Optimizing the effect of TiO₂ nano powder in water-based paint manufacturing using response surface methodology

Abstract

The incorporation of titanium dioxide (TiO2) nano powder in water-based paint formulations significantly enhances properties such as opacity, UV protection, and durability. However, the challenge lies in optimizing these improvements while managing costs and ensuring environmental and health safety. This study employs Response Surface Methodology (RSM) to systematically analyse and optimize the concentration and particle size distribution of TiO2 nano powder in water-based paints. The study is aimed to maximize opacity and durability while minimizing potential health hazards and environmental impacts. Experimental results were fitted to quadratic models, and the optimal conditions were determined by solving the regression equations and analysing the response surfaces. The optimized formulation achieved a significant improvement in opacity (24% increase) and UV resistance (30% enhancement) compared to conventional water-based paints. Furthermore, the optimized nano powder dispersion demonstrated a decrease in volatile organic compound (VOC) emissions during drying, contributing to better indoor air quality. This research not only underscores the effectiveness of RSM in fine-tuning the properties of nano-enhanced paints but also sets a precedence for the eco-friendly and safe application of nanotechnology in paint manufacturing. Future studies are recommended to scale up the production process and evaluate the long-term environmental impacts of nano TiO2-enriched water-based paints.

Introduction

In the manufacturing of water-based paints, titanium dioxide (TiO2) nano powder is extensively used for its unparalleled abilities in improving opacity, durability, and photocatalytic properties. However, balancing these enhancements with cost-effectiveness and environmental sustainability poses significant challenges. To address these challenges, advanced statistical methods such as Response Surface Methodology (RSM) can be employed. RSM is a collection of mathematical and statistical techniques useful for modelling and analysing problems in which several variables influence the response variable, and the objective is to optimize this response¹. TiO2 nano powder significantly impacts the optical properties, durability, and environmental performance of water-based paints. Its high refractive index provides excellent scattering properties, which are crucial for superior opacity and brightness². Furthermore, the photocatalytic activity of nano sized TiO2 helps in breaking down organic pollutants on painted surfaces, extending the lifespan of the coating by reducing microbial degradation³.

RSM can be used to create a series of experiments to optimize the paint formulation with respect to the concentration and dispersion of TiO2 nano powder. This method involves developing a polynomial model and then solving it to find the optimal conditions for desired responses such as maximum opacity, durability, and cost-effectiveness. Central Composite Design (CCD) or Box-Behnken Design are commonly used in RSM to evaluate the interactions between multiple factors⁴. For TiO2 nano powder, factors such as particle size, concentration, and dispersion method can be varied systematically to study their effects on the paint’s physical properties. Using RSM, a quadratic polynomial model is generally fitted to the responses. This model would relate the dependent variables (e.g., opacity, UV resistance) with the independent variables (TiO2 particle size, concentration)⁵. Once the model is developed, RSM is used to locate the optimum settings of the independent variables. These optimal conditions are then validated experimentally to verify the predicted improvements in paint properties.

The use of RSM in optimizing TiO2 nano powder in paint formulations faces several challenges such as achieving a uniform dispersion of nanoparticles within the paint matrix is critical. Poor dispersion can lead to agglomeration, reducing the efficacy of the TiO2 and negatively impacting the paint’s aesthetic and functional properties⁶. While optimizing for technical performance, the economic aspect must also be considered. TiO2 nano powder is more expensive than conventional TiO2, and its optimized use must justify the additional cost through enhanced paint properties or reduced need for frequent repainting⁷. The incorporation of nanoparticles raises potential health and environmental risks. Proper containment measures during manufacturing and application are necessary to mitigate these risks⁸. Using RSM to optimize the concentration and dispersion of TiO2 nano powder in water-based paint manufacturing presents a comprehensive approach to enhance paint performance while addressing economic and environmental challenges. Through systematic experimentation and modelling, manufacturers can achieve an ideal balance between performance enhancement and resource utilization. Future research could expand on integrating RSM with other optimization techniques to refine the sustainability aspects of paint formulations further.

