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Article

Oxidative Degradation Mechanism of Zinc White Acrylic Paint: Uneven Distribution of Damage Under Artificial Aging

by
Mais Khadur
1,
Victor Ivanov
2,
Artem Gusenkov
1,2,
Alexander Gulin
1,2,
Marina Soloveva
1,
Yulia Diakonova
1,3,
Yulian Khalturin
3 and
Victor Nadtochenko
1,2,4,*
1
Moscow Center for Advanced Studies, Kulakova Str. 20, 123592 Moscow, Russia
2
Federal Research Center for Chemical Physics RAS, Kosigin Str. 4, 119991 Moscow, Russia
3
State Tretyakov Gallery, 10 Lavrushinsky Lane, 119017 Moscow, Russia
4
Chemical Department, Lomonosov Moscow State University, Leninskie Gory, 1, 119991 Moscow, Russia
*
Author to whom correspondence should be addressed.
Heritage 2025, 8(10), 419; https://doi.org/10.3390/heritage8100419
Submission received: 29 August 2025 / Revised: 22 September 2025 / Accepted: 27 September 2025 / Published: 3 October 2025
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Abstract

Accelerated artificial aging of zinc oxide (ZnO)-based acrylic artists’ paint, filled with calcium carbonate (CaCO3) as an extender, was carried out for a total of 1963 h (~8 × 107 lux·h), with assessments at specific intervals. The total color difference ΔE* was <2 (CIELab-76 system) over 1725 h of aging, while the human eye notices color change at ΔE* > 2. Oxidative degradation of organic components in the paint to form volatile products was revealed by attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, micro-Raman spectroscopy, and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). It appears that deep oxidation of organic intermediates and volatilization of organic matter may be responsible for the relatively small value of ΔE* color difference during aging of the samples. To elucidate the degradation pathways, principal component analysis (PCA) was applied to the spectral data, revealing: (1) the catalytic role of ZnO in accelerating photodegradation, (2) the Kolbe photoreaction, (3) the decomposition of the binder to form volatile degradation products, and (4) the relative photoinactivity of CaCO3 compared with ZnO, showing slower degradation in areas with a higher CaCO3 content compared with those dominated by ZnO. These results provide fundamental insights into formulation-specific degradation processes, offering practical guidance for the development of more durable artist paints and conservation strategies for acrylic artworks.

1. Introduction

Acrylic binder paint appeared on the American art market in the early 1950s under the commercial name “Magna” and immediately gained popularity among artists. The advertising slogan of the Bocour Artist Colors company, which produced the paint, presented the new product as “the first new painting technique in 500 years”, which was essentially fair, given the two techniques that had dominated throughout the history of easel painting: oil-based and emulsion-based (tempera) techniques [1]. Painters quickly appreciated the advantages of the new product over traditional techniques, which consisted of the rapid drying of the paint, the intensity of color that was preserved even in the thinnest translucent layers, and the durability of the new product. Due to the high homogeneity of the paint mass, the new technique was well suited for creating large, uniform in color and texture painting planes, where the brushstroke was completely leveled. All these properties of paints gave artists new pictorial possibilities, and accordingly, it can be said that the invention of acrylic had a direct and significant impact on artistic technique (in the understanding of not only the material, but also the direct creative work, including the formation of an image, the creation of texture, and the application of layers) and, in general, on the pictorial art of the second half of the twentieth century. The scale of the use of acrylic paints is evidenced, in particular, by the fact that the collection of the State Tretyakov Gallery in Russia contains about 500 works from the second half of the 20th century, executed in the technique of acrylic painting. As it turns out, acrylic paints can degrade over time, showing noticeable changes in color, gloss, and surface texture [2,3,4,5,6]. This deterioration has been demonstrated through accelerated aging studies involving thermal- and light-induced aging [2,7,8,9,10,11,12,13]. Artists’ acrylic emulsion paints contain various additives, such as coalescing agents, thickeners, defoamers, pH buffers, preservatives, surfactants, and wetting/dispersing agents, that influence performance properties [4,5,6]. These additives are designed to adsorb or chemically bond to pigment and extender surfaces to enhance dispersion stability, viscosity control, opacity, and durability [14]. However, environmental factors (UV exposure, heat, moisture, and oxidation) can degrade and destabilize these additives over time, altering the paint’s performance [4,5,6]. Accelerated aging studies have shown that degradation processes significantly enhance the migration of surfactants (polyethylene oxide (PEO)-based types, which predominate in acrylic emulsion formulations) from the base polymer matrix to the surface [3,7,15]. In particular, PEOs exhibit a tendency to oxidatively degrade over time. [16,17]. To investigate acrylic paint degradation mechanisms, attenuated total reflectance–Fourier transform infrared (ATR-FTIR) and micro-Raman spectroscopy are employed as powerful, rapid analytical techniques [8,18,19]. However, the interpretation of spectral data in acrylic paint aging studies presents a significant challenge due to the complexity and size of the datasets [8,20]. Chemometric algorithms, particularly principal component analysis (PCA) [20,21], can be effective for investigating the aging of acrylic paints using ATR-FTIR and micro-Raman spectroscopy.
The aim of this study was to investigate the aging of white acrylic ZnO-based artistic paints under artificial conditions: (1) checking color changes; (2) characterizing the evolution of the paint layer structure and assessing changes in the chemical composition of aged paint; and (3) clarifying the mechanism of paint degradation. The acquired findings provide a comprehensive understanding of degradation processes in commercial ZnO acrylic artists’ paint, demonstrating chemometrics (PCA) as an effective analytical methodology for spectral data analysis.

2. Materials and Methods

2.1. Sample Preparation

Commercial white acrylic artists’ paint based on ZnO (Pigment White 4, PW4) was used in this study. A uniform layer of ZnO-based white acrylic paint was applied to an aluminum foil substrate using a medium brush and then dried under ambient conditions (approximately 22 °C and 40% RH) for two weeks.

2.2. Artificial Daylight Aging

The accelerated UV aging of the paint samples was conducted in a Q-sun chamber under controlled conditions (38 °C and 65% relative humidity (RH)). The radiation was produced by a Xe lamp simulating outdoor solar conditions (λ > 290 nm), with a radiation intensity of about 60 W/m2. To study the kinetics of aging, individual probes of the sample were periodically taken. The maximum aging time in the chamber was 1963 h, which, taking into account that for the spectrum of sunlight, 685 Lux = 1 W/m2, corresponds to 8.1107 lux·h.

