intensity: consistent with sluggish but persistent localised activity under lower oxygen availability.

For belly-type transients (bottom panels), we observe predominantly negative correlations across all features, particularly between footprint area and all belly characteristics. This suggests that higher aeration (smaller footprints) generally enhances all aspects of belly-type transients (their timing, duration, intensity, and total charge).

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

Fig. 11. Correlation analysis showing how environmental and electrochemical parameters influence different transient event characteristics. Top panels display correlations for peak-type transients (position, width, height, charge); bottom panels show correlations for belly-type transients. Solid bars represent Pearson correlations; hatched bars show Spearman rank correlations.

These contrasting behaviours highlight the complex interplay between oxygen availability and passive film properties in determining both the likelihood and character of different types of metastable pitting events. While higher aeration leads to more aggressive but quickly stifled
peak events (facilitated by rapid repassivation), it also promotes broader, more substantial
belly events when the passive film is breached later during the measurement period.

Correlation of electrochemical features with microstructure

To explore whether microstructural features influence corrosion behaviour, we employed
Linear Discriminant Analysis (LDA) on EBSD data. The first LDA component (LDA1 =
-0.553×ind1 - 0.299×ind2 + 0.148×ind3) was derived as a weighted combination of the three
crystallographic orientation indices, where ind1, ind2, and ind3 represent proximity to <001>,
<101>, and <111> directions, respectively. The LDA weights indicate that deviations from
<001> orientation have the strongest influence (|0.553|), followed by deviations from <101>
(|0.299|), while proximity to <111> has a weaker but opposite effect (0.148). Higher LDA1
values thus correspond to surfaces with orientations closer to {111} crystallographic planes.
As a supervised discriminant, LDA1 represents the crystallographic axis that optimally
separates the observed electrochemical behaviour classes rather than any intrinsic material
property.

Fig. 12 shows the relationship between the LDA1 parameter and key electrochemical
descriptors. For decay_max_j (Fig. 12a), a slight negative trend with increasing LDA1 values
is visible specifically for meta and meta_decay events (blue and orange points). The right
panel (Fig. 12b) shows a somewhat clearer relationship between LDA1 and peak height for
metastable pitting events. Site 113 (red point) stands out as an extreme case: characterised by
an exceptionally high LDA1 value (indicating strong {111} character), it exhibited both
relatively low initial reactivity and the most severe localised attack.
<figure><img src="image_18.png" /><figcaption>Fig. 12. Correlation between the first Linear Discriminant Analysis component and electrochemical parameters, with points coloured by transient event classification. (a) LDA1 versus decay max j, showing a negative trend specifically for meta and meta decay events (blue and orange points), indicating microstructural influence on initial reactivity for these event types. (b) LDA1 versus peak height, demonstrating clearer microstructural influence on metastable pitting intensity.</figcaption></figure>

Fig. 13 shows the relationship between LDA1 and generalised corrosion charge. The data exhibits considerable scatter, but meta and *meta decay* events (blue and orange points) show a negative trend with LDA1, like Fig. 12a, while belly-type events (green points) show no correlation. The *meta_major* event (site 113, red point at high LDA1) exhibits relatively high generalised corrosion charge, but this single outlier does not establish a general relationship. Meta and *meta_decay* events span similar LDA1 values (consistent with their shared nature as metastable pitting events), but their generalised-corrosion charge tends to decrease with increasing LDA1. Belly-type events (green points) are distributed across the entire LDA1 range with no apparent correlation to LDA1.
<figure><img src="image_19.png" /><figcaption>Fig. 13. Relationship between the first LDA component derived from EBSD data and generalised corrosion charge, with points coloured by transient event classification. The substantial scatter indicates that microstructural effects are secondary to other factors.</figcaption></figure>