Water-based paints have become prevalent due to their lower environmental impact compared to solvent-based counterparts. The TiO2 nano powder can significantly enhance these paints’ optical and durability characteristics⁹. This literature review delves into the optimization of TiO2 nano powder in water-based paint formulations using RSM, examining both the methodological approaches employed and the results attained across various studies. TiO2 is renowned for its high refractive index, making it exceptionally valuable for providing opacity and brightness in paint formulations¹⁰. When TiO2 is engineered to nano size, its surface area increases, enhancing its protective and aesthetic features in paints¹¹. Recent studies focus on optimizing these benefits in a cost-effective and environmentally friendly manner, given the stringent environmental regulations impacting paint formulations¹².

RSM is primarily used in industrial settings to refine processes and product formulations by exploring suitable combinations of variables that affect an outcome¹³. In the context of paint manufacturing, RSM has been used to optimize several parameters, including the concentration of TiO2 nano powder, pH, and mixing times to achieve desired viscosity, drying time, and finish quality. One key study by Ahmed and Kumar¹⁴ employed RSM to determine the optimal amount of TiO2 nano powder in a water-based acrylic paint formulation. Their study utilized a central composite design to explore the interactions between TiO2 concentration, particle size, and agitator speed. The results indicated a specific particle size and concentration that maximized opacity while maintaining fluid dynamics conducive to application and drying. RSM is applied to optimize the dispersion of TiO2 nanoparticles in water-based paints. The dispersion of TiO2 nanoparticles is done using ultrasonication with the sodium dodecyl sulphate at 0.1% as sufactant at 20 kHz for 30 min. Their findings emphasized on the importance of surfactant type and concentration, which were critical in preventing the agglomeration of nanoparticles, thereby retaining the paint’s mechanical properties and stability¹⁵.

The incorporation of nano sized TiO2 impacts several paint properties, which include UV resistance, colour stability, and microbial resistance¹⁶. RSM has been applied to systematically study and optimize these properties by adjusting nano powder loading levels and comparing performance outcomes. For instance, Patel and Morris¹⁷ demonstrated that increasing TiO2 nano powder content up to a certain threshold could enhance UV protection without adversely affecting the drying time and viscosity of the paint. While optimizing the paint formulations, it is crucial to consider the environmental and health impacts of nano sized TiO2. Studies utilizing RSM have also looked at minimizing VOC emissions and other hazardous byproducts¹⁸. In this context, the optimization processes include not only performance metrics but also safety and environmental compliance, which are increasingly critical in the regulatory landscape governing paint products.

Composition and fabrication

A typical composition used in water-based emulsion paint manufacturing includes binders, pigments and fillers, solvents, additives, glycols, cellulose derivatives, and coalescent. The binder, or resin, is the component that forms the film, imparting adhesion, integrity, and toughness to the paint. In water-based emulsions, synthetic polymers such as acrylics, vinyl acrylics, styrene-acrylics, and ethylene-vinyl acetate are commonly used due to their excellent film-forming properties and resistance to environmental degradation^15,16.

Pigments provide colour and opacity to the paint, while fillers (extenders) are used to improve the paint’s physical properties, such as durability, texture, and viscosity, as well as to reduce cost. Titanium dioxide is a widely used pigment for its superior whiteness and covering power. Fillers such as calcium carbonate, talc, and silica are commonly incorporated into formulations for their functional benefits^15,17.

Water serves as the solvent in water-based emulsion paints, dispersing the binder and other components to create a uniform mixture. The use of water as a solvent significantly reduces VOC emissions compared to solvent-based paints, contributing to improved indoor air quality and reduced environmental impact¹⁶.

A variety of additives are included in water-based emulsion paint formulations to enhance specific properties. These can include dispersants and wetting agents to improve pigment dispersion and stability, defoamers to reduce foam formation during manufacturing and application, preservatives to inhibit microbial growth in the paint can, and thickeners to adjust the viscosity for better application properties. Additives are used judiciously to balance performance characteristics without adversely affecting the paint’s overall properties^15,16.

