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Article

Digital Tools in Action: 3D Printing for Personalized Skincare in the Era of Beauty Tech

by
Sara Bom
1,
Pedro Contreiras Pinto
1,2,
Helena Margarida Ribeiro
1 and
Joana Marto
1,*
1
Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, 1649-003 Lisboa, Portugal
2
PhD Trials, Avenida Maria Helena Vieira da Silva, n° 24 A, 1750-182 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Cosmetics 2025, 12(4), 136; https://doi.org/10.3390/cosmetics12040136
Submission received: 22 May 2025 / Revised: 21 June 2025 / Accepted: 23 June 2025 / Published: 25 June 2025
(This article belongs to the Special Issue Feature Papers in Cosmetics in 2025)

Abstract

3D printing (3DP) enables the development of highly customizable skincare solutions, offering precise control over formulation, structure, and aesthetic properties. Therefore, this study explores the impact of patches’ microstructure on hydration efficacy using conventional and advanced chemical/morphological confocal techniques. Moreover, it advances to the personalization of under-eye 3D-printed skincare patches and assesses consumer acceptability through emotional sensing, providing a comparative analysis against a non-3D-printed market option. The results indicate that increasing the patches’ internal porosity enhances water retention in the stratum corneum (53.0 vs. 45.4% µm). Additionally, patches were personalized to address individual skin needs/conditions (design and bioactive composition) and consumer preferences (color and fragrance). The affective analysis indicated a high level of consumer acceptance for the 3D-printed option, as evidenced by the higher valence (14.5 vs. 1.1 action units) and arousal (4.2 vs. 2.7 peaks/minute) scores. These findings highlight the potential of 3DP for personalized skincare, demonstrating how structural modifications can modulate hydration. Furthermore, the biometric-preference digital approach employed offers unparalleled versatility, enabling rapid customization to meet the unique requirements of different skin types. By embracing this advancement, a new era of personalized skincare emerges, where cutting-edge science powers solutions for enhanced skin health and consumer satisfaction.

1. Introduction

The cosmetics industry is continually evolving, driven by an unwavering commitment to enhancing product efficacy and consumer satisfaction through innovation, while fueling operational efficiency [1,2,3]. In the context of Industry 4.0, and with an eye toward Industry 5.0, significant investments are being directed toward technology-driven beauty solutions that leverage advancements in data analytics, artificial intelligence (AI), virtual and augmented reality, digitalization, and additive manufacturing [4]. Today’s landscape reflects the integration of big data collection for consumer insights, AI for powered diagnosis, virtual tools for product try-ons, and augmented reality for immersive product demonstrations. Digitalization facilitates scanning and skin analysis, while 3D printing (3DP) enables the creation of customized product formulations [5,6,7,8]. The convergence of these cutting-edge technologies is set to elevate the cosmetics industry, fostering pioneering concepts such as beauty tech and personalized beauty [9].
Likewise, this transformation largely responds to a more knowledgeable consumer base that seeks tailored solutions aligned with their beauty aspirations and needs. As cosmetics become increasingly accessible, today’s modern consumers are also becoming more discerning, in search of innovation and uniqueness [10]. A notable trend is the preference for selecting ingredients based on specific skin characteristics, as individuals pursue distinctive formulations that are perceived to outperform conventional off-the-shelf products [11]. Nonetheless, the urge for personalization encompasses a range of dimensions, from tailoring formulations, bioactive ingredients, fragrances, and colors, to customizing packaging. This provides a compelling alternative to the traditional one-fits-all products that have historically characterized the market.
Among the emerging technologies that are shaping the future of personalized skincare, 3DP has gained momentum as a highly versatile manufacturing platform, providing the flexibility to rapidly customize skincare products to meet individual skin needs and features [12]. It enables the fabrication of tailor-made skincare products with precise control over formulation, structure, and aesthetic attributes, while promoting sustainable practices via waste reduction [9,13,14,15]. Indeed, 3DP is rapidly redefining how cosmetic products—particularly skincare masks and patches—are designed, fabricated, personalized, and commercialized. Unlike conventional manufacturing processes, which rely on fixed molds and standardized dimensions, 3DP enables on-demand manufacturing with complete digital control. This allows patches to be tailored in macrostructure, microarchitecture (i.e., internal porosity network), and spatial distribution of bioactive ingredients, accommodating both anatomical variability and specific skin needs. These add-in features have been demonstrated to promote enhanced skin adhesion, augmented hydration capacity, and facilitate modulation of bioactives release, supporting greater mask comfort for users and optimized efficacy [16,17,18]. In addition, such customization is especially relevant for facial applications, where contours and concerns vary significantly between individuals [19]. This structural versatility facilitates the design of highly customized, anatomically fitted, and performance-oriented skincare products. Furthermore, the potential for personalization extends beyond physical dimensions: digital design files can incorporate user-specific parameters obtained through facial scanning or skin diagnostics, enabling patches to target localized concerns such as fine lines, wrinkles, dryness, or acne with enhanced precision [17].
Various cosmetic companies are already leveraging 3DP to design personalized skincare masks. A remarkable example is Neutrogena’s MaskiD™, which combines 3DP technology with skin analysis and facial scanning—Skin360®—data, to produce hydrogel masks. These masks boast varying active ingredients and structures to suit different face areas, ensuring a precise fit to users’ unique facial contours and specific skincare requirements [20]. AmorePacific (Iope), a South Korean company, has embraced a similar approach that personifies the principle of tailored technology for everyone. By acknowledging the unique differences in customers’ facial features and needs, the company has developed a smartphone app that scans users’ faces, enabling a personalized hydrogel mask to be 3D-printed in just five minutes [21,22]. As academic contributions, the following works are particularly noteworthy. Goyanes et al. [23], explored the practicability of producing anatomically adaptable salicylic acid anti-acne patches by integrating 3D scanning within the 3DP workflow. Manousi et al. [24] highlighted the development of personalized iota carrageenan-based 3D-printed face patches. These patches were tailored to address the requirements of different skin types by incorporating hydrating or antiseborrheic ingredients. However, despite the encouraging outcomes of these studies, the application of 3DP in cosmetic products remains relatively underexplored, particularly in tailoring the patches’ microstructure.
Therefore, this study explored for the first time the influence of patches’ internal network-design on hydration efficacy and advanced to the personalization of under-eye 3D-printed skincare patches. Specifically, the size and shape, internal design, compounds, color and fragrance of the advanced skincare patches were personalized according to each volunteer’s skin needs, conditions, and preferences using a straightforward biometric-preference-driven approach. Additionally, an affective analysis was conducted to assess the acceptability of the 3D-printed patches, providing a comparative evaluation against a non-3D-printed market option.