2.3. Colorimetric Measurements

Determination of colorimetric characteristics and reflectance spectra in the range of 400–700 nm was carried out with a ColorFlex spectrophotometer (HunterLab, Reston, VA, USA) in the following mode: 45/0°, an observation angle of 10°, and a D65 light source. The value of the color difference ΔE in the CIELab-76 system was used as the main criterion [22].

2.4. Scanning Electron Microscopy (SEM)

Electron microscopy images of aged samples were obtained using Prisma E (Thermo Fisher Scientific, Brno, Czech Republic). Samples intended for secondary electron imaging only were pre-coated with a 40 nm layer of gold by a Q150R ES plus sputter-coater (Quorum Technologies, Laughton, UK) and additionally mounted with a copper tape for better charge drain. Chemical mapping and elemental analysis of aged samples were carried out using energy-dispersive X-ray spectroscopy (EDS) in a low-vacuum mode.

2.5. Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) Spectroscopy

The infrared spectra of the paint samples were acquired in attenuated total reflectance (ATR) mode using a Vertex-70 FTIR spectrometer (Bruker, Bremen, Germany), with Platinum Diamond ATR. The spectra were recorded in the range of 600–4000cm−1, with a resolution of 4 cm−1, averaging 64 scans per measurement. For each sample, ten ATR-FTIR measurements were collected from different randomly selected surface areas.

2.6. Micro-Raman Spectroscopy

For comprehensive chemical analysis and a complete molecular fingerprint, micro-Raman spectroscopy was used as a complementary technique to ATR-FTIR. Raman spectra were acquired using a Raman microscope spectrometer, Senterra (Bruker), equipped with a λ = 785 nm excitation laser and a 50× objective with numerical aperture NA = 0.75. The spatial resolution of the microscope for measuring Raman spectra was estimated as 1.22·λ/NA ≈ 1.3 μm. For each sample, 10 Raman spectra were collected from different randomly selected points on the sample surface.

2.7. Principal Component Analysis (PCA) of ATR-FTIR and Micro-Raman Spectra

Spectral data acquisition was performed using software built into the Raman spectroscope–microscope and the ATR-FTIR spectroscope. The obtained raw spectral data of ATR-FTIR and Raman scattering were pre-processed, consisting of smoothing, baseline correction, wavenumber range selection, normalization, and analysis of outliers. Smoothing was performed using spectral filters, removing random noise using the Savitzky–Golay (SG) algorithm [23]. An iterative polynomial fitting method was applied for the estimation of the baseline in the Raman spectra [24]. The derivative treatment of data by the SG algorithm emphasizes band widths, positions, and separations, while simultaneously reducing or eliminating baseline and background effects. Differentiating data using the SG algorithm emphasizes the width of bands, positions, and splits, while reducing or eliminating baseline and background effects [25]. The positions of the peaks of the ATR-FTIR spectra were determined by recording the minima of the second derivative of the ATR-FTIR spectra or, in the case of poorly separated bands, additionally, by measuring the positions of the maxima of the fourth derivative of the same spectra. Principal component analysis (PCA) is a class of so-called unsupervised learning or pattern recognition methods [26]. In the present study, the purpose of using PCA was primarily to discover a hidden structure, such as a pattern or grouping, in a dataset without prior knowledge of the pattern itself. Mean centering was performed in PCA [27]. PCA was also used to detect outliers [28]. Pre-processing of spectral data and their subsequent mathematical analysis were carried out using home-made scripts written in Python 3.11 (2022) or MATLAB R2021a using the SVD, PCA functions, and the Savitsky–Golay algorithm.

3. Results

3.1. Colorimetry

The results of the color changes due to the samples’ aging are shown in Table 1. The results include the colorimetric changes in the values of the lightness/darkness (L*), red/green (a*), yellow/blue (b*), and the total color difference (ΔE*) from 0 to 1725 h of exposure. The color difference value ΔE in the CIELab-76 system used as the main criterion was determined using Equation (1):
ΔE = [(ΔL*)2 + (Δa*)2 + (Δb*)2]1/2
where ∆L* = L*i − L*0, ∆a* = a*i − a*0, and ∆b* = b*i − b*0, where index 0 refers to the sample before aging, and index i to the sample after a certain period of aging.
An ∆E* value below 2 indicates a color difference that is barely perceptible to the average human observer, whereas a value greater than 2 becomes perceptible by the human eye. By comparing the colorimetric values of the white paint samples during 1725 h of photoaging, we can realize that the ΔE* value is below 2. However, there is a slight increase in the a* value (shifted to red), likely due to the impurities in the extender and pigment. The rise in the L* parameter suggests the increase in lightness due to the changes in the surface roughness, as a rougher surface will scatter more light, increasing L* [29]. Also, the surfactant migration to the paint film surface can affect the gloss of this film [15], which, in turn, affects the L* values.

3.2. SEM Images

Figure 1 demonstrates SEM images of the sample before aging and samples aged for 1963 h. Detailed changes in the SEM images of the paint layer at different aging times are shown in Figure S1 (Supplementary Information). In the unaged sample, a uniform paint layer consisting of the binder and pigment is observed (Figure 1(A1,A2)). After 1963 h of aging (Figure 1(B1,B2)), significant microstructural changes were observed, including pronounced surface roughness and the appearance of pigment microcrystals, zinc oxide (ZnO), and calcium carbonate (CaCO3) [23]. The observed changes suggest the binder’s degradation. To reduce paint costs and increase its volume, CaCO3 is commonly used as an extender, partially replacing ZnO [2]. While CaCO3 influences paint stability, opacity, gloss, and film toughness, it indirectly influences acrylic binder deterioration [30,31]. CaCO3 scatters and reflects UV radiation, reducing direct UV exposure and offering partial protection to the paint film [17]. Particle size analysis (Figure 1(B1)) further indicated that the CaCO3 (calcite) grains ranged from 0.2 to 1 μm in size with the typical euhedral to subhedral habit [32], while ZnO particles (~100–200 nm) had a hexagonal sectional shape (wurtzite crystal structure) [33].