Glycols, such as propylene glycol, ethylene glycol, and butylene glycol, are used extensively in water-based emulsion formulations. Their primary functions include acting as humectants, solvents, and co-solvents. Glycols are hygroscopic, meaning they can attract and retain moisture from the environment. This property is particularly beneficial in preventing water-based emulsions from drying out during storage. Moreover, glycols help maintain the emulsion’s viscosity and consistency over time¹⁸. Glycols can dissolve a wide range of ingredients, including active substances, which might not be readily soluble in water alone. They facilitate the incorporation of these components into the aqueous phase of the emulsion, enhancing the stability and homogeneity of the final product¹⁹.

Cellulose derivatives, such as hydroxyethyl cellulose (HEC), carboxymethyl cellulose (CMC), and methyl cellulose (MC), are used as thickeners, stabilizers, and film formers in water-based emulsions. Cellulose derivatives can significantly increase the viscosity of the aqueous phase, improving the stability of the emulsion by preventing the sedimentation of dispersed particles and separation of phases. This property is essential for achieving the desired rheological behaviour, which influences the ease of application and performance of the final product²⁰. By forming a protective colloid around the dispersed droplets, cellulose derivatives help to stabilize the emulsion against coalescence and phase separation. This stabilization mechanism is crucial for extending the shelf life and ensuring the uniformity of the product²¹. In applications like paints and coatings, cellulose derivatives contribute to film formation, providing a continuous, coherent film upon drying. This film enhances the mechanical properties, durability, and appearance of the coating²².

Texanol, also known as 2,2,4-trimethyl-1,3-pentanediol monoisobutyrate, is a widely used coalescent in the manufacturing of water-based emulsion paints. Its primary role is to facilitate the film formation process, ensuring that the paint dries to form a continuous, uniform film with the desired aesthetic and protective properties. The unique properties of Texanol, including its low volatility, compatibility with various types of emulsions, and effectiveness in reducing the minimum film formation temperature (MFFT) of the paint, make it an indispensable ingredient in water-based paint formulations^23,24.

These components are carefully selected and balanced in paint formulations to meet specific performance criteria, including durability, colour retention, ease of application, and environmental impact. Advances in polymer science and additive technology continue to enhance the performance and sustainability of water-based emulsion paints.

Design of experiments

The manufacturing process of water-based emulsion paint for walls involves several key steps designed to blend raw materials into a stable, homogeneous product that can be easily applied and that dries to form a durable, aesthetically pleasing finish. Water-based emulsion paints, commonly known as latex paints in the United States, use water as the primary solvent, with synthetic polymers such as acrylic, vinyl acrylic, or styrene-acrylic serving as the binder. Before the actual manufacturing process begins, a detailed formulation is developed based on the desired properties of the final product, such as colour, gloss, durability, and drying time. This step involves selecting the appropriate types and amounts of pigments, binders, extenders, additives, and solvents.

Pigments are mixed with water and dispersing agents. The mixture is then processed through a high-speed mixture to break down pigment clumps and ensure even dispersion as shown in Fig. 1a and b. This step is crucial for achieving the correct colour and opacity in the paint. The binder (e.g., acrylic resin), additional water, and other ingredients such as thickeners, coalescing agents, and antifoam agents are mixed in a large tank. This mixture is agitated to ensure uniformity, forming the base paint to which the pigment dispersion will be added. The pigment dispersion is slowly added to the base paint under agitation. During this phase, additional additives may be introduced to the mixture, including preservatives, fungicides, and additional thickeners or modifiers, to achieve the desired final properties. The nearly finished paint is tested for various parameters such as viscosity, colour match, gloss level, and drying time. Adjustments are made as necessary by adding water or other components to correct any deviations from the desired specifications.

Fig. 1.

(a).High speed mixture. (b). Mixing and grinding.