2. Materials and Methods

2.1. Materials

Type B gelatin powder was purchased from Acofarma (Madrid, Spain). Sucrose was obtained from Fisher Scientific (Hampton, NY, USA). Glycerin was acquired to Lacrilar (Torres Vedras, Portugal). Kholiphor® RH40 (INCI: PEG-40 Hydrogenated Castor Oil) was obtained from BASF (Ludwigshafen, Germany). Sepinov™ EMT 10 (INCI: Hydroxyethyl Acrylate and Sodium Acryloyldimethyl Taurate Copolymer) and Lanol™ 99 (INCI: Isononyl Isononanoate) were acquired from Seppic (Paris, France). Purified water was obtained by reverse osmosis and electrodeionization (Millipore® Elix 3 water purification system, MilliporeSigma, Burlington, MA, USA), followed by filtration (filter pore 0.22 µm) and sterilization. The fine fragrances selected for this study were acquired from Iberchem (Murcia, Spain): Apple Intense (Fruity, Reference: 1511410), Florencia (Floral, Reference: 1093667), Definitely Authentic (Sweet, Reference: 1567194), and Sea Minerals 0719 (Fresh, Reference: VO57364). The incorporated dyes were Tartracina (Sancolor, Barcelona, Spain), Azul Brilhante (Supplier: Proaromática—Aromas Alimentares, Lda, Forte da Casa, Portugal, Rojo Proquidet C-1064 (Proquimac Food & Pharma, Barcelona, Spain), and Paracelsus® Violet (Zeus Quimica, Matosinhos, Portugal). The pigment Colorona® Karat Gold MP-24 (Merck KgaA, Darmstadt, Germany) was also used. A commercial hydrogel patch was used as a control—Reference.

2.2. Formulations Development

A gelatin-based hydrogel formulation with printable properties was prepared in a water bath (Nahita International, London, UK) at 55 °C under discontinuous stirring for 4 h, prior to printing. The formulation contained gelatin (40% w/w), sucrose (10% w/w), glycerin (10% w/w) and PEG-40 hydrogenated castor oil (5% w/w). Color (0.03% w/w) and fragrance (0.2% w/w) were added to the ink after formulation development, considering each volunteer’s preference (see Section 2.5.3. Biometric-Preference-Driven Personalization). A gel-like formulation was also prepared to be applied beneath the 3D-printed patches to promote skin adhesion and comfort during application. In this formulation, a combination of Sepinov™ EMT 10 (0.75% w/w), glycerin (10% w/w) and Lanol™ 99 (1% w/w) was used. All the excipients were manually stirred at room temperature for 10 min until a homogeneous gel was formed.

2.3. 3D Printing

The printing procedures were carried out in an extrusion-based 3D printer (Allevi2, Allevi, Philadelphia, PA, USA) employing various tapered metal(M)-gauge(G) nozzles: M-25G, M-27G and M-30G nozzles. For hydration studies, six-layered patches (30 mm × 30 mm × 1.20 mm) with a square-shape and grid internal design were printed. For personalization studies, six-layered patches with an under-eye shape format were printed, with dimensions adjusted according to each selected volunteer’s anatomy (see Section 2.5.3. Size and Shape). Prior to printing, the square patch designs were personalized using the Allevi2 Online Slicer, whereas the under-eye patches were processed using Simplify3D® (Simplify3D, v.4.1.2, Cincinnati, OH, USA) and Cura (UltiMaker B.V., v.4.8.0, Utrecht, The Netherlands) software slicers. Printing parameters were set as constant according to preliminary extrudability tests: printing temperature (43 °C), printing pressure (15 PSI, M-25G nozzle and 30 PSI, M-27G and M-30G nozzles), printing speed (30 mm/s), layer height (0.2 mm), and line distance (LD, 1.3 mm).

2.4. In Vitro Studies—Physical Analysis of the Patches’ Hydration Performance

To physically demonstrate the impact of the patches’ internal design on hydration performance, a Franz cell system was used, simulating a plastic occlusion stress test (POST) approach [25] (see Section 2.5.2. Hydration Efficacy). First, the fully dried mass of occlusive and porous patches was recorded. The 3D patches were then placed over a synthetic membrane (Polyether Sulfone, PES membrane filters, Supor®, 0.45 µm, 25 mm, Pall Corporation, New York, NY, USA) and covered with a parafilm-gauze sandwich for 24 h. Water was used as the receptor phase to mimic skin water changes. After the incubation period, the patches were reweighed to determine their swelling variation over time, according to Equation (1):
S w e l l i n g   % = W 0 h W 24 h W 0 h × 100 ,
where W0h represents the initial weight of the patches, and W24h represents the weight after 24 h. Measurements were conducted in n = 6 for each patch design.

2.5. In Vivo Studies—From Sensitization to Hydration Efficacy and Personalization

The protocol for the in vivo assays was submitted to and approved by the Ethical Committee of PhD Trials® (http://phdtrials.com/, Opinion n° 005/2012, 15 June 2012). All studies were conducted at the PhD Trials® facilities in compliance with the regulations of the Helsinki Declaration (Good Clinical Practices), the Agence Française de Securité Sanitaire des Produits de Santé (AFSSAPS) and Infarmed. These measures ensured a thorough evaluation of all technical aspects during product application in humans. All assessments were performed in a fully controlled and acclimatized room (temperature: T = 21 ± 2 °C; relative humidity: RH = 55% ± 10%). Prior to enrolment, all participants provided written informed consent and completed an authorization form permitting the use and publication of their images.

2.5.1. Sensitization—Human Repeat Insult Patch Test (HRIPT)

A sensitization study was performed using the Marzulli and Maibach Human Repeat Insult Patch Test (HRIPT) protocol [26]. Briefly, occlusive patches (8 mm diameter) and patches with gel (20 µL per patch) were applied to the backs of healthy volunteers (n = 55). The Finn Chambers containing the samples were applied to the same skin area for three consecutive weeks—induction period. Each patch remained in place for 48 h, after which it was removed, the skin was evaluated, and a new patch was applied. Post-application skin reactions were scored according to the International Contact Dermatitis Research Group (ICDRG) [27]. Following a 3-week rest period, the challenge phase was initiated, during which new patches were applied using the same procedure as in the induction period. This study was conducted under the supervision of a dermatologist who evaluated any potential irritant or allergic reactions to the tested films.