The horizontal spread of points at each electrochemical value reflects the crystallographic heterogeneity within individual footprints. Overall, while some weak correlations between crystallographic orientation and specific corrosion descriptors are observable (particularly for metastable pitting intensity and generalised corrosion charge in meta-type events), the dominant factor controlling both general corrosion and transient event characteristics is clearly oxygen availability, as demonstrated by the strong footprint area correlations in Figs. 4, 6 and 7. Microstructural features appear to exert only secondary influence under our experimental conditions, likely because the aggressive conditions (0.01 M NaCl, +1.20 V)
and high oxygen variability overshadow the more subtle orientation-dependent effects that
can dominate under milder conditions. The positive correlation between ind3 and LDA1
explains why Figs. S9 and S10, which plot ind3 directly, show similar trends to Figs. 12 and
13 that use LDA1.

DISCUSSION

Mechanistic understanding of oxygen-passive film interactions

Fig. 14 synthesises the complete causal chain linking droplet geometry to electrochemical
observables: smaller droplets enhance oxygen flux at the metal-electrolyte interface (Fig. 14
a, b), which influences passive film defect chemistry (Fig. 14 c) and manifests as distinct
electrochemical signatures including initial reactivity, baseline dissolution, and transient
events (Fig. 14 d). This framework, interpreted through Point Defect Model (PDM)
principles, explains the nonlinear amplification of modest oxygen variations into substantial
electrochemical responses.

While the variation in droplet footprint was not deliberately controlled (arising instead from
natural evaporation and/or instrumental factors common to SECCM experiments[48]) it
provided valuable insights into oxygen-passive film interactions across a gradient of aeration
conditions[11,17,21]
<figure><img src="image_20.png" /><figcaption>Fig. 14. Mechanistic framework synthesising oxygen-mediated corrosion behaviour in SECCM measurements. (a) Droplet size, (b) modulates oxygen flux ($J_{O2}$) at the metal–electrolyte interface, (c) influencing passive-film state within a PDM-inspired defect/vacancy framework, (d) producing distinct electrochemical signatures: initial reactivity (decay max $j$), baseline dissolution ($Q_{gen}$), and localised transient events.</figcaption></figure>

Finite-element modelling confirms that geometric constraints produce the observed variation in oxygen flux (Fig. 5). While this model is deliberately simplified (neglecting time-dependent meniscus shape and hydrodynamics) it demonstrates that the sign and magnitude of oxygen-supply changes are sufficient to account for the observed electrochemical differences (Figs. 4, 6).

To clarify how oxygen controls passive film behaviour without being electrochemically reduced at our anodic potentials, we present the relevant PDM reactions[51,52] (using simplified Kröger-Vink notation). At the metal/film (m/f) interface—oxygen vacancy generation:
$$m \rightarrow M_M + V_{\ddot{o}} + 2e^{-} \qquad (\text{Eq. 1})$$

At the film/solution (f/s) interface—oxygen vacancy annihilation:

$$V_{\ddot{o}} + H_2O \rightarrow O_0^x + 2H^+ \qquad (\text{Eq. 2})$$

And for film dissolution/thinning:

$$MO + 2H^+ \rightarrow M^{2+} + H_2O \qquad (\text{Eq. 3})$$

Where $V_{\ddot{o}}$ represents doubly-charged oxygen vacancies, $M_M$ is a metal cation in its lattice site, and $O_0^x$ is an oxygen ion in its lattice site. Note that in the PDM framework, oxygen's principal effect is via defect chemistry rather than direct electrochemical reduction. The $O_2/H_2O$ couple establishes the oxidising boundary condition that accelerates vacancy annihilation without $O_2$ being consumed as a reactant[51,52].

In this framework, dissolved oxygen enhances reaction (Eq. 2) by maintaining oxidising conditions at the f/s interface, effectively increasing the sink strength for oxygen vacancies. While $H_2O$ provides the lattice oxygen atoms in Eq. (2), dissolved $O_2$ sets a more oxidising boundary condition at the f/s interface through the $O_2/H_2O$ redox couple. Higher dissolved oxygen concentrations increase the local oxidising potential, which in turn raises the rate constant for vacancy annihilation (Eq. 2), thereby strengthening the vacancy sink.