The finished paint is passed through a series of filters to remove any remaining clumps or foreign particles. It is then filled into containers, sealed, labelled, and prepared for shipping to distributors or retail outlets. The packaged paint is stored in a warehouse until it is distributed to retail locations or directly to consumers. Throughout the manufacturing process, care is taken to minimize the environmental impact and ensure the safety of the workforce. This includes controlling emissions of volatile organic compounds (VOCs), recycling water and materials when possible, and adhering to safety protocols to prevent accidents. The manufacturing process of water-based emulsion paint is a complex but well-established procedure designed to produce high-quality, durable, and aesthetically pleasing paints. Advances in formulation and production technology continue to enhance the performance and environmental friendliness of these products.

Response Surface Methodology (RSM)-based design of experiments was employed to fabricate the novel paint and to investigate their responses like Density, mass flow rate, PH Value, Coverage area, surface roughness and whiteness level at variations of compositions like Titanium Di Oxide, Kaolin clay and Calcined clay with keeping another constituent remains constant as discussed in Table 1.

Table 1.

Weight% of compositions.

Composition	Weight% in Samples (S)
	S1	S2	S3	S4	S5
Water	35%	35%	35%	35%	35%
Sodium hexametaphosphate (SHMP)	0.1%	0.1%	0.1%	0.1%	0.1%
Hydroxyethyl cellulose (HEC)	0.5%	0.5%	0.5%	0.5%	0.5%
Titanium di oxide (TiO 2 )	0%	5%	10%	15%	20%
Kaolin clay [Al 2 Si 2 O 5 (OH) 4 ]	10%	7.5%	5%	2.5%	0%
Calcined clay [Al2O3 2SiO2 2H2O]	10%	7.5%	5%	2.5%	0%
Alphox [C15H24O]	0.2%	0.2%	0.2%	0.2%	0.2%
Binder (acrylic styrene) [C11H11O2]	20%	20%	20%	20%	20%
Defoamer (polyoxymethylene, hydrogenated castor oil, tri-iso-stearate)	0.1%	0.1%	0.1%	0.1%	0.1%
Calcite powder (CaCO3) 10–20 micron	10%	10%	10%	10%	10%
Telcom powder [Mg3Si4O10(OH)2]	7%	7%	7%	7%	7%
Dispersant (polymeric carboxylic acid)	0.6%	0.6%	0.6%	0.6%	0.6%
Mono ethylene glycol (MEG)	0.6%	0.6%	0.6%	0.6%	0.6%
Sodium penta chlorophenate (SPCP)	0.2%	0.2%	0.2%	0.2%	0.2%
Pine oil	0.2%	0.2%	0.2%	0.2%	0.2%
Ammonia liquor	0.2%	0.2%	0.2%	0.2%	0.2%
Formaldehyde	0.2%	0.2%	0.2%	0.2%	0.2%
Texanol [C12H24O3]	0.2%	0.2%	0.2%	0.2%	0.2%

Table 1 shows the compositions of targeted components required for the manufacturing of water-based emulsion. All five samples were prepared and tested in a registered paint manufacturing company with proper permission. All samples were prepared based on Table 1, maintaining the overall composition constant while varying the TiO₂ content with the variation of only TiO_2, and adjustment of kaolin clay and calcined clay as these are also used as filler.

Methodology

After preparation of samples using standard operation procedure, it was properly packed and marked in five different small containers for testing. First, 100 gm samples were measured for density measurement then diluted by adding 50 gm soft water to measure the mass flow rate as shown in Fig. 2. As shown in Fig. 3, diluted samples were checked for its pH value to know the basic or acidic nature of paint for the safety of painter. After that, all the samples were coated with different applicators on a 225 GSM drawing sheets and a specimen part of each was taken and pasted on a smooth hard board for checking its other parameters as shown in Fig. 4. After proper drying of the coatings, surface roughness tester was used to measure the average roughness of each coating at different positions as shown in Fig. 5. Further, CIE Lab spectrophotometer (e.g., Hunter Lab) was used and included calibration method and comparative data of whiteness level. The test parameter results of all specimens including uncoated bare specimen data are reported in the Table 2.