2.5.2. Hydration Efficacy

For assessing hydration efficacy, a series of in vivo studies were conducted resorting to conventional (Capacitance and Transepidermal Water Loss (TEWL)) and advanced methods (confocal Raman spectroscopy (CRS) and Reflectance Confocal Microscopy (RCM)). The assays were performed under occlusion and in non-occlusive conditions, as detailed below. A crossover randomization scheme was used for patch applications, ensuring a balanced distribution between products and arms. A total of 10 healthy female volunteers participated in the studies, with an age range of 25 to 55 years, and mean age 28.9 ± 9.3 years.
Conventional Methods
To evaluate the impact of patch design on hydration performance, occlusive (Ocl_Patch, 100% Infill) and porous (Por_Patch, grid, LD = 1.3 mm, M-27G nozzle) patches were applied to the ventral side of the forearm of volunteers for 4 h, under non-occlusive conditions. Additionally, the effect of applying 100 µL of gel between the skin and the patches was also tested (Ocl_Patch + Gel and Por_Patch + Gel). At t_0 h and t_4 h, quantitative measurements of hydration and TEWL were obtained with a Corneometer® CM 825 and Tewameter® TM 300 (Courage—Khazaka Electronic GmbH, Koln, Germany), respectively. The data recorded were then transformed into a variation percentage (% T4-T0 h). To facilitate further analysis of the data, the software program Orange Data Mining v.3.36.1 (Bioinformatics Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia) [28], was utilized to map the data with the visualization programming tool, thereby generating a scatterplot matrix.
A POST was also performed as described elsewhere [25], following a 24-h application. In addition, occlusion was achieved by placing a layer of gauze ‘sandwiched’ between Parafilm sheets (Bemis™ Parafilm™ M Laboratory Wrapping Film, Neenah, WI, USA), which was then secured to the skin using self-adhesive fabric (Mefix®, Molnlycke, Gothenburg, Sweden). After this period, the patches were removed and TEWL data were continuously recorded for 30 min. These POST data points were then fitted to a validated bi-compartmental mathematical model using a modified simplex routine and software developed for Microsoft Office Excel® [25,29]. The model simulates two distinct compartments that reflect varying water content levels within the skin. Compartment 1 represents the epidermal barrier, characterized by low water content, while compartment 2 corresponds to the more hydrated deeper layers of the skin. Under these conditions, the TEWL decay curves can be accurately described using Equation (2):
T E W L = B + I ( e K e v a p × t e K h y d r × t ) ,
where B corresponds to the baseline effect, I is the common multiplicative parameter for both exponentials, Kevap represents the evaporation rate constant, serving as an indicator of barrier function, and Khydr refers to the hydration rate constant, which relates to water distribution changes between the two compartments.
To facilitate interpretation, these parameters were transformed using Equation (3):
t 1 / 2 _ e v a p = L n   ( 2 ) K e v a p ,
where t1/2_evap represents the time needed by the system to reduce water loss by half.
Confocal Raman Spectroscopy (CRS)
A CRS study was conducted to validate the previously recorded POST-derived hydration data by measuring water mass content across skin depth. The same cohort of volunteers who participated in the hydration performance conventional studies was enrolled to ensure accurate data comparison. Raman measurements were performed with a gen2-SCA confocal Raman spectrometer (RiverD International B.V., Rotterdam, The Netherlands), according to the instrument specifications and data acquisition procedures described by Caspers et al. [30]. Raman spectra depth profiles were recorded from the skin surface to the dermis (0–150 µm) using a 671 nm laser wavelength. Measurements were at 1-s intervals per spectrum, with an exposure time of 5 s and a 1 µm tracking, in the high wavenumber (HWN) region. To minimize biological lateral variation in skin composition, four frames were recorded at each measurement position, and the spectra were averaged. Additionally, two location sites per volunteer, patch type, and application condition were analyzed. The products were applied to the ventral side of the forearm of each volunteer using an occlusion procedure (POST technique as described in Conventional Methods; 24 h). The water mass content (%) profiles were determined from the intensity ratios of the Raman bands for water (OH stretch vibration at 3350–3550 cm−1) and protein (CH3 stretch vibration at 2910–2965 cm−1), followed by integration of the corresponding spectral peaks at sequential depth increments [31,32,33]. In addition, this assay also enabled the estimation of the mean stratum corneum thickness of the selected volunteers, based on the water concentration profiles obtained at basal conditions, as described by Bielfeldt et al. [34]. The data were further processed and interpolated using SkinTools 3 analysis software (RiverD International B.V., v.4.7, Rotterdam, The Netherlands), according to the methods described by Caspers et al. [30]. All results are reported as the mean ± standard deviation (SD), and the area under the curve (AUC) values were calculated for water mass content using trapezoidal integration.
Reflectance Confocal Microscopy (RCM)
Morphological and structural skin characterization were performed through sequential confocal imaging, capturing stacked images from the skin surface to a depth of 150 µm (corresponding to the papillary dermis) in 1.5-µm increments using RCM (VivaScope® 1500, Caliber I.D., Rochester, NY, USA). RCM uses a 830 nm diode laser with power < 22 mW at the tissue level, to generate high-resolution in vivo images at the cellular level. To ensure optical correction, an ultrasonic gel (Aquasonic 100 Gel; Parker Laboratories, Inc., Fairfield, NJ, USA) was placed between the water-immersible objective lens of RCM and the adhesive window. Image acquisition, including both single and composite sets, was automated using the Vivastack® software (Caliber I.D., v.1.5.12, Rochester, NY, USA).

2.5.3. Biometric-Preference-Driven Personalization

For personalizing the under-eye 3D-printed patches, different biometric and preference-driven approaches were tested: skin needs, patch size and shape, advanced design customization to specific skin conditions (acne and age spots), and consumer preferences (color and fragrance). The target population for this study consisted of volunteers with standard skincare habits, who are regular consumers of skincare patches, as per the inclusion criteria. A total of 10 healthy female volunteers participated in the study (age range 22–37, mean age 27.3 ± 4.9 years).
Personalization Survey
Before the personalization process, each participant received a link to a qualitative sensorial questionnaire via e-mail, hosted on Google Forms (Google LLC, Mountain View, CA, USA). The questionnaire included five questions regarding skincare patches usage (frequency, application goals, and skincare needs) and personalization preferences (color and fragrance). The survey can be consulted in Appendix A.
Skin Needs
The Visia-Complexion Analysis (CA)™ imaging system (Canfield Scientific, Parsippany-Troy Hills, NJ, USA) was used as a 2D skin analysis tool to assess individual skin needs. The analysis evaluated several parameters, including spots, wrinkles, texture, pores, UV- brown- and red-spots, and porphyrins. Higher percentiles (above 50%) indicated that the volunteer’s skin condition was better than the average of her age group, while lower percentiles suggested the reverse.
Size and Shape
The AEVA-HE2 system/software (Eotech, v.3, Marcoussis, France) was used as a 3D skin scanner to capture and analyze the under-eye contour for each volunteer. The system enabled further drawing, extraction, and flattening of the patch area. The extracted patch area (topo mode) was flattened using the shape compensation setting (0 mm) and saved as an stl. file. The Simplify3D® software (Simplify3D, v.4.1.2, Cincinnati, OH, USA) was then employed to slice the previously drawn structure.
Skin Conditions
To personalize the patches for specific skin conditions, such as acne and age spots, a targeted approach was employed by adjusting the internal design. This was achieved through a combination of digitalization and 3DP slicing strategies, allowing for contouring and filling of these areas.
Color and Fragrance
The selection of color and fragrance was based on the responses obtained from the personalization survey, specifically questions Q4 and Q5.