At steady state, the vacancy generation rate at the m/f interface (Eq. 1) must match the annihilation rate at the f/s interface. Higher oxygen availability thus drives a greater vacancy flux through the film, resulting in a more defective (n-type) barrier layer with enhanced ionic conductivity. Importantly, even modest increases in oxygen availability can shift this vacancy source/sink balance non-linearly within the PDM framework[51,52], amplifying the electrochemical response.
This non-linear amplification explains our experimental observations. Our finite-element modelling predicted only ~20% higher average O₂ flux for the smallest droplets (Fig. 5), yet the electrochemical activity increased substantially. Quantitative correlation analysis (Fig. S11) reveals a power-law relationship between COMSOL-estimated oxygen flux and generalised corrosion charge ($Q \propto J_{\{O_2\}}^{4.64}$, R²=0.59), demonstrating the strong nonlinear amplification consistent with PDM's exponential dependence of rate constants on interfacial conditions. The PDM's exponential dependence of rate constants on interfacial potential and electric field means that small O₂-driven shifts in the oxidising boundary condition can disproportionately increase the steady vacancy flux, translating modest flux differences into the large changes in passive current and transient activity we observed.

Oxygen plays a crucial role in sustaining metastable pits once nucleated. The timing of events is significant: later events must overcome more developed passive films, explaining their extended duration despite lower intensity. This mechanistic relationship aligns with established understanding that increased film thickness corresponds to longer time needed for restoring pH at the bottom of acidic pits[54].

Concerning the cathodic reactions, we do not consider the higher oxygen concentration to facilitate oxygen reduction on the pit walls or nearby surface, as the cathodic reaction is expected to be fully supported in the counter electrode under force corrosion imposed by substrate polarisation[55,56]. Nonetheless, the net result is that increased aeration promotes more aggressive both general and localised attack; an observation aligned with macro-scale studies showing that aerated solutions, while raising pitting potential, render pits once formed harder to extinguish[19].

Film maturity modulates how oxygen availability influences different event types. Therefore, aeration's effect is multifaceted: higher oxygen availability creates less protective passive films that exhibit both elevated general dissolution and increased susceptibility to localised
breakdown. The timing-dependent nature of these distinct transient modes reflects the evolving passive film properties throughout the measurement periods. The observation that lower aeration conditions (larger droplet footprint) result in more protective films reflects reduced oxygen supply moderating oxidation kinetics, while higher aeration (smaller droplet footprint) promotes rapid but less robust film formation that provides less effective long-term protection against both uniform and localised corrosion. This extends to practical scenarios where flowing aerated solutions often exhibit higher corrosion rates than stagnant conditions[57].

As a result, when a flaw or inclusion triggers a local dissolution event under well-aerated conditions, that event can grow more substantially (as seen with the belly transients) before it is stifled. These considerations echo the dual role of oxygen noted in prior studies: beneficial for maintaining passivity[58], yet potentially detrimental for repassivation[19].

While oxygen availability influences both general dissolution and repassivation behavior, distinct but related processes dominate these phenomena. General dissolution (baseline passive current) responds directly to oxygen-controlled film defect chemistry through the PDM framework: higher aeration produces more defective films with enhanced ionic conductivity, yielding higher steady-state dissolution currents (Figs. 4, 6). In contrast, repassivation kinetics are primarily governed by film maturity and protectiveness, as evidenced by the stronger correlation between transient width and timing (Fig. 8) compared to footprint area (Fig. 7). Oxygen's role in repassivation is thus indirect: it influences the film properties that later govern repassivation kinetics, but film maturity emerges as the dominant factor controlling event duration and healing capability.

## Passive film development and repassivation trade-offs

The mechanisms behind this trade-off become clear when considering the physical barrier properties of a more protective film. Such a film not only suppresses small fluctuations
(requiring a larger disturbance to initiate a pit) but also impedes lateral oxide growth that would otherwise quench an active pit. This trade-off is exemplified by Site 113 (discussed in detail in below), where an exceptionally protective film ultimately experienced catastrophic breakdown.