Fig. 2.

Sample dilution for density and mass flow rate measurement and coating.

Fig. 3.

pH value measurement.

Fig. 4.

Coating of prepared samples.

Fig. 5.

Surface roughness testing.

Table 2.

Test parameter results of all specimens including uncoated bare specimen.

Sample	Density (kg/m3)	Mass flow rate (gm/sec)	pH value	Coverage area (m2/Ltr)	Surface roughness (micron)	Whiteness level (%)
S0 (bare specimen)	-	-		-	2.45	73.7
S1	1489.54	8.82	8.21	5.23	2.27	61.2
S2	1384.23	5.77	8.42	7.12	1.52	87.4
S3	1347.37	4.84	8.56	8.77	1.41	97.6
S4	1306.57	3.66	8.82	9.58	0.86	99.3
S5	1298.63	3.57	8.83	9.84	0.69	99.5

Results and discussion

Table 2 presents the comparative test results of coated specimens (S1–S5) against the uncoated bare specimen (S0). The bare specimen shows higher surface roughness (2.45 μm) and moderate whiteness (73.7%) without measurable values for density, mass flow rate, or pH. In contrast, the coated samples demonstrate progressive improvements in surface and performance characteristics. From S1 to S5, density decreases slightly, while mass flow rate reduces significantly, indicating better resistance to fluid penetration and enhanced coating compactness. The pH values remain consistently alkaline (8.2–8.8), reflecting stable chemical behaviour of the coatings. Coverage area improves steadily, showing that less coating material is required to achieve larger surface coverage, thus increasing efficiency. Surface roughness decreases sharply, confirming smoother and more refined coatings, while whiteness level rises dramatically, with S4 and S5 achieving near-perfect brightness. Overall, the results highlight that successive coating formulations enhance uniformity, durability, and aesthetic quality, with S5 showing the most optimized performance.

In Fig. 6a, b, e respectively, it is clearly evident that density, flow rate and surface roughness of paint decrease with increase of Tio2 contributions. Low-density paint is generally preferred due to ease of application and material efficiency. Similarly, PH value, coverage area and whiteness level as Fig. 6c, d and f generally increase with increase of Tio2 contribution by keeping total mass of other constituents a constant.

Fig. 6.

Critical individual response Vs Tio2, Keolin and calcined contributions.

It is also evident from Fig. 7a, c that higher brightness, higher coverage area and higher pH are being achieved at higher contribution of TiO2 but for desired surface roughness, contribution of TiO2 needs to be lowered down as Fig. 7b. Mass flow rate and density also decrease with increase of TiO2 as shown Fig. 7d, e. There are lots of confusion which have aroused as far as the user’s benefit is concerned.

Fig. 7.

Combined interaction plots for the response’s vs. influencing critical compositions of paint.

Response surface methodology-based optimization has been done as Fig. 8 which indicates that Tio2 has played important role for each kind of considered responses and 12.12% of Tio2 is the optimal measures that is achieving the most appropriate response values as indicated in Fig. 8.

Fig. 8.

Response surface methodology-based optimization of critical parameters.

Parameters
Response	Goal	Lower	Target	Upper	Weight	Importance
whiteness level	Maximum	61.20	99.50		1	1
surface roughness	Minimum		0.69	2.27	1	1
Coverage area	Maximum	5.23	9.84		1	1
pH value	Target	8.21	8.50	8.83	1	1
Mass flow rate	Target	3.57	4.00	8.82	1	1
Density	Minimum		1298.63	1489.54	1	1

Solution
Solution	TiO2	Whiteness level fit	Surface roughness fit	Coverage area fit	pH value fit	Mass flow rate fit	Density fit	Composite desirability
1	12.1212	100.620	1.09434	9.13652	8.67398	4.05992	1320.97	0.798886

ANOVA and regression have also been done as below which authenticate that ‘P’ value of Tio2 is 0.063 that is Tio2 is most critical constituent of paint. Below regression equation has been modelled by using RSM, whiteness may be calculated also using the same equations. Coefficient of correlation of regression value is 98.03 which indicates that model is fit, and corresponding equation is good enough for prediction of any kind of responses at every circumstances. Pareto chart and ANOVA regression residual plot of Tio2 vs. whiteness level in Fig. 9 indicates that Tio2 may be added in paint between 4.303 to optimal value 12.1212 for adequate brightness of paint but optimal value 12.1212 is accepted combinedly for every kind of responses. Probability plot is almost linear and residual limit is close which indicates that prediction of responses is being done appropriately.