3.3. SEM-EDS Analysis of Zinc White PW4 Acrylic Paint

The results of the SEM-EDS analysis of the zinc white acrylic paint are summarized in Figure 2, and the analysis details are presented in Figure S2 and Table S1. In addition to carbon (C) and oxygen (O) peaks from the polymeric binder, the zinc (Zn) peaks confirmed ZnO as the primary pigment, while the calcium (Ca) peaks corresponded to the CaCO3 as an extender. SEM-EDS analysis showed significant differences in the distribution of ZnO and CaCO3 nanocrystals in the dyed layer of the sample, as shown in Figure 2. Areas of the sample with an elevated calcium content are clearly visible, shown in green in Figure 2B,D. The regions where zinc oxide dominates over CaCO3 are clearly visible in Figure 2C,D (red dots). The approximate size of the region of increased CaCO3 concentration is from ~3 to ~15 μm. Apparently, such heterogeneity of the sample layer can be manifested when measuring spectra with a Raman microscope with a resolution of about 1.3 μm and, possibly, when measuring ATR-FTIR spectra (see below).
The SEM-EDS results of the paint samples indicated spatial heterogeneity in the elemental distribution across the paint surface (Section S2). While most areas showed strong ZnO predominance (ZnO/CaCO3 > 20:1), isolated regions revealed near-equivalent ZnO and CaCO3 contents (~1:1 ratio), indicating CaCO3 agglomeration. This indicates that the surfactant and dispersant system used in the paint formulation does not provide a sufficiently uniform distribution of ZnO and CaCO3. Traces of elements (Si, S, and K < 1 wt%) were detected in localized regions, likely due to incidental impurities or residual processing additives.

3.4. Aging of Acryl Zinc White Paint Revealed by ATR-FTIR Spectroscopy

Figure 3 manifests the main motif of changes in the ATR-FTIR averaged spectra of a zinc white layer during aging, using a fresh and aged 1963 h sample as an example. In the ATR-FTIR spectrum of the fresh sample, the bands of the organic components of the paint dominate, whereas in the aged sample, the bands of the organic components are inferior in intensity to the band of the inorganic components, in particular, CaCO3. Figure 3 and Figure S3 (the detailed evolution of average spectra during aging) depict a pronounced degradation of the acrylic component peak at 1727 cm−1. The intensity of the CO32− peaks associated with CaCO3, in particular, the peak at 876 cm−1, increases relative to the peaks of the organic component. These changes suggest the degradation of organic components of the paint. The acquired ATR-FTIR spectra of the unaged paint sample (summarized in Table 2) were dominated by bands of the acrylic binder. The stretching vibrations of carbonyl ether groups C=O were observed at 1727 cm−1, and the stretching vibrations of the C-H bond at 2873, 2930, and 2956 cm−1, and the vibrations of ether groups (-C-C(=O)-O-) at 1159 and 1254 cm−1 can be tentatively attributed to acrylic chains. The bands corresponding to =C-H stretching (at 3083, 3061, and 3027 cm−1), C=C aromatic stretching (at 1603 and 1493 cm−1), and C-H bending (at 759 and 698 cm−1) obtained due to ν(C=C) bending and out-of-plane C-H bending (759 and 698 cm−1) suggest the presence of a styrene–acrylic copolymer blend as a binder. The stretching vibrations of C-O-C groups (at 1102, 1118, 992, and 1060 cm−1) were ascribed to a non-ionic surfactant polyethylene oxides (PEO) [3]. Weak bands at 1414, 874, and 712 cm−1 indicated the presence of calcium carbonate (CaCO3) [34]. A broad, weak band at 1563 cm−1, assigned to asymmetric stretching vibrations of COO− groups, suggested the presence of a poly(acrylic acid) (PAA), likely used as a dispersant, and likely forming metal–carboxylate complexes with the extender and pigment particles [3,35,36]. PAA also shows characteristic peaks of ester groups in the acrylic chain. Additionally, a weak shoulder at 1670 cm−1 was assigned to the hydrogen-bonded C=O stretching vibration from the interactions between residual carboxylic acid (-COOH) groups and binder carbonyl (C=O) groups.
The use of averaged spectra (Figure 3) is due to the fact that the spectra obtained from different points of the sample differ somewhat (see Figure S4). The deviation of spectra measured from different random points relative to the average spectrum increases in the region of organic compound bands as the sample ages, which is not observed for bands of inorganic compounds, such as CaCO3. These deviations in the spectra of the aged sample are apparently due to the uneven distribution of the paint components ZnO and CaCO3, revealed by SEM (Figure 2), as well as different degradation rates in different areas of the sample.
It can be assumed that the concentration of CaCO3 during the aging process of the sample is almost constant, since chemically inert inorganic CaCO3 is stable compared with the organic components of the paint. Therefore, to quantitatively characterize the kinetics of degradation of an organic binder, the ratio of the acrylate band intensity to the CaCO3 band intensity I1727/I876 can be used as a marker. The ratio of the intensity of the C-H band to the CaCO3 band (I2928/I876 and I3027/I876) can also be used as an additional marker. Figure 4 shows the averaged FTIR spectra normalized to the peak intensity at 876 cm−1. In this representation, Figure 4 manifests the kinetics of all markers I1727/I876, I2928/I876, and I3027/I876. The exponential fit predicts a characteristic degradation time of 469 h for the acrylic component of 1727 cm−1 (insert Figure 3B). The decay time is equal to 287 h for the band of 2928 cm−1 and 271 h for the band of 3027 cm−1.