2.5.4. Affective Analysis

A biometric sensory analysis was also conducted to assess the acceptability of the patches, employing a facial action classification system (FACS) performed on Afets (PhD Trials®). To enhance the significance of the analysis, volunteers applied two types of under-eye patches: a 3D-printed patch, personalized according to their preferences, and a commercial non-3D-printed alternative patch, formulated to enhance skin hydration. Furthermore, volunteers were instructed to apply the previously tested gel beneath the 3D-printed patches. To minimize data bias, the selected commercial patch, which also contained a gel, was used as a reference. A surprise box concept was used, whereby volunteers were unaware of the specific product inside the box until the moment of application. The patches were applied following a randomized scheme controlled via Afets software (v.1.0.1), ensuring randomization between boxes: (Box 1: Reference, Box 2: 3D-Printed). To assess the emotional response during and after application, a recording setup was implemented where volunteers applied the patches while looking in a mirror: https://onlinemirror.net/ (accessed on 17 April 2025). The analysis based on FACS, was used to assess emotional valence, defined as the degree of pleasantness or unpleasantness associated with an experience. Emotions like joy are classified as positive valence, while emotions such as disgust fall under negative valence [35]. In addition, galvanic skin response (GSR) data was also collected to further assess arousal responses. Arousal is defined as the extent of emotional activation or intensity associated with an experience, encompassing a range of energy mobilization from calm to excited states [36]. Dormo® Ag/AgCl electrodes (TELIC S.A.U., Bigues i Riells del Fai, Barcelona, Spain) were placed on the inner area of the foot throughout the analysis for monitoring GSR. Furthermore, to align the emotional recordings with the volunteers’ verbal responses, the volunteers were asked to articulate their opinions about each product and provide comparisons.

2.6. Statistical Analysis

Prior to the analysis, normality was assessed using the Shapiro-Wilk test (p > 0.05 indicating normal distribution), and homogeneity of variances was verified with Levene’s test (p > 0.05 indicating equal variances). For inferential statistics, a One-Way ANOVA was performed to determine whether statistically significant differences existed between the means of hydration levels, TEWL variation at t_0 h and t_4 h, and AUC-derived mass water content from 0 to 18 µm. This was followed by the application of the post hoc Tukey multiple comparison test to analyze differences between groups. Values of p < 0.05 were considered statistically significant for all analyses. Eta-squared (η2) was also calculated to estimate the effect size. All data was organized and analyzed using IBM SPSS Statistics v.29 software (Armonk, NY, USA).

3. Results and Discussion

3.1. Sensitization Assessment

A HRIPT study was conducted to assess skin safety. No reactions were observed during the initial three-week application period or after the final challenge exposure. Therefore, the repeated application of both the patch and the patch combined with the gel did not induce sensitization. Overall, the tested formulations presented excellent skin compatibility, with minimal risk of irritation, sensitization, or allergic contact reactions.

3.2. Hydration Efficacy

3.2.1. Conventional Methods

The hydration-TEWL performance mapping is displayed in Figure 1A. Specifically, the hydration data indicates that, under no-occlusion conditions, the Ocl_Patch increased moisture content by 108.85%, whereas the Por_Patch exhibited an increase of 45.38%. The application of a gel beneath the patches increased hydration performance, independently of patch design. This enhancement may be attributed not only to the application of the gel, but also to the inherent capacity of the patches to undergo a certain degree of swelling under such conditions.
A one-way ANOVA confirmed a statistically significant difference between samples (p = 9.08E-12, η2 = 0.715). Post/hoc Tukey analysis revealed that the hydration variation (%) for Ocl_Patch was significantly higher than both the Blank area and Por_Patch + Gel (p < 0.001), Ocl_Patch + Gel was statistically significantly higher than Por_Patch (p < 0.001), Por_Patch + Gel (p < 0.01), and Blank area (p < 0.001), while Por_Patch + Gel also showed significantly higher hydration than the Blank area (p < 0.001). In addition, TEWL results further revealed greater variability for occlusive patches compared to porous ones. Notably, porous patches led to a decrease in TEWL after 4 h, regardless of gel application. One-way ANOVA confirmed a statistically significant difference between samples (p = 0.012, η2 = 0.245), and Tukey’s test revealed that TEWL induced by Por_Patch was significantly lower (p < 0.05) than that induced by Ocl_Patch.
Moreover, the map provides a clear overview of the relationship between hydration and water loss. The Ocl_Patch demonstrated the most significant increase in skin hydration, yet it also exhibited the highest levels of TEWL variation. This suggests that, despite its capacity to deliver enhanced moisture, it may not form an optimal barrier. Following the addition of the gel to the patch, there was a decrease in TEWL, which is likely to be a consequence of enhanced skin contact and the partial occlusion that was promoted by the material swelling. In contrast, Por_Patch consistently exhibited the lowest TEWL values, both with and without gel, indicating a more efficient barrier function. This phenomenon can be attributed to the porous patches elevated swelling capacity, which enables them to adapt more closely to the skin surface, thereby increasing the contact area. Despite yielding the most minimal hydration variation, Por_Patch demonstrated a superior capacity for moisture retention, underscoring the significance of structural properties in regulating TEWL, irrespective of the absolute hydration provided. Overall, these findings suggest that the internal geometry of the patches impacts hydration performance, likely due to the varying degrees of occlusion created by different designs.
Subsequently, a POST analysis was conducted to evaluate skin barrier function under occlusion. This involved applying an impermeable parafilm sandwich to the skin for 24 h to trigger water accumulation. Upon removal of the occlusion, water evaporated from the skin surface until equilibrium was restored. The time required to achieve this equilibrium relies on the dynamic water balance between deeper and more superficial skin layers. Consequently, the kinetic parameters obtained through this analysis are a valuable indicator of epidermal barrier integrity [25]. The experimental TEWL decay curves—desorption (Figure 1B), were used to estimate the evaporation half-life period (t1/2_evap) as presented in Figure 1C. This parameter reflects the skin barrier’s recovery time after occlusion-induced stress [25]. The Ocl_Patch showed a similar recovery profile to the control site (without patch, Blank area), whereas the Por_Patch showed a slower TEWL decay, suggesting that porous patches retained a higher amount of water on the skin surface compared to occlusive ones. These results are further supported by comparing the area under the curve (AUC) analysis, which quantifies the total amount of water involved in the 30-min test: 188.3 g/m2 for occlusive patches and 233.0 g/m2 for porous patches. This suggests that reducing occlusion, which is determined by patch design, particularly pore size, enhances hydration performance. The porous patches facilitated greater water retention in the stratum corneum, increasing skin hydration. The physical explanation for these findings is portrayed and discussed in Section 3.2.3.