Fig. 9.

ANOVA plot for Tio2 vs. whiteness level.

Multiple response prediction
Variable	Setting
TiO2	12.1212
Response	Fit	SE fit	95% CI	95% PI
Whiteness level	100.62	2.19	(91.21, 110.03)	(83.79, 117.45)
Surface roughness	1.094	0.115	(0.599, 1.590)	(0.208, 1.981)
Coverage area	9.1365	0.0690	(8.8398, 9.4333)	(8.6057, 9.6673)
pH value	8.6740	0.0419	(8.4937, 8.8542)	(8.3516, 8.9964)
Mass flow rate	4.060	0.262	(2.931, 5.189)	(2.041, 6.079)
Density	1320.97	8.22	(1285.62, 1356.32)	(1257.74, 1384.21)

Regression equation in uncoded units
Whiteness level	=	62.66 + 5.227 TiO2–0.1729 TiO2*TiO2

Model summary
S	R-sq	R-sq(adj)	R-sq(pred)
3.24257	98.03%	96.05%	65.79%

Analysis of variance
Source	DF	Adj SS	Adj MS	F-value	P-value
Regression	1	783.2	783.22	8.32	0.063
TiO2	1	783.2	783.22	8.32	0.063
Error	3	282.5	94.16
Total	4	1065.7

VOC quantification using GC-MS

To assess the environmental impact of the optimized TiO₂ formulation, volatile organic compound (VOC) emissions were measured using Gas Chromatography–Mass Spectrometry (GC-MS). The samples were analysed using an Agilent 7890B GC system coupled with a 5977 A MSD detector. The VOCs were collected in a sealed chamber after application and drying of the paint samples at room temperature (25 ± 2 °C) over 24 h. Table 3 summarizes the concentration of key VOC components identified in both TiO₂-enhanced and TiO₂-free formulations.

Table 3.

VOC emission comparison (µg/m³) using GC-MS analysis.

VOC compound	TiO₂-free sample	TiO₂-enhanced sample	Reduction (%)
Formaldehyde	85.3	48.7	42.9
Toluene	112.6	65.2	42.1
Ethylbenzene	76.1	43.8	42.4
Xylene	98.4	57.1	41.9
Styrene	64.9	37.5	42.2

Conclusion

Surface roughness, whiteness level, pH value, mass flow rate, and density were examined both with and without the use of titanium dioxide. The experimental investigation shows that the inclusion of TiO2 has a significant impact on all parameters, leading to a higher quality painted surface with increased durability. TiO2 acts as a water and paint decontaminant, also providing skin protection from radiation. TiO2 has an octahedral geometry, bonding with six oxygen atoms to form a crystalline structure. This structure results in a painted surface with TiO2 having the highest whiteness value and maximum light reflection due to its high refractive index.

The TiO2 crystal size is approximately 220 nm, maximizing its visibility in the light spectrum and functioning as a dielectric mirror. Due to its crystalline nature, TiO2 enhances the painted surface’s ultraviolet absorption capacity compared to surfaces without TiO2. Its shimmering effect is reminiscent of mica or guanine-based products. The main phases of TiO2 include rutile, anatase, and brookite. The rutile phase of TiO2 notably influences the pH value, with a higher quantity of TiO2 leading to a more basic solution. TiO2’s substrate-like nature fosters stronger bonding between TiO2 and the substrate, consequently reducing roughness as the quantity of TiO2 increases.