Notably, the transient width appears to be more strongly influenced by the time-induced passive film protectiveness increase (Fig. 7) than by the oxygen-induced activity increase (Fig. 6). This suggests that while oxygen availability can modulate the intensity of localised dissolution events, the duration of these events is primarily governed by the maturity of the passive film at the time of initiation.

This aligns with phenomena in coated metals: a performant coating prevents corrosion until damaged, but once breached, corrosion can undercut it rapidly because the intact coating hinders reparative processes. In bare metals with passive films, the analogy is that an overly stable film, when locally disrupted, might impede the supply of metal cations or electrons to reform the film at the pit location, leading to poor repassivation.

Our results thus highlight a trade-off between passivity and repassivation ability. An optimal scenario might be a moderately protective film that both resists initiation to some degree and, if broken, can promptly reform. In practice, alloy design tries to achieve both: elements like Cr increase film stability (raising pitting resistance) while Mo enhances repassivation within pits.

This classification approach reveals powerful insights for modelling pitting behaviour and predicting corrosion susceptibility. This fundamental trade-off between passive film protectiveness and repassivation ability has important implications for material design and service conditions. The balance creates a corrosion paradox: the same features that provide excellent general corrosion resistance (highly resistive passive films) potentially create vulnerability to persistent localised attack if breakdown occurs.
Microstructural influences on corrosion susceptibility

We statistically examined whether certain grain orientations tended to show higher passive
currents or more frequent transients. Unlike some literature reports that found clear
orientation dependencies in passivity [25,27], our results showed only weak correlations
between crystallographic orientation and the general corrosion metrics. No crystallographic
plane of 316L in our sample was overwhelmingly prone or immune to metastable pitting
under the test conditions – metastable events were observed on a variety of grain orientations.
This could be because our solution (0.01 M NaCl) and potential were quite aggressive, so
even “less susceptible” orientations eventually showed breakdown, and differences were
subtle.

While our primary focus was on oxygen availability and passive film development, the
correlative EBSD analysis revealed some microstructural influences on corrosion
susceptibility. As observed in the Results section, the LDA1 parameter derived from EBSD
data showed meaningful but limited correlations with electrochemical descriptors, particularly
for surfaces prone to metastable pitting events.

The LDA analysis reveals relationships between crystallographic orientations and corrosion
behaviour. Site 113, with its anomalously high LDA1 value, represents an extreme example of
this crystallographic influence on corrosion response.

Large grains with fewer boundaries might experience fewer initiation events [12], but once a
pit forms, the crystallography can influence the shape of the pit and the ease of repassivation.
In our high-throughput approach, the substantial dataset enabled us to detect even subtle
effects through LDA. We note that aside from grain orientation, other SEM-based metrics
(like proportions of grain boundaries/bulk grains and of duplex grains within each footprint)
were examined, but no relevant trends with pitting were found in this specific dataset. This
could imply that, under our conditions, the metastable pitting process was dominated by the
passive film chemistry (tied to oxygen and polarisation time) more than by static metallurgical
factors.

Methodological advances and implications

A central strength of our work lies in the combination of quantitative (data-driven) analysis
with manual classification based on domain expertise. The extraction of numerical descriptors
(e.g., decay max j, transient charge, etc.) and subsequent statistical correlations provided clear
insights into the transients’ pathways. The supervised labelling of transient events was pivotal
in segmenting the data, allowing us to identify distinct modes of metastable pitting that
correlate with both aeration conditions and underlying microstructure.