3.2.2. Advanced Methods

Afterwards, CRS was used to quantify the water mass content per each applied product, providing deeper insight into the effect of patch design across skin depth. The data (Figure 2A) followed the same trend as previously discussed, with Por_Patch exhibiting the highest initial water mass content (44.48%), followed by Ocl_Patch (37.19%) and the control site (28.85%). Moreover, the AUC results (Figure 2A(i,ii)) further supported these findings. Statistical analysis using one-way ANOVA confirmed significant differences (p < 0.001, η2 = 0.818), and Tukey’s post hoc test indicated that: the AUC-derived mass water content for the control site was significantly lower than the blank area; Por_Patch was statistically significantly higher than both the control site and Ocl_Patch, but was lower than the blank area; and, Ocl_Patch showed significantly lower values than blank area. Overall, these results demonstrate a strong correlation between conventional and advanced biometric methods. Moreover, despite the relatively small number of volunteers, the data revealed statistically significant effects with meaningful effect sizes, which supports the robustness of the findings.
To further investigate these findings, RCM analysis was conducted. This method permits the visualization and assessment of changes in cell organization, compactness, presence of microcracks and irregularities, and detects fluorescence signals associated with water content, as well as changes in contrast and visual texture [37]. As illustrated by the single and composite image sets (Figure 2B), the application of the patches led to a more organized and uniform cellular structure, characterized by smaller dark areas due to fluorescence signals indicative of increased water retention. Furthermore, a reduction in microcracks, skin irregularities, and adjacent grooves was observed when compared to the control site, further supporting the increased hydration. Notably, the Por_Patch exhibited a more pronounced expansion of the stratum corneum, indicating greater water retention in keratinized cells, which is consistent with the previously discussed data.

3.2.3. Insights into the Physical Mechanisms Governing Patches’ Hydration

Skin occlusion can significantly increase stratum corneum water content, elevating it from its typical range to as high as 50%, while also raising skin temperature from 32 to 37 °C [38]. The data obtained show greater water accumulation in the stratum corneum for porous patches after POST application, prompting an exploration of the physical mechanisms behind this phenomenon. In occlusive patches, the hydration effect primarily derives from the patch’s inherent occlusion, despite the placing of the parafilm sandwich. Conversely, porous ones feature open spaces within their structure, leading to two combined sources of occlusion, the patch itself, and the parafilm sandwich. This dual occlusion generates a significant amount of water in the area, which is retained within the cavities (pores) of the porous patch, facilitating water exchange with the skin. Briefly, occlusive patches create two fully occlusive barriers, limiting the formation of a water vapor atmosphere between the patch and the skin, while porous patches allow more free space between the POST occlusion and the skin due to the structure of the pores, increasing water retention between the exterior and the skin. As a result, porous patches exhibited significantly higher swelling (108.68% ± 4.87) compared to occlusive patches (38.38% ± 2.23). This higher swelling in porous patches also leads to an increase in contact area, further enhancing hydration. Furthermore, the AUC values obtained for the blank area (Figure 2A(i,ii)) reinforce this conclusion, as this group had the highest mass water value (68.5% . µm), indicating that the greater the free space between the skin and the parafilm sandwich, the greater the water retention. Moreover, the CRS mass water analysis, performed immediately after POST occlusion removal, revealed that porous patches delayed water loss restoration, as evidenced by their higher t1/2_evap values (Figure 1C). Therefore, the higher water retention values observed in porous patches can also reflect this process.

3.3. Biometric-Preference-Driven Personalization

A series of proof-of-concept tests were carried out to validate the biometric-preference-driven personalization approach. These included multiple scenarios to evaluate the effectiveness of the setup in capturing and interpreting biometric data and assess the feasibility of merging multiple features into a single patch structure by leveraging the accuracy and precision of 3DP.

3.3.1. From Size and Shape to Skin Needs

To develop more complex, effective, and customized forms that meet current market needs, an under-eye patch was designed to match the volunteer’s unique skin characteristics in terms of size, shape, composition, and internal structure (Figure 3). A 2D imaging skin analysis system was used to assess the volunteer’s skin needs. This device captures high-resolution images that can be used to assess eight skin characteristics: general spots, wrinkles, texture, pores, UV-brown- and red-spots, and porphyrins. The system also compares the subject’s skin characteristics with aged-matched controls, generating percentile scores to position subjects within a population-based distribution. Additionally, this technology can assist individuals in comprehending their skin concerns and guide treatment decisions [5,39]. As shown in the skin analysis overview (Figure 3A), the selected volunteer exhibited 21% of wrinkles, primarily located in the periorbital region, 23% of texture—a parameter reflecting raised and depressed skin areas that indicate surface irregularities—and 48% of red spots.
Following the 2D skin assessment, a 3D analysis was conducted to design an anatomically corresponding under-eye patch, ensuring precise adherence to the unique contours of the periorbital and midfacial regions of the volunteer (Figure 3B–D). Later, employing a dual-3DP approach, it was possible to incorporate different bioactive compounds within a single patch structure. In this case, an anti-aging bioactive was incorporated into the ocular area, while a moisturizer and anti-spot compound were incorporated into the cheekbones’ region (Figure 3E). Overall, the printing outcomes substantiate that integrating advanced 3DP with digital biometric design, enables accurate, site-specific embedding of distinct bioactives within a single patch, underscoring the potential of this strategy for the development of multifunctional and personalized topical treatments. Moreover, these patches take around 10 min to be printed, which facilitates their further implementation in on-demand scenarios.