3.5. Aging of Acryl Zinc White PW4 Paint Revealed by Micro-Raman Spectroscopy

Due to the high-spatial-resolution Raman microscopy (~1.3 µm) and the complex composition of the paint with a non-uniform distribution of paint components in the layer (see Figure 2), the Raman spectra collected from different points of the same sample turned out to be variable (see Figure S5). This fact apparently indicates a different rate of degradation of the binder at different points of the sample (see Figure S5). The averaged spectra for all points measured in the sample and their derivatives were analyzed to determine the positions of the Raman band peaks (Figure S6). The Raman peaks are summarized in Table 3. The Raman spectra of the samples (Table 3) revealed characteristic peaks corresponding to the vibrational modes of ZnO, which confirmed the presence of ZnO as a pigment in the wurtzite phase. The most intense peak at 438 cm−1 was attributed to the E2(high) mode, the shoulder at 328 cm−1 was attributed to second-order scattering (E2), a very weak shoulder around 384 cm−1 corresponded to the A1(TO) mode, and a weak peak at 546 cm−1 was associated with defective ZnO or probably originated from impurities [54,55]. Additionally, overlapping peaks in the region of 700–900 cm−1 were attributed to vibrations in the acrylic binder, PEO, and polystyrene. A strong peak appeared at 1006 cm−1, accompanied by a shoulder at 1033 cm−1, corresponding to aromatic ring vibrations in styrene. The peaks at 284, 712, and 1086 cm−1 were associated with CaCO3 [53,56,57]. These findings are consistent with the ATR-FTIR results.
The micro-Raman method was effective in detecting the ZnO peaks and aromatic ring changes corresponding to polystyrene. It is reasonable to assume that zinc oxide was not destroyed during the aging process of the sample. Based on this assumption, the peak intensity ratio I999/I876 can be used as a marker for quantitative characterization of the aromatic ring degradation kinetics. The averaged Raman spectra normalized to the 433 cm−1 peak associated with ZnO (Equation (S2)), corresponding to different aging times, are shown in Figure 5. The kinetics of the decay of the aromatic ring at 999 cm−1 are shown in the inset of Figure 5. The rate constant of aromatic ring decay estimated as an exponential fit was ~0.001 1/h. This was ~3.5 times slower than the C-H decay estimate made from the ATR-IR data (Figure 4). It can be assumed that such a difference in the degradation rate of different organic groups was due to their different localization relative to the ZnO crystals (see below).
Spectral variations that exist between different points of the same sample may be due to different rates of sample degradation or other uncontrolled experimental factors. All this makes the interpretation of Raman and ATR-FTIR spectra difficult without the use of chemometrics, which has become indispensable for eliminating confounding effects and extracting subtle spectral variations of interest from the measured spectra (see below).

3.6. PCA of ATR-FTIR Spectra

PCA was performed separately on the ATR spectra in the ranges of 600–1850 cm−1 and 2700–3700 cm−1, considering all samples over the aging period. The score plots (PC1 vs. PC2) for both spectral ranges (Figure 6) displayed distinct sample clustering by aging time, confirming progressive degradation.

3.7. In Spectral Range of 600–1850 cm−1

Principal component 1 (PC1, 71.5% variance) captured the dominant photodegradation trends in total (Figure 6A,B), where the positive loadings (representing decreasing peak intensities) revealed decreases in the following:
(a)
C-O stretching vibrations (at 1027, 1060, 1159, 1254, and 1267 cm−1) and carbonyl C=O stretching (at 1725 cm−1), indicating the degradation of the acrylic chains.
(b)
Characteristic polystyrene vibrations (at 700, 732, 760, and 1604 cm−1) and out-of-plane deformations (906–940 and 963 cm−1), confirming the polystyrene degradation.
(c)
COO− stretching (at 1562 cm−1), suggesting disruption of metal–carboxylate complex.
(d)
C=O (H-bonded) stretching vibration (at 1675, shoulder) attributed to the degradation of the dispersant PAA and further degradation of degradation products.
Conversely, negative peaks (representing increasing intensities) showed (a) CaCO3 bands (at 712, 874 and 1414 cm−1), as well as a broadening at 1527–1307 cm−1, reflecting a relative increase in the intensity of the CaCO3 band as a result of the degradation of the binder and dispersant (PAA), and (b) increased C-O-C stretching (at 992, 1102, and 1118 cm−1), consistent with the surfactant PEO migration to the film surface.
PC2 (18.46% variance) elucidated initial chemical changes in the paint film during the light exposure period (287–455 h). The spectra after 455 h of exposure exhibited the highest positive value in the PC2 scores plot, indicating the most advanced degradation state in this period. The PC2 loading highlighted the following spectral changes:
(a)
Asymmetric broadening of the carbonyl peak (at 1725 cm−1) toward lower wavenumbers (~1710 cm−1), indicating the formation of new C=O species from oxidative degradation of the PAA, acrylic, and styrene chains [7];
(b)
Narrowing of the carbonyl ester peak on the higher wavenumber side, consistent with oxidative cleavage of ester groups in the acrylic binder;
(c)
An increase in the shoulder intensity at 1675 cm−1, due to a change in hydrogen bonding-induced shifts in carbonyl (C=O) stretching frequencies, providing additional evidence for the formation of new C=O groups;
(d)
Broadening of the CaCO3 absorption band (1414 cm−1), especially near 1427 cm−1 and 1370 cm−1, due to the binder degradation and CaCO3 exposure;
(e)
Broadening of C-O-C stretching bands (at the peak at 1102 cm−1 and shoulder at 1118 cm−1), reflecting the migration and reorganization of the surfactant (PEO);
(f)
A decreased intensity at ~1620, consistent with the loss of the adsorbed water [3,45].
PC3 (3.46% variance) tracked intermediate aging changes in the paint film, where the spectra acquired after 621 h of light exposure showed the highest positive value in the PC3 scores plot (Figures S8 and S9). The corresponding PC3 loading plot (Figure 6B) revealed narrowing of the ester carbonyl peak, accompanied by a reduction in the H-bonded C=O band at 1675 cm−1, suggesting a decline in carboxylic acid (-COOH) groups due to further degradation.

3.8. In Spectral Range of 2700–3700 cm−1

PC1 (78.29% variance) represented the dominant degradation pattern, with positive loadings corresponding to a decrease in peak intensity for the following:
(a)
Stretching vibrations of CH3 and CH2 (at 2868, 2927, and 2959 cm−1) and =C-H (3027, 3044, 3060, and 3086 cm−1), indicating the breakdown and degradation of the (acrylic–styrene) binder chains;
(b)
The broad IR band (3130–3600 cm−1), attributed to O-H groups, suggesting weakened H-bonding between PEO and paint components, initial water desorption at the beginning of the aging, and the formation of volatile products as aging advanced.
PC2 (12.3% variance) reflected the significant baseline variations during the aging process. In the PC2 scores plot (Figure 7A), the sample aged for 455 h showed the highest value, confirming the early-stage transformations detected by PC2 in the 600–1850 cm−1 range. The corresponding loadings plot (Figure 7B) highlighted the transient changes, including the following:
(a)
Asymmetric broadening of the C-H stretching vibration band (at ~2700–2850 cm−1), likely corresponding to the formation of the degradation products, e.g., aldehyde at 2695–2830 cm−1 (with a weak Fermi doublet at 2720 and 2820 cm−1 registered for aged samples).
(b)
A non-uniform reduction in the peak intensity, where the PC2 positive loadings represented the spectral regions that were more resistant to decreases, such as symmetric stretching vibrations at 2873 cm−1. In contrast, negative loadings represented bands more susceptible to radical attack, including asymmetric stretching vibrations of the -C-H groups of alkenes and aromatic compounds.