LDA revealing that specific local microstructures, as characterised by EBSD, influence the
electrochemical reactivity of the 316L stainless steel surface demonstrates the effectiveness of
this data-driven technique in corrosion research. Furthermore, the combination of quantitative
descriptor extraction with manual, expert-based categorization of transient events highlights a
synergistic approach to data analysis. By first using domain knowledge to label different types
of metastable pitting events based on their unique current-time profiles and then applying
statistical methods like LDA to correlate these labels and quantitative descriptors with
experimental conditions (aeration) and material properties (microstructure), we developed a
robust framework for gaining deeper insights into the localised corrosion mechanisms of 316L
stainless steel.

Our approach demonstrates how post-hoc analysis of unintended footprint variations
(typically discarded as experimental artifacts) can yield mechanistic insights. This represents
a departure from the common practice of selecting only “ideal” measurements with consistent
footprints. Through analysis of many micro-sites with varying footprints, we inherently
sampled different microstructural states, oxygen conditions, and degrees of passive film
development (since each site had its own 60 s polarisation history). Traditional bulk
measurements might only yield an average response, whereas here we could deconvolute the roles of O₂ and level of passive film protectiveness and grain orientation to a significant extent. This high-throughput localised testing combined with statistical analysis represents a promising approach in modern corrosion research. It provides a more comprehensive understanding of localised corrosion phenomena that are highly stochastic by nature. For example, the ability to capture hundreds of metastable pit events would enable constructing distributions of pit nucleation propensity and pit stability as a function of controllable variables (O₂, etc.), which is valuable for predictive modeling.

## Study scope and limitations

All electrochemical measurements in this study were performed on a single 316L stainless steel specimen from one industrial batch. While this represents a limitation in terms of batch-to-batch variability assessment, several factors support the validity and generalisability of our findings. First, the industrial treatment (electropolishing and passivation) employed by Packo Inox has been previously characterised and demonstrated high reproducibility at the microscale [43]. Second, the large number of measurement sites (205 individual tests) across the specimen surface captured substantial microstructural variability within the sample, including variations in grain orientation, grain boundaries, and local surface features. Third, our focus on fundamental oxygen-passive film interactions and the PDM-based mechanistic framework suggests that the observed trends should be transferable across specimens with similar microstructures.

## Practical implications

The insights gained from this study can inform both manufacturing practices for 316L components and maintenance strategies for stainless steel infrastructure:
**Optimisation of passivation treatments:** our results challenge the conventional assumption that “thicker is always better” for passive films. The protection-repassivation trade-off identified in this work suggests that passivation protocols should target moderately protective films that balance initial resistance with rapid repassivation capability.

**Non-destructive corrosion susceptibility assessment:** Fig. 10 and Fig. S7 demonstrate that decay max j measured within the first 0.4 seconds correlates strongly with total generalised corrosion charge, enabling prediction of overall corrosion susceptibility without requiring full potentiostatic exposure or triggering transient events. These strong early-time correlations could enable the development of rapid diagnostic protocols. A portable SECCM-like probe measuring initial anodic response within the first seconds of contact could provide quantitative assessment of passive film quality and localised corrosion susceptibility without requiring extended exposure or surface damage. This approach could prove particularly valuable for in-service monitoring of critical infrastructure components where traditional destructive testing is impractical. While a full real-environment pilot study is beyond the scope of this work, we used industrially produced 316L subjected to standard industrial to ensure the extracted early-time signatures reflect realistic component surface states.

A logical next validation step is to benchmark SECCM-derived descriptor distributions against macro-scale performance metrics measured on the same surface state, establishing calibration curves that link micro-descriptor percentiles/tails to component-level performance. However, establishing universal numerical thresholds would require system-specific calibration across different material-electrolyte-potential combinations.

**Environment-specific component design:** the distinct response patterns of peak versus belly transients to aeration conditions (Fig. 6) provide design guidance for components with spatially variable oxygen exposure. Areas prone to oxygen depletion (crevices, gasket interfaces, buried surfaces) should receive enhanced surface treatments to promote protective
film formation, while well-aerated surfaces may benefit from treatments that optimise
repassivation kinetics rather than maximum film protectiveness.