3.3.2. Skin Conditions

Based on the preceding evidence, the potential for personalizing the internal design of under-eye patches was explored, considering both the volunteers’ facial anatomy and the presence of specific skin conditions, such as acne and age spots (Figure 4A). First, a 3D scan of the under-eye area of a volunteer was created, and the patch area was digitally drawn and flattened (Figure 4A(i,ii)). Subsequently, the area affected by acne was contoured (Figure 4A(iii)), and the patch was sliced using two different fill types: occlusive (100% infill) and grid (LD = 1.3). In both cases, a precise contouring of the area was achieved (Figure 4A(iv)), providing a viable option for the application of under-eye patches without them directly contacting acne lesions. Additionally, attempts were made to fill the delimited area, allowing for the targeted delivery of specific bioactives for acne treatment, while other components could be incorporated in the rest of the patch. The results shown in Figure 4A(v) confirm the feasibility of this approach, exhibiting high-quality printing and precision. This method also allowed for modulation of the internal porosity using nozzles with different gauges.
To further demonstrate the versatility of this method, the strategy was applied to more complex areas with greater irregularity, such as age spots (Figure 4B(i)). The use of M-27G and M-30G nozzles resulted in optimal contouring and filling of the irregular zone (Figure 4B(ii)). Overall, these findings substantiate the efficacy and versatility of the developed gelatin-based ink, even when employed for printing small structures with simple or complex designs. Beyond the technological feasibility, the ability to apply bioactive ingredients specifically to targeted areas such as acne-prone skin or age spots marks a significant advancement in personalized skincare. This innovative approach has the potential to enhance skincare outcomes by promoting overall skin health while considering the unique needs and conditions of individual users.

3.3.3. From Consumer Insights to Preferences

Another desirable feature of personalized skincare under-eye patches is that they cater to consumer preferences (Figure 5). In this context, implementing co-creation strategies that actively engage potential users in the development process can promote a user-centered design framework, enhancing the likelihood that resulting products will be both functionally effective and emotionally captivating. To gain insights into users’ application goals, skincare needs, and fragrance and color preferences, a questionnaire was applied. The results revealed that the primary concerns among volunteers were skin hydration, enhanced appearance, and overall aesthetic satisfaction (Appendix A, Table A1). Furthermore, participants were asked to identify the most important elements they would consider when designing their own patches. As shown in the pie chart (Figure 5A), 50% of respondents prioritized the inclusion of bioactives, alongside considerations for application and comfort. Concerning visual and sensory preferences, greenish blue, pink, yellow, and light shades were the most favored colors, while fragrance choices were primarily fresh, floral, sweet, and fruity (Figure 5B). Based on these findings, the under-eye patches were personalized by incorporating a moisturizer to meet the volunteers’ skincare goals and 3D-printed in their preferred colors and fragrances (Figure 5C).

3.4. Affective Analysis

An affective analysis was also conducted to ensure that the personalized patches not only addressed individual needs and preferences but also delivered a high level of consumer satisfaction. The implementation of qualitative methodologies represents a valuable approach for elucidating the nuanced dimensions of consumer perception, which are frequently overlooked by exclusively quantitative techniques. Accordingly, participants’ emotional responses were monitored via video recording to assess their acceptance of the 3D-printed patches and to generate comparative insights against a commercially available, non-3D-printed alternative.
The FACS analysis revealed clear emotional responses from participants, particularly those of joy and disgust. These emotions were transformed into valence scores to facilitate a more precise interpretation of the emotional spectrum. As illustrated in Figure 6A, participants sensed a higher level of pleasantness for the personalized 3D-printed product, reflected in a notable increase in the valence scoring, both in the application and post-application (think) phases. This indicates a strong positive emotional response and a favorable perception of the product’s design and attributes. In addition, the arousal data (Figure 6B) revealed that the 3D-printed patch elicited a higher level of emotional activation compared to the reference (application: 4.2 vs. 2.7 peaks/minute; think: 5.9 vs. 2.4 peaks/minute). This suggests that the sensory attributes of the 3D-printed version were more stimulating and engaging for the participants. A heightened arousal response is often associated with increased attention and stronger emotional connection, which may positively influence product desirability [36,40]. These findings, combined with the previously discussed valence results, highlight the potential of the 3D-printed patch to deliver a more emotionally impactful user experience.
Moreover, factors such as aesthetic appeal, ease of use, and comfort during application play a critical role in shaping emotional responses, which can in turn influence the perceived efficacy of the product and overall consumer satisfaction [41]. This interpretation was further supported by participants’ verbal feedback, as presented in Table A2 (Appendix A). In general, volunteers expressed a clear preference for the 3D-printed product, frequently highlighting its more visually appealing color, larger patch size, long-lasting refreshing effect, and greater comfort upon application (see thematic codes attributed per volunteer in Table A2, Appendix A). These opinions align with recent literature, which emphasizes the growing consumer demand for simplicity, efficacy, and comfort in cosmetic products, particularly facial masks [18]. Furthermore, 50% of volunteers expressed a clear preference for the color of the 3D-printed patches, which aligns with existing literature emphasizing the role of sensory attributes—such as color and fragrance—in influencing product pleasantness [42].

4. Conclusions

This study provides an in-depth exploration of effective personalization strategies, focusing on the impact of internal design on hydration and the adaptability of 3D-printed patches in terms of size, shape, internal design, composition, color, and fragrance. This innovative approach offers unparalleled versatility, enabling rapid customization to meet the unique requirements of different skin types. Moreover, the affective results suggest that personalization in product design can foster deeper emotional engagement among users. By embracing this advancement, the beauty industry moves toward a new era of personalized skincare, where cutting-edge science powers next-generation solutions for enhanced skin health and consumer satisfaction.
Future studies should explore the cost-effectiveness of 3D-printed patches in comparison to commercially available alternatives, as this aspect will be critical for their translation into scalable and economically viable cosmetic applications. Furthermore, subsequent studies should include a larger and more ethnically and gender-diverse cohort, including male participants and a wider array of skin types/conditions. Long-term hydration assessments should also be performed to comprehensively validate and expand upon the existing findings.

Author Contributions

S.B.: Conceptualization, Methodology, Investigation, Writing—original draft preparation. P.C.P.: Conceptualization; Methodology; Writing—review and editing; Supervision. H.M.R.: Writing—review and editing; Supervision. J.M.: Conceptualization; Writing—review and editing; Supervision; Project Administration. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Fundação para a Ciência e Tecnologia, Portugal (UID 04138 to iMed.ULisboa, CEECINST/00145/2018 to J.M.), and fellowship UI/BD/153624/2022 (https://doi.org/10.54499/UI/BD/153624/2022) to S.B.). This research was also funded by Programa Regional Lisboa (Lisboa 2030) (LISBOA2030-FEDER-00580400).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, the Agence Française de Securité Sanitaire des Produits de Santé (AFSSAPS) and Infarmed. Approval was received from the Ethical Committee of PhD Trials® (http://phdtrials.com/, Opinion n° 005/2012, 15 June 2012) for studies involving humans.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data will be made available upon request to the authors.