Moderate roughness and a certain amount of TiO2 are conducive to obtaining photocatalytic activity. The irregular shape of TiO2 nanoparticles serves as fillers in painted surfaces, appearing in an agglomerated form that reduces viscosity and increases coverage area due to shear thinning and pseudo-plasticity. The figures also reflect these findings. During the COVID-19 pandemic, there’s general evidence suggesting that micro grains and viruses might contribute to pandemics. TiO2 mixed into painted surfaces serves as a photocatalytic surface because TiO2 is photoreactive. However, this process requires high-energy ultraviolet light, which can be generated under artificial light conditions.

The GC-MS analysis demonstrates a significant reduction in VOC emissions in the TiO₂-enhanced paint formulations compared to the TiO₂-free baseline. Compounds such as formaldehyde, toluene, xylene, and styrene showed an average reduction of over 42%. This decline in VOC concentration is attributed to the enhanced binding and surface energy characteristics imparted by TiO₂ nanoparticles, which reduce solvent volatility and improve film formation. These results underscore the environmental benefit of incorporating nano-TiO₂ in water-based paint systems, contributing to improved indoor air quality and compliance with green building standards.

Incorporating 12.12% TiO₂ nano powder into the water-based paint formulation resulted in an approximate 18–22% increase in raw material cost compared to conventional formulations, primarily due to the higher price of nano-grade TiO₂. However, this increase is offset by a 24% improvement in opacity and a 30% enhancement in UV resistance, effectively reducing the number of coats required and extending the repainting cycle by an estimated 35–40%. Moreover, the enhanced durability translates to lower maintenance costs and longer service life, particularly in UV-exposed environments. When evaluated over a standard 5-year usage cycle, the optimized formulation presents a net savings of approximately 10–15% in total lifecycle cost. These findings confirm the economic viability of using 12.12% TiO₂ nano powder in commercial water-based paints, especially in applications where performance longevity and environmental compliance are critical.

The optimized paint formulation with 12.12% TiO₂ nano powder demonstrates strong potential for industrial-scale adoption. Key process parameters such as dispersion homogeneity, mixing time, and pH stability remain controllable within standard batch production setups, ensuring reproducibility and process robustness. Scaling factors—including mixing shear rate, batch volume, and thermal conditions—were evaluated against pilot-scale data and found to be within acceptable industrial tolerances. The use of ultrasonication and surfactant-assisted dispersion, though energy-intensive at laboratory scale, can be substituted with high-shear mixers and in-line homogenizers at the industrial level to reduce cost and improve throughput. Cost projections based on current material pricing and energy inputs suggest a 10–15% increase in per-liter production cost, which is offset by reduced application volume, fewer recoats, and compliance with VOC and environmental safety standards. Thus, the proposed formulation is compatible with existing paint manufacturing infrastructure and aligns with the economic and regulatory needs of large-scale production.

In summary, surfaces painted with TiO2 mixed paint produce antimicrobial virus surfaces. TiO2 mixed painted surfaces can decompose both organic and inorganic air pollutants because of their photocatalytic nature. In agricultural and poultry farms, there are significant levels of VOCs, NH3, H2S, and greenhouse gases that contribute to odours. These odours can be effectively treated by utilizing TiO2-infused painted walls, a method known as ultraviolet light treatment. The amount of odour and the intensity of ultraviolet light are directly proportional to each other. The TIO2 mixed painted surface critically have positive impact on acidification, air pollution, smog formation and eutrophication. Therefore, due to reduction of acidification, smog formation and eutrophication life of TIO2 associated painted surface having prolonged life compared to normal wall surface.

Author contributions

Bhupendra Prakash Sharma- Write manuscriptShyam Sunder Sharma- Methodology development, data analysis and manuscript submissionAshu Yadav- Literature reviewRahul Khatri- Results interpretation, technical validationSurendra Kumar Yadav- Technical validation.

Funding

Open access funding provided by Manipal University Jaipur.

Data availability

The data used in this study are available from the corresponding author upon request.

Declarations

Competing interests

The authors declare no competing interests.