CONCLUSIONS

This comprehensive SECCM-EBSD investigation of 316L stainless steel in 0.01 M NaCl solution reveals fundamental insights into the interplay between oxygen availability, passive film evolution, and localised corrosion susceptibility:

1. Oxygen-mediated passive film behaviour: Systematic variation in droplet footprint area (2–8 µm) effectively modulated oxygen availability, producing faster-forming but less protective passive films under enhanced aeration conditions. The PDM framework explains this through increased oxygen vacancy flux, creating more defective barrier layers with enhanced ionic conductivity. These oxygen-dependent film characteristics have direct implications for passivation strategies in specific service environments.

2. Distinct transient event mechanisms: two distinct transient modes were identified through combined quantitative analysis and expert classification. Peak-type events represent classical metastable pitting with sharp current spikes and short durations, while longer lasting belly-type events indicate gradual passive film thinning with moderate current increases.

3. Microstructural correlations: LDA revealed that crystallographic orientation influences corrosion susceptibility, with surfaces closer to {111} planes (higher LDA1 values) showing distinct electrochemical responses. The LDA1 parameter effectively discriminated between different corrosion descriptors, though microstructural effects proved secondary to oxygen availability and passive film chemistry under our test conditions.
4. Practical implications for corrosion management: strong correlations between early-time descriptors (decay max j measured within first 0.4 s) and overall corrosion behavior suggest feasibility for rapid, non-destructive assessment.

This work demonstrates how high-throughput electrochemical measurements can unravel the multifaceted nature of localised corrosion in passive metals, providing quantitative guidance for both mechanistic understanding and practical management strategies.

## Data availability

All data generated or analysed during this study are included in this published article (and its supplementary information files) and are available in the Zenodo Data repository: https://doi.org/10.5281/zenodo.18668632

## Code availability

The code required to reproduce these findings is included in this published article (and its supplementary information files) and is available to download from GitHub: https://doi.org/10.5281/zenodo.18671042

## AUTHOR CONTRIBUTIONS

L.B.C.: Conceptualization. Data curation. Formal analysis. Investigation. Methodology. Software. Validation. Visualization. Writing – original draft. Writing – review and editing.

D.T.: Formal analysis. Validation. Visualization. Writing – review and editing. M.B.: Data curation. L.M.: Data curation. A.M.: Data curation. Software. F.K.: Resources. Supervision.

Validation. Visualization. Writing – review and editing. V.S.: Formal analysis. Investigation. Methodology. Software. Validation. Visualization. Writing – review and editing. J.U.:

Conceptualization. Formal analysis. Funding acquisition. Investigation. Methodology. Project administration. Resources. Supervision. Validation. Visualization. Writing – review and editing.
**Additional Information**

**Competing interests:** the authors declare no Competing Financial or Non-Financial Interests.

**Acknowledgements**

We dedicate this work to the memory of Professor Digby Macdonald, who passed away on June 12, 2025. From May to December 2023, Professor Macdonald actively participated in discussions and validated our approach. His Point Defect Model framework greatly contributed to the mechanistic understanding presented in this work, but more importantly, his vision and guidance inspired us to pursue this research direction. His profound impact on the field of corrosion science and his generous mentorship will be deeply missed.

The author L.B.C. was a Postdoctoral Researcher of the Fonds de la Recherche Scientifique – FNRS (Belgium), which is gratefully acknowledged, during the execution of this work. The authors acknowledge Prof. Marjorie Olivier (University of Mons) for providing stainless steel treated plates. D.T. acknowledges financial support to the Fonds de Recherche dans l'Industrie et dans l'Agriculture (FRIA). M.B. acknowledges financial support to the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under Grant No. F.4531.19. J.U. acknowledges financial support to the Federation Wallonie-Bruxelles for the ARC-Consolidator grant RENEGADE.

**Declaration of Generative AI and AI-assisted technologies in the writing process**

During the preparation of this work the authors used Claude (Anthropic) to improve language clarity, and readability of the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

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