Conflicts of Interest

PhD Trials is an International Contract Research Organization (CRO), which is engaged in the clinical assessment of the safety and efficacy of products for topical application. The company provided the equipment, facilities, and technical support necessary to carry out the in vivo studies reported. Moreover, the research was conducted with the company’s principal investigator, who is a co-author of this work. We also confirm that no conflicts of interest exist related to this work. Specifically, the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors alone are responsible for the content and writing of this article.

Abbreviations

The following abbreviations are used in this manuscript:
3DP3D Printing
AIArtificial Intelligence
AUCArea Under Curve
CRSConfocal Raman Spectroscopy
FACSFacial Action Classification System
GGauge
HRIPTHuman Repeat Insult Patch Test
ICDRGInternational Contact Dermatitis Research Group
INCIInternational Nomenclature Cosmetic Ingredient
LDLine Distance
MMetal
OclOcclusive
PorPorous
POSTPlastic Occlusion Stress Test
RCMReflectance Confocal Microscopy
TEWLTransepidermal Water Loss
VolVolunteer

Appendix A

Personalization Survey

  • Q1. How many times a month do you usually use face masks? Selecet 1 option.
1
2
3
4
+4
  • Q2. Why do you like using face masks? What is your skincare goal? (Open Question)
  • Q3. If you could create your own face mask, what would be most important to you? Select up to 2 options.
Color
Application and Comfort
Fragrance
Size and Shape
Bioactives
  • Q4. In terms of color, what would be your preference? Indicate 2 color options. (Open Question)
  • Q5. What are your fragrance preferences? Select 1 option.
Sweet
Woody
Citric
Fruity
Floral
Fresh
Table A1. Personalization survey: responses to question Q2. skincare goals.
Table A1. Personalization survey: responses to question Q2. skincare goals.
Volunteer IDResponse
Vol01Intense treatment and care.
Vol02To get a clear skin, reduce signs of fatigue and prevent aging.
Vol03I like using face masks because they give my skin that extra boost it needs. It is a relaxing ritual that helps to deep cleanse, hydrate or just leave my skin feeling refreshed and glowing. My skincare goal is to have healthy skin that looks and feels great.
Vol04Hydration.
Vol05Hydration.
Vol06Fondness for self-care and the feeling of velvety skin after using a mask. Aim for intensive hydration.
Vol07Hydration.
Vol08Improve my skin appearance.
Vol09Hydration.
Vol10To have a good-looking, moisturized and beautiful skin.
Table A2. Participants’ opinion: Reference (Box 1) vs. 3D-Printed (Box 2). Following the application of both products, which one did you prefer?
Table A2. Participants’ opinion: Reference (Box 1) vs. 3D-Printed (Box 2). Following the application of both products, which one did you prefer?
Volunteer IDResponsePreferences
Vol01I liked the product in box 2 better. It was more refreshing. Easier to apply. It is much more comfortable when applied to the skin. The surface area also helped a lot. The color is also very pleasant, and I can notice a subtle shine in the product. For me, 2 was without a doubt the best.Box 2
Refreshing effect
Comfort
Patch size
Color
Vol02I liked box 2 more, because in addition to the product being light, it also has a larger skin coverage area. I felt it was more refreshing. I feel that the product in box 1 has an oily application, while the product in box 2 does not, I really liked the sensation.Box 2
Light
Patch size
Refreshing effect
Vol03Overall, I liked both products. However, the patch from box 2 was the one I liked the most. I feel the color is more attractive. I feel a good freshness. And I feel that the fact that it is bigger, covers a larger area of the face and that’s why I liked this factor more. I feel like it could be part of my skincare routine.Box 2
Color
Refreshing effect
Patch size
Vol04I liked the product I put on the left side (box 2) better. Since it has a larger area, it covers more. I like this part the most, because it will give me better hydration. And the feeling I have of refreshed and awake skin is greater and lasts longer with this product, compared to the other.Box 2
Patch size
Hydration
Refreshing and awake effect
Vol05I would say box 2. I like the color. I feel it has good structure, consistency and resistance. It feels much more comfortable on my skin than when applying the product from box 1. The one from box 1, I feel, is very dense. While the one in box 2 is much more comfortable.Box 2
Color
Structure
Comfort
Vol06I liked the one in box 2 better. It’s more comfortable. It doesn’t feel so wet on the skin. I liked the smell. The shape is also very pleasant and doesn’t bother me as much. I liked the color. It’s nice, I liked the one in box 2 better.Box 2
Comfort
Fragrance
Patch Format
Comfort
Color
Vol07I liked box 1 better. The feeling of freshness continues to be felt more in box 2 than in box 1. However, I enjoyed the application of the first one and prefer the texture of the patch itself.Box 1
Application
Texture
Box 2
Refreshing effect
Vol08I liked the first one better, more pleasant on the skin. Fresher. And I didn’t like the smell of box 2.Box 1
Comfort
Refreshing effect
Vol09I liked the patch on box 2 better. Not only is the color blue (I love it). It’s also wider and more comfortable on the skin. I feel that a larger area is being moisturized. Besides having to apply the gel beforehand, which gives the skin more freshness. Whereas the one in box 1, apart from being difficult to pick up due to its slippery texture, doesn’t leave the skin as moisturized and doesn’t smell as good.Box 2
Color
Comfort
Patch size
Refreshing effect
Hydration
Fragrance
Vol10The one in box 2, for the moisturizing sensation and the icy feeling it gives.Box 2
Moisturizing sensation
Refreshing effect