3.9. PCA of Raman Spectra

The micro-Raman technique is particularly more effective for detecting the changes in ZnO and the aromatic ring (PS), owing to its high sensitivity to these characteristic molecular vibrations. In this study, PCA was applied to the spectral range of 150–1130 cm−1, where 300–1800 cm−1 covers the key vibrational modes of ZnO, PS, CaCO3, and C=O groups. Due to the high-spatial-resolution micro-Raman spectroscopy (spot size: ~1.3 μm) and the complex distribution of ZnO and CaCO3 in the paint layer (Figure 2), the Raman spectra were quite variable, as mentioned above. Before mean centering, two Raman spectral-scaling procedures were used for the PCA: vector normalization was applied to the spectral range of 150–1130 cm−1, and normalization was applied to the intensity of the ZnO peak at 433 cm−1 to the spectral window of 300–1800 cm−1. Scaling according to vector normalization leads to greater scattering in the score plot (see Section S7) (PCA of Raman spectra). Figure 8 shows a plot of the scores and loadings when the spectra are normalized to the ZnO peak at 433 cm−1. The score plot (Figure 8) shows trends in PC1 (82.6%) and PC2 (10.46%) changes with the aging time, similar to those observed for PC1 and PC2 of the ATR-FTIR spectra (Figure 6 and Figure 7). At the same time, PCA of the Raman spectra showed a wider spread of PC1 and PC2 scores associated with each specific aged sample, compared with similar scores for ATR-FTIR spectroscopy. The high spatial resolution of Raman microscopy makes it possible to detect areas with different rates of degradation of the organic binder, i.e., areas with a high ZnO content and areas with an increased CaCO3 content. For example, Figures S13e and S14 show that the Raman spectra of the sample aged for 1963 h can be divided into two groups of similar spectra, which can apparently be attributed to the region of fast and slow degradation of the polymer binder. This observation, together with the SEM-EDS data confirming the non-uniform dispersion of the filler with high ZnO or CaCO3 contents, indicates localized heterogeneous degradation of organics.
The spectrum of PS1 loading 1 (82.6%) is similar in shape to the spectrum of the organic component of the paint (Figure 8B). This suggests that changes in the PC1 scores during sample aging were associated with organic degradation. The spectrum of PC2 loading 2 (10.5%) shows that the change in PC2 during sample aging was associated with peaks at ~1000 cm−1 related to the aromatic ring, 1086 cm−1 related to CaCO3, 1600 cm−1 related to C=O, and a broad band consisting of several overlapping bands between 1230 cm−1 and 1450 cm−1, which can be tentatively attributed to various carbon–carbon and carbon–hydrogen bonds. The spectrum of PC3 loading 3 (~3.5%) reveals the peak at 1086 cm−1 corresponding to CaCO3. This suggests that the changes in the PC3 score corresponded to an increase in the CaCO3 peak intensity relative to the organic matter peaks during sample aging.

4. Discussion

Degradation Mechanism of Acrylic Paint ZnO PW4

The main degradation characteristics of ZnO paint can be summarized as follows: (a) ATR-FTIR and Raman spectroscopy studies showed that the degradation of ZnO acrylic paint is associated with the oxidation of the organic binder. (b) SEM revealed deep oxidation with the formation of volatile low-molecular-weight products. Apparently, deep oxidation of organic semi-products and volatilization of organics may be the reason for the relatively small value of the total color difference ΔE during the aging of the samples. (c) Raman spectroscopy–microscopy and ATR-FTIR spectroscopy displayed the uneven degradation of organic substances in the paint layer.
It is known that the presence of oxygen, light, and heat leads to the degradation of polymers [62]. Chain radical reactions can be initiated in primary photochemical reactions, followed by the autoxidation process (Scheme 1). Peroxy (O2•−), hydroperoxy HO2• radicals, and peroxyl radicals RO2• are far more stable than either HO• radicals or the carbon-centered radical R• and the related alkoxyl radicals RO•. The simplified Scheme 1 does not emphasize that the peroxyl radical ROO• is a low-reactivity radical, and Reaction (12) may be kinetically or thermodynamically disfavored for most organic components in polymers [62]. Thus, the deep and uneven oxidation of organic matter in ZnO paint is difficult to explain based solely on photochemically initiated autoxidation.
Photocatalytic reactions involving inorganic substances should be taken into account. CaCO3 is a wide-bandgap material. The indirect energy gap is estimated to be 5.07 eV (245 nm), and the direct allowed optical transitions along high-symmetry directions in the Brillouin zone give the fundamental absorption edge of ~6.0 eV (207 nm) [63]. As a result, CaCO3 is photochemically inert in aging experiments. ZnO is a direct-bandgap semiconductor with a bandgap energy Eg of ~3.2–3.4 eV [64]. It absorbs light in the ultraviolet (UV) range, approximately below a wavelength of ~385 nm. The flat band potential of ZnO ECB is around −0.46 to −0.88 eV vs. an NHE (normal hydrogen electrode). The EVB is the highest energy level in the valence band. It can be calculated using the following formula: EVB = ECB + Eg ~ 2.3 eV. The electron and hole can act as highly reactive agents [64]. Lipovsky et al. reported that ZnO nanoparticles induce increased formation of reactive oxygen species, namely, hydroxyl radicals and singlet oxygen, not only under UV light, but also under visible light, where a remarkable enhancement of the oxy radicals was also found (400–500 nm) [65].
Since ZnO is photocatalytically active and CaCO3 is not, the oxidation of the organic components in the region of a high CaCO3 concentration is slower than in the region of an enhanced ZnO concentration, where active radicals and oxygen species can be formed. This is probably the main reason for the uneven degradation rate of the organic binder.
PCA of the ATR and Raman spectra showed that the degradation mechanism of acrylic paint ZnO had clearly defined stages associated with the formation of intermediate products of organic oxidation. For example, the accumulation of carboxyl compounds leads to the formation of radicals due to the photo-Kolbe Reaction (5) with the formation of R• radicals [66,67]. Due to the reduction in the content of ester bonds revealed by ATR-FTIR, it can be assumed that, in parallel with the photocatalytic process, an important degradation pathway may occur through the oxidation of the C=O ester group, leading to cleavage at the α-carbon via Reactions (6) and (7) of the Norrish type I mechanism (α-cleavage). This side-chain scission mechanism generates two radical intermediates: (1) an α-carbon ester radical (•COOR′) that undergoes hydrogen abstraction from another organic molecule, forming volatile fragments, and (2) a new centered radical [36,43,68,69].
The PC3 of ATR-FTIR spectra revealed the dominant chemical changes occurring at the aging intervals (621 and 855 h), indicating an advanced stage of degradation. The narrowing in the C=O peak, along with the decline in the hydrogen-bonded C=O peak and the OH peak, suggested the oxidation of -COOH groups forming low-molecular-weight degradation products [70]. The progressive decrease in ester C=O and aromatic ring peaks intensities without emergence of new carbonyl shoulders confirmed that the binder degradation mechanism primarily involved the formation of volatile fragments.
The final aging intervals (1143–1963 h) were primarily represented by PC1 analysis of the ATR-FTIR and Raman data, reflecting the complete degradation pathway. The PC1 loadings (Figure 6 and Figure 8) revealed the key changes after 1963 h of aging compared with the unaged paint. Here, the progressive decline in the aromatic ring content, the reduction in bonded/non-bonded carbonyl bands, and the absence of the C=C peak indicated a more severe stage of binder degradation driven by oxidation and volatile loss [7]. Consistent with the SEM observations, no significant crosslinking between the binder chains was detected, since the IR bands of C-O-C associated with PEO did not show a shift or emergence of a new shoulder in this region. The continued decrease in the ATR-FTIR and Raman characteristic binder signals with aging further demonstrated that the primary degradation mechanisms were chain scission and volatile loss. Meanwhile, PC1 and PC2 of the Raman data revealed significant spectra discrimination at this stage due to heterogeneous degradation, reflecting extreme degradation of the organic binder in ZnO-rich regions (Figure S13e).