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Figure 1. Impact of patch design on hydration performance—Conventional methods. (A) Mapping the influence of different formulations on hydration variation (%) and TEWL variation (%). Hydration changes are represented by color gradients, while the area of each symbol is proportional to the TEWL values. (B) POST-derived TEWL decay curves, highlighting the AUC values (g/m2) per patch design extrapolated from the 30-min profiles. (C) Evaporation half-life (t1/2_evap) calculated from the POST assay, in function of patch design. AUC, Area Under Curve; Ocl, Occlusive; Por, Porous; POST, Plastic Occlusion Stress Test; TEWL, Transepidermal Water Loss.
Figure 1. Impact of patch design on hydration performance—Conventional methods. (A) Mapping the influence of different formulations on hydration variation (%) and TEWL variation (%). Hydration changes are represented by color gradients, while the area of each symbol is proportional to the TEWL values. (B) POST-derived TEWL decay curves, highlighting the AUC values (g/m2) per patch design extrapolated from the 30-min profiles. (C) Evaporation half-life (t1/2_evap) calculated from the POST assay, in function of patch design. AUC, Area Under Curve; Ocl, Occlusive; Por, Porous; POST, Plastic Occlusion Stress Test; TEWL, Transepidermal Water Loss.
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Figure 2. Impact of patch design on hydration performance—Advanced methods. (A) Hydration performance analyzed by Confocal Raman Spectroscopy. (A,i) Water mass profiles from 0 to 18 µm, corresponding to the mean stratum corneum thickness of the selected volunteers. (A,ii) AUC values extracted from (A,i) profiles and presented as % . µm. (B) Reflectance Confocal Microscopy images showing stratum corneum depth, after 24-h POST occlusion, recorded from a single volunteer (n = 1). Data sets with significant differences are identified with * p < 0.05, ** p < 0.01 and *** p < 0.001. AUC, Area Under Curve; Ocl, Occlusive; Por, Porous; POST, Plastic Occlusion Stress Test.
Figure 2. Impact of patch design on hydration performance—Advanced methods. (A) Hydration performance analyzed by Confocal Raman Spectroscopy. (A,i) Water mass profiles from 0 to 18 µm, corresponding to the mean stratum corneum thickness of the selected volunteers. (A,ii) AUC values extracted from (A,i) profiles and presented as % . µm. (B) Reflectance Confocal Microscopy images showing stratum corneum depth, after 24-h POST occlusion, recorded from a single volunteer (n = 1). Data sets with significant differences are identified with * p < 0.05, ** p < 0.01 and *** p < 0.001. AUC, Area Under Curve; Ocl, Occlusive; Por, Porous; POST, Plastic Occlusion Stress Test.
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Figure 3. Design and printing features of the under-eye patches: From 2D scanning and analysis to size/shaped-fitted patches that address specific skin needs. (A) Skin assessment using Visia-CA™ (Canfield Scientific, Parsippany-Troy Hills, NJ, USA), detailing the percentage of spots, wrinkles, texture, pores, UV-brown- and red-spots, and porphyrins. (B,C) 3D scanning, drawing, and extraction of the patch area using AEVA-HE2 system. (D) Patch surface flattening with AEVA-HE2 software (Eotech, v.3, Marcoussis, France) and slicing with Simplify3D® (Simplify3D, v.4.1.2, Cincinnati, OH, USA) software. (E) Final 3D-printed personalized under-eye patches. 3DP, 3D Printing.
Figure 3. Design and printing features of the under-eye patches: From 2D scanning and analysis to size/shaped-fitted patches that address specific skin needs. (A) Skin assessment using Visia-CA™ (Canfield Scientific, Parsippany-Troy Hills, NJ, USA), detailing the percentage of spots, wrinkles, texture, pores, UV-brown- and red-spots, and porphyrins. (B,C) 3D scanning, drawing, and extraction of the patch area using AEVA-HE2 system. (D) Patch surface flattening with AEVA-HE2 software (Eotech, v.3, Marcoussis, France) and slicing with Simplify3D® (Simplify3D, v.4.1.2, Cincinnati, OH, USA) software. (E) Final 3D-printed personalized under-eye patches. 3DP, 3D Printing.
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Figure 4. Design and printing features of the under-eye patches: From 3D scanning to advanced-fitted patches for specific skin conditions: (A) acne, and (B) age spots. (A,i,ii) 3D scan and flattening of the patch area using the AEVA-HE2 system (Eotech, v.3, Marcoussis, France). (A,iii) Contouring and slicing of the acne-affected area using Cura (UltiMaker B.V., v.4.8.0, Utrecht, The Netherlands) software. (A,iv,v) 3D-printed under-eye patches with different infills. (B,i) Contouring and slicing of an age spot-affected area. (B,ii) 3D-printed under-eye patches with and without spot filling. LD, line distance; M-25G (white nozzle), M-27G (pink nozzle), M-30G (black nozzle), metal(M)-gauge(G) nozzles.
Figure 4. Design and printing features of the under-eye patches: From 3D scanning to advanced-fitted patches for specific skin conditions: (A) acne, and (B) age spots. (A,i,ii) 3D scan and flattening of the patch area using the AEVA-HE2 system (Eotech, v.3, Marcoussis, France). (A,iii) Contouring and slicing of the acne-affected area using Cura (UltiMaker B.V., v.4.8.0, Utrecht, The Netherlands) software. (A,iv,v) 3D-printed under-eye patches with different infills. (B,i) Contouring and slicing of an age spot-affected area. (B,ii) 3D-printed under-eye patches with and without spot filling. LD, line distance; M-25G (white nozzle), M-27G (pink nozzle), M-30G (black nozzle), metal(M)-gauge(G) nozzles.
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Figure 5. Personalization of under-eye patches based on consumer preferences. (A) Pie chart representing the responses obtained for question Q3—critical features of skincare patches. (B) Color and fragrance preferences reported per volunteer. (C) Examples of personalized 3D-printed eye patches vs. commercial alternative (Reference). Vol, volunteer.
Figure 5. Personalization of under-eye patches based on consumer preferences. (A) Pie chart representing the responses obtained for question Q3—critical features of skincare patches. (B) Color and fragrance preferences reported per volunteer. (C) Examples of personalized 3D-printed eye patches vs. commercial alternative (Reference). Vol, volunteer.
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Figure 6. Assessment of emotional responses (Reference vs. 3D-Printed): From valence to arousal affective analysis. (A) Facial action classification system (FACS): valence data obtained during application and post-application (think). (B) Arousal rate per volunteer: Galvanic skin response (GRS) data monitored during application and post-application (think).
Figure 6. Assessment of emotional responses (Reference vs. 3D-Printed): From valence to arousal affective analysis. (A) Facial action classification system (FACS): valence data obtained during application and post-application (think). (B) Arousal rate per volunteer: Galvanic skin response (GRS) data monitored during application and post-application (think).
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MDPI and ACS Style

Bom, S.; Pinto, P.C.; Ribeiro, H.M.; Marto, J. Digital Tools in Action: 3D Printing for Personalized Skincare in the Era of Beauty Tech. Cosmetics 2025, 12, 136. https://doi.org/10.3390/cosmetics12040136

AMA Style

Bom S, Pinto PC, Ribeiro HM, Marto J. Digital Tools in Action: 3D Printing for Personalized Skincare in the Era of Beauty Tech. Cosmetics. 2025; 12(4):136. https://doi.org/10.3390/cosmetics12040136

Chicago/Turabian Style

Bom, Sara, Pedro Contreiras Pinto, Helena Margarida Ribeiro, and Joana Marto. 2025. "Digital Tools in Action: 3D Printing for Personalized Skincare in the Era of Beauty Tech" Cosmetics 12, no. 4: 136. https://doi.org/10.3390/cosmetics12040136

APA Style

Bom, S., Pinto, P. C., Ribeiro, H. M., & Marto, J. (2025). Digital Tools in Action: 3D Printing for Personalized Skincare in the Era of Beauty Tech. Cosmetics, 12(4), 136. https://doi.org/10.3390/cosmetics12040136

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