5. Conclusions

The color change caused by the aging of ZnO-based white acrylic artists’ paint was measured by the total color difference ΔE* in the CIELab-76 system. The ΔE* value was below 2 during 1725 h of sample aging (~7 × 107 Lux·h). It is generally accepted that the human eye notices a change in color when ΔE* > 2. At the same time, SEM, ATR-FTIR, and micro-Raman spectroscopy showed significant changes in the structure and chemical composition of the ZnO white paint layer during ~1000–1963 h of aging. SEM showed increased surface roughness, cavitation, and chalkiness with the progressive degradation of the binder. The SEM results indicate deep oxidation with the formation of volatile low-molecular products. Apparently, deep oxidation of organic semi-products and volatilization of organics may be the reason for the relatively small value of the color difference ΔE during aging of the samples.
Micro-Raman spectroscopy and, to some extent, ATR-FTIR spectroscopy revealed uneven degradation of organic components on the surface of the paint layer. Analysis of spectral data using the principal component PCA method showed that the degradation of organic components of the paint occurs through the mechanism of chain radical oxidation, with the initiation of the reaction as a result of the photocatalytic oxidation on ZnO microparticles. The uneven distribution of ZnO and CaCO3 in the paint layer has been demonstrated at the micro scale by SEM-EDS. Carboxyl compounds present in paint or accumulated during oxidation of organic components can also serve as a source of active radicals due to the photo-Kolbe reaction. PCA of micro-Raman data confirmed the photocatalytic role of ZnO in binder degradation, showing higher degradation rates in ZnO-rich regions compared with areas dominated by CaCO3.
This study demonstrated the effectiveness of principal component analysis (PCA) in analyzing complex datasets related to the degradation kinetics of acrylic art paints and can provide guidance for the development of more durable ZnO-based acrylic paints. Importantly, since ZnO-based paint is highly susceptible to photoinduced degradation, the results highlight the need for careful selection of additives, particularly dispersants and surfactants. In this study, it was shown that dispersant (PAA) decarboxylation induced by the ZnO photo-Kolbe reaction accelerated active radicals’ generation and binder degradation. In addition, the surfactant–dispersant system plays an important role in the optimal dispersion of the filler in the paint matrix, thereby avoiding differences in degradation rates throughout the paint film.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/heritage8100419/s1. Figure S1: SEM images of the ZnO-based white acrylic artists’ paint SEM images; Figure S2: SEM image of the sample after 1963 h of aging; Table S1: The relative content of elements at the points shown in Figure S2; Equations (S1)–(S3): Scaling and normalization of spectra; Figure S3: ATR-IR spectra of acrylic zinc white paint PW4 during aging at time points of 0, 287, 455, 621, 855, 1143, 1626, and 1963 h; Figure S4: ATR-FTIR spectra of acrylic zinc white PW4 samples at different aging times; Figure S5: Raman spectra of acrylic zinc white PW4 samples at different aging times; Figure S6: Raman spectra of acrylic zinc white paint PW4 during aging at time points of 0, 287, 455, 621, 855, 1143, 1626, and 1963 h in the form of the 2 d derivative; Figure S7: Three-dimensional plot of PC1, PC2, and PC3 scores of ATR-FTIR spectra in the range of 600–1850 cm−1; Figure S8: PC1 vs. PC3 scores of ATR-FTIR spectra in the range of 600–1850 cm−1; Figure S9: PC2 vs. PC3 scores of ATR-FTIR spectra in the range of 600–1850 cm−1; Figure S10: Three-dimensional plot of PC1, PC2, and PC3 scores of ATR-FTIR spectra in the range of 2700–3700 cm−1; Figure S11: PC1 vs. PC2 scores of ATR-FTIR spectra in the range of 2700–3700 cm−1; Figure S12: (A) PC1 vs. PC3 scores of ATR-FTIR spectra in the range of 2700–3700 cm−1. (B) PC1, PC2, and PC3 loadings of ATR-FTIR spectra in the range of 2700–3700 cm−1; Figure S13: PC1, PC2, and PC3 scores and loadings of Raman spectra; Figure S14: Hierarchical cluster analysis of the 1963 h sample spectra.

Author Contributions

Investigation, formal analysis, methodology, and writing—original draft preparation, M.K.; resources and formal analysis, V.I.; resources and data curation, A.G. (Artem Gusenkov); resources and data curation, A.G. (Alexandr Gulin); funding, M.S.; methodology and writing—original draft preparation, Y.D.; methodology and writing—original draft preparation, Y.K.; conceptualization, methodology, formal analysis, supervision, funding acquisition, and writing—review and editing, V.N. All authors have read and agreed to the published version of this manuscript.

Funding

This research was funded by the Russian Science Foundation, grant #25-78-20011.

Data Availability Statement

The data will be available upon request.

Acknowledgments

The experiments were carried out using the equipment of the Center for Collective Use of the Federal Research Center for Chemical Physics of the Russian Academy of Sciences “Analysis of Chemical-Biological Systems and Natural Materials”.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations were used in this manuscript:
ATR-FTIRAttenuated total reflectance–Fourier transform infrared spectroscopy
SEMScanning electron microscopy
SEM-EDSScanning electron microscopy with energy-dispersive X-ray spectroscopy
PEOPolyethylene oxide
VISVisible
UVUltraviolet
PCAPrincipal component analysis
RHRelative humidity
SGSavitzky–Golay algorithm

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Heritage 08 00419 g001
Figure 1. SEM images of the sample before aging (A1, scale bar: 4 µm; A2, scale bar: 10 µm) and samples aged for 1963 h (B1, scale bar: 4 µm; B2, scale bar: 10 µm).
Figure 1. SEM images of the sample before aging (A1, scale bar: 4 µm; A2, scale bar: 10 µm) and samples aged for 1963 h (B1, scale bar: 4 µm; B2, scale bar: 10 µm).
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Figure 2. SEM-EDS images of the sample stained with acrylic paint PW4. (A) SEM image. Points marked with (+) indicate areas of EDS analysis (the analysis results are documented in Section S2). (B) SEM-EDS image of calcium distribution in the stained sample. (C) SEM-EDS image of zinc distribution in the stained sample. (D) Superimposed SEM-EDS/SEM image, showing zinc (red) and calcium (green).
Figure 2. SEM-EDS images of the sample stained with acrylic paint PW4. (A) SEM image. Points marked with (+) indicate areas of EDS analysis (the analysis results are documented in Section S2). (B) SEM-EDS image of calcium distribution in the stained sample. (C) SEM-EDS image of zinc distribution in the stained sample. (D) Superimposed SEM-EDS/SEM image, showing zinc (red) and calcium (green).
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Figure 3. ATR-FTIR average spectra of the non-aged 0 h and aged 1963 h acrylic zinc white PW4 samples. The averaged spectra were obtained from ten spectra measured at random points on the sample. Spectra are normalized to the minimum–maximum scale (Section S1). (A) ATR-FTIR spectra in the form of second derivatives. (B) Averaged ATR-FTIR spectra. Insert: kinetics of decay of the I1727/I876 ratio with the time; exponential fit with rate constant of 0.00213 ± 0.00016 1/h.
Figure 3. ATR-FTIR average spectra of the non-aged 0 h and aged 1963 h acrylic zinc white PW4 samples. The averaged spectra were obtained from ten spectra measured at random points on the sample. Spectra are normalized to the minimum–maximum scale (Section S1). (A) ATR-FTIR spectra in the form of second derivatives. (B) Averaged ATR-FTIR spectra. Insert: kinetics of decay of the I1727/I876 ratio with the time; exponential fit with rate constant of 0.00213 ± 0.00016 1/h.
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Figure 4. ATR-FTIR average spectra at aging times of 0, 287, 455, 621, 855, 1143, 1626, and 1963 h. The color gradient indicates the progression of aging from black (average spectra at 0 h) to blue (average spectra at 1963 h). Spectra are normalized to the peak at 876 cm−1 (Figure S2). Insert (A): experimental points of kinetics I2928/I876. Solid line is exponential fit exp(-k·time), where k = 0.0035 1/h. Insert (B): experimental points of kinetics I3027/I876. Solid line is exponential fit exp(-k·time), where k = 0.0037 1/h.
Figure 4. ATR-FTIR average spectra at aging times of 0, 287, 455, 621, 855, 1143, 1626, and 1963 h. The color gradient indicates the progression of aging from black (average spectra at 0 h) to blue (average spectra at 1963 h). Spectra are normalized to the peak at 876 cm−1 (Figure S2). Insert (A): experimental points of kinetics I2928/I876. Solid line is exponential fit exp(-k·time), where k = 0.0035 1/h. Insert (B): experimental points of kinetics I3027/I876. Solid line is exponential fit exp(-k·time), where k = 0.0037 1/h.
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Figure 5. Raman spectra of ZnO acrylic paint samples at aging times of 0, 287, 455, 621, 855, 1143, 1626, and 1963 h. The color gradient indicates the progression of aging from black (average spectra at 0 h) to blue (average spectra at 1963 h). Scaling: all average spectra are normalized to the peak of 433 cm−1 associated with ZnO. This inset shows the decay of the peak of 999 cm−1 over time.
Figure 5. Raman spectra of ZnO acrylic paint samples at aging times of 0, 287, 455, 621, 855, 1143, 1626, and 1963 h. The color gradient indicates the progression of aging from black (average spectra at 0 h) to blue (average spectra at 1963 h). Scaling: all average spectra are normalized to the peak of 433 cm−1 associated with ZnO. This inset shows the decay of the peak of 999 cm−1 over time.
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Figure 6. PCA results of ATR-FTIR spectra in the range of 600–1850 cm−1. Vector normalization was used (Equation (S3)). (A) PC2 vs. PC1 scores. (B) PC1, PC2, and PC3 loadings.
Figure 6. PCA results of ATR-FTIR spectra in the range of 600–1850 cm−1. Vector normalization was used (Equation (S3)). (A) PC2 vs. PC1 scores. (B) PC1, PC2, and PC3 loadings.
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Figure 7. PCA results of ATR-FTIR spectra in the range of 2700–3700 cm−1. Vector normalization was used (Figure S3). (A) PC2 vs. PC1 scores. (B) PC1, PC2, and PC3 loadings.
Figure 7. PCA results of ATR-FTIR spectra in the range of 2700–3700 cm−1. Vector normalization was used (Figure S3). (A) PC2 vs. PC1 scores. (B) PC1, PC2, and PC3 loadings.
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Figure 8. PCA results of Raman spectra in the range of 300–1800 cm−1. (A) PC2 vs. PC1 scores. (B) PC1, PC2, and PC3 loadings.
Figure 8. PCA results of Raman spectra in the range of 300–1800 cm−1. (A) PC2 vs. PC1 scores. (B) PC1, PC2, and PC3 loadings.
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Scheme 1. The proposed degradation mechanism of the ZnO acrylic paint.
Scheme 1. The proposed degradation mechanism of the ZnO acrylic paint.
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Table 1. Colorimetric characteristics of the aged layer of ZnO white acrylic paint PW4.
Table 1. Colorimetric characteristics of the aged layer of ZnO white acrylic paint PW4.
Time, HoursL*a*b*ΔΕ*
094.75−1.294.040.77
12095.06−0.713.630.78
24095.19−0.73.780.75
36095.17−0.683.921.19
48095.76−0.664.141.18
73095.72−0.624.051.08
85095.63−0.694.221.08
97095.61−0.634.031.53
109095.81−0.623.661.31
124094.96−0.534.360.85
136095.56−0.554.071.1
148095.60−0.493.881.18
160095.64−0.443.741.27
172595.56−0.513.671.18
Table 2. Characteristic ATR-FTIR bands in white acrylic artists’ paint PW 4. A tentative assignment of peaks was carried out based on the analysis of the spectra in the form of second or fourth derivatives.
Table 2. Characteristic ATR-FTIR bands in white acrylic artists’ paint PW 4. A tentative assignment of peaks was carried out based on the analysis of the spectra in the form of second or fourth derivatives.
ATR-FTIRIR Absorption Band (cm−1)Functional Group AssignmentCompound Assignment
(cm−1)
The Present Study
3130–36003130–3600 -OH associated stretching vibrationPAA, aging products, water, and H-bonded [2,37]
3027, 3044, 3060, and 30863027–3059–3085=C-H stretching (aromatic)Polystyrene [20,25]
2927 and 29592956–2930-CH2 and -CH3 asymmetric stretching vibrationPolyethylene oxide (PEO) non-ionic surfactant, PAA, and acrylic medium [3,38,39]
2853 and 28682873 and 2856-CH2 and -CH3 symmetric stretching vibrationPolyethylene oxide (PEO) non-ionic surfactant, acrylic medium, and PAA [3,38,39]
17271725-C=O stretching vibrationAcrylic medium [3]
1675, 1686, and 16991675-C=O (H-bonded) stretching vibrationAcrylic medium and PAA [40]
1624 and 16371632C=C stretchingAging product [11,41,42,43]
16031600 Polystyrene [7,44]
1596–16241600 to 1636O-H bending vibrationAdsorbed water [3,45]
1560 and 15761562COO− asymmetric stretching (carboxylate)Metal–carboxylate complexes [35,36]
1487, 1500, and 15161492Aromatic C=C in-plane bending and C-C ring stretching [7]Polystyrene [37,38,44,46]
1448 and 14581452-CH2 bending vibrationPolystyrene [44], PAA, and acrylic medium [3,39,47,48]
14141414CO32− stretching vibration (ν3)Calcium carbonate extender [3,34,49,50,51]
1371, 1387, and 14011395–1300-CH3 and -CH2 bending vibrationPolyethylene oxide (PEO) non-ionic surfactant and acrylic medium [3]
1156, 1180, 1206, 1222, 1244, and 12661267, 1254, and 1159C-O stretching vibrationPAA and acrylic medium [15]
1095, 1110, and 11221118, 1102-C-O-C- stretching vibrationPolyethylene oxide (PEO) non-ionic surfactant [3,7]
1026 and 10661027–1064-C-O- stretching vibrationPAA and acrylic medium [38]
902, 912, 922, 940, and 963906–940–963C-H out-of-plane bending vibrationPolystyrene [38,52]
876874CO32− stretching vibration (ν2)Calcium carbonate extender [8,53]
843846C-H rocking vibrationAcrylic medium and polystyrene [42,44]
712712CO32− stretching vibration (ν4)Calcium carbonate extender [8,53]
701700, 732, and 760C-H bending vibration Acrylic medium [7,8] and polystyrene [44]
Table 3. Micro-Raman peak assignments for ZnO acrylic paint film before and after 1963 h of simulated daylight exposure.
Table 3. Micro-Raman peak assignments for ZnO acrylic paint film before and after 1963 h of simulated daylight exposure.
Raman Shift (cm−1)Functional Group AssignmentCompound Assignment
10861086Symmetric CO32− stretching (ν1)Calcium carbonate extender [53,56,57]
10341038C-H in-plane bending (aromatic ring)Polystyrene [58]
9991000C-H symmetric in-plane vibrations (aromatic ring)Polystyrene [20,38,58]
752, 799, and 843700–900Vibrations of C-O, -C-COO, C-O-C, C-C, and =C-H groupsPolystyrene, polyethylene oxide (PEO) non-ionic surfactant (PEO), and acrylic medium [59]
708712In-plane CO32− bending (ν4)Calcium carbonate extender [53,56,57]
619612 and 760C-H out-of-plane bending (aromatic ring)Polystyrene [38,58,60]
543546A1(LO) phonon mode (defective ZnO)Zinc oxide pigment [55,61]
433438E2(high) mode Zinc oxide pigment [54,55]
380384A1(TO) mode Zinc oxide pigment [54,55]
332328E2(low) modeZinc oxide pigment [54]
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MDPI and ACS Style

Khadur, M.; Ivanov, V.; Gusenkov, A.; Gulin, A.; Soloveva, M.; Diakonova, Y.; Khalturin, Y.; Nadtochenko, V. Oxidative Degradation Mechanism of Zinc White Acrylic Paint: Uneven Distribution of Damage Under Artificial Aging. Heritage 2025, 8, 419. https://doi.org/10.3390/heritage8100419

AMA Style

Khadur M, Ivanov V, Gusenkov A, Gulin A, Soloveva M, Diakonova Y, Khalturin Y, Nadtochenko V. Oxidative Degradation Mechanism of Zinc White Acrylic Paint: Uneven Distribution of Damage Under Artificial Aging. Heritage. 2025; 8(10):419. https://doi.org/10.3390/heritage8100419

Chicago/Turabian Style

Khadur, Mais, Victor Ivanov, Artem Gusenkov, Alexander Gulin, Marina Soloveva, Yulia Diakonova, Yulian Khalturin, and Victor Nadtochenko. 2025. "Oxidative Degradation Mechanism of Zinc White Acrylic Paint: Uneven Distribution of Damage Under Artificial Aging" Heritage 8, no. 10: 419. https://doi.org/10.3390/heritage8100419

APA Style

Khadur, M., Ivanov, V., Gusenkov, A., Gulin, A., Soloveva, M., Diakonova, Y., Khalturin, Y., & Nadtochenko, V. (2025). Oxidative Degradation Mechanism of Zinc White Acrylic Paint: Uneven Distribution of Damage Under Artificial Aging. Heritage, 8(10), 419. https://doi.org/10.3390/heritage8100419

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