CIELAB Color Space Research Articles

Machine-Learning-Based Spectral Modeling: A Biomimetic Guide for Enhancing Esthetics.

To provide guidelines and means for optimal coverage and distribution of computer models with 1-10 clusters, designed based on an invivo extensive dental colorimetric database and compare the findings with some reputable shade guides. The Fuzzy C-Means (FCM) algorithm was used to optimize the tab position in the CIELAB color space, while MATLAB Fuzzy Logic was used to implement the codes. A minimization process was performed to fine-tune the cluster centers, minimizing Coverage Error (CE00) and Maximum Error (ME00). Spectral reconstruction was performed, and the results were compared with the corresponding data for VITA classical A1-D4 (VC), Linearguide 3D-Master (LG), and Bleachedguide 3D-Master (BG) shade guide. Paired t-test was employed to assess the significance of CE00 differences. CE00 and ME00 ranges for 1-to-10-tab models were 3.8 (14.2) to 1.7 (4.3), while the corresponding values for shade guides were 3.1 (9.1) for VC, 2.3 (6.0) for LG, and 2.9 (9.3) for BG. Hence, the 4-tab-model with CE00 of 2.2 and ME00 of 7.1, outperformed CE00 of LG (the lowest/best CE00 among shade guides), while the 6-tab-model, with CE00 of 2.0 and ME00 of 5.6, outperformed its ME00. A paired t-test confirmed that increasing the number of tabs models resulted in significant CE improvement in all cases (p ≤ 0.05). Shade guide models with only 4 to 6 tabs, optimized using computational techniques, outperformed physical dental shade guides tested, exhibiting significantly lower (better) Coverage Error and Maximum Error. Given that the spectral modeling was performed on an extensive invivo database of natural teeth, this approach was validated as a biomimetic guide for shade guides and corresponding materials of the future, offering a possibility for better shade matching with fewer shades/shade tabs.

Prediction of the thermal degradation–induced colour change of acrylonitrile butadiene styrene products as a function of temperature and titanium dioxide content

In this study, we examined the thermal degradation–induced colour change of acrylonitrile butadiene styrene (ABS)–based titanium dioxide (TiO2)–doped products as a function of TiO2 content and temperature. Based on the time–temperature superposition (tTS) principle and the CIELAB colour space, we proposed a methodology for developing a robust model to estimate the long-term colour change of polymers doped with TiO2 at elevated temperatures. We used 800 h of measurement data to develop the model and validated the colour change predictions of the model in 1600 h. The average colour difference between the measured and modelled results at the application temperature range (below 80 °C) was <1.5, smaller than the just noticeable difference (JND) in the CIELAB colour space. We found that above a certain TiO2 content (approximately 3 wt%), the colour retention of the ABS/TiO2 specimens can no longer be improved by increasing their TiO2 content. To show the applicability of the model, we present two simple case studies predicting the colour and appearance of a polymer product under constant and variable heat loads. The proposed methodology provides an excellent tool for design, as it can be used to determine the optimum amount of TiO2. Moreover, it accelerates and simplifies the design process.

Effect of cigarette smoking on the optical properties of contemporary dental ceramics: an in-vitro analysis.

Cigarette smoking is the most common form of tobacco use worldwide. With the frequent introduction of new dental materials, the effect of smoking on their optical properties such as long term color stability, should to be thoroughly investigated. This in-vitro study aims to investigate the effect of smoking on the optical properties of contemporary dental ceramics used currently for restoration of teeth. Five different materials in two shades (B1 and C1) were used with 15 samples from each pressable lithium disilicate (Emax), layered lithium disilicate (Lmax), porcelain fused to metal (PFM), monolithic zirconia (MZr) and layered zirconia (LZr) were used (n=75). The samples were exposed to conventional cigarette smoke and color stability was assessed at four different time intervals i.e., baseline, 1 week, 1 month and 6 months. CIELAB color space (CIE L*a*b*) values were used to evaluate the color difference (ΔE). A one-way analysis of variance (Anova) was used for statistical analysis of ΔE. Significant P-value was kept as <0.05, followed by Tukey post-hoc test. All test materials demonstrated significant color differences (ΔE) after exposure to cigarette smoke (p<0.05). For shade B1, the highest change in shade ΔE 17.02 was exhibited by Lmax, whereas the least change in shade was exhibited by Emax followed by PFM at values of ΔE 10.11 and 11.2 respectively. For shade C1, the highest change (11.47) in shade at 6 months was demonstrated by MZr, whereas lowest values of ΔE were exhibited by Emax (7.52). Traditional smoking causes significant change in shade of dental ceramics which can affect the esthetics of the patients. All material samples tested showed the values of ΔE > 3.3 which is higher than the acceptable range. Lowest color change was observed in Emax and PFM.

Environmentally sustainable color fading approaches of denim fabric using alternative garments dry process: An insight into chromatic parameters and physical properties

Denim has been elevated to the status of "classic" in the modern fashion cycle by numerous advancements and development efforts over the past few decades. Consumers today are interested in clothing comfort, environmental sustainability, and aesthetic appeals in addition to newborn designs and novel looks. Previous research works had only focused on evaluating the physical properties of denim samples after applying existing hazardous dry processes (whiskering, sandblasting, and PP {Potassium permanganate} spray), but those don’t portray scientific assessment regarding environmental impact assessment, chromatic values and vintage effects of denim. This research work explores sustainable alternative dry garment processes using sodium hydroxide and heat treatment with a view to achieving highly fashionable worn-out effects on sulfur-dyed denim, focusing on color fading. Process variables such as different sodium hydroxide concentrations (5 g/L, 10 g/L, 15 g/L, and 20 g/L) and heat treatment by stenter machine operating temperatures (140°C, 160°C, 180°C, and 200 °C) are used to conducting the research. Addressing a research gap, it evaluates Environmental Impact Measurement scores using EIM Software and chromatic parameters using Datacolor Spectrophotometer. Other examining factors such as color fastness (CF), and physical characteristics such as tensile and tear strength, shrinkage%, air permeability, and contact angle are evaluated. The alternative methods of dry process using are environmentally sustainable with scores of 7 and 1 (EIM software’s scoring system implies 0–33 for low impact, 34–66 for medium impact, and >66 for high impact) respectively. Evaluations indicate decreasing patterns in color strength and chromaticity with higher sodium hydroxide concentration like 20 g/L and heat treatment, leading to a light black or grayish hue at 200°C. Conversely, lower concentrations (5 g/L, 10 g/L) and temperatures (140°C and 160°C) show increased a* (red-green), b* (yellow-blue) values, and r% (reflectance %). Fabrics treated with lower sodium hydroxide concentrations and temperatures exhibit weaker resistance to color change and staining. The study provides a comprehensive assessment using metrics like EIM score, color strength, chromaticity, hue angle, CIELAB color space, reflectance%, and color fastness tests, including rubbing, washing, and light fastness.

Characterization of Italian Grape Ale beers obtained with different additions of Malvasia di Candia Aromatica must and marcs

In 2015, a new style of fruit beer, called Italian Grape Ale (IGA), was introduced by the Beer Judge Certification Program. The style of this beer is defined as a union between beer (wort or final beer) and grape or grape must (from red or white cultivar). This study aimed to characterize the chemical composition and sensorial properties of IGA beers obtained by adding grape juice or marc. To this aim, different amounts of grape juice or marcs (10 % and 20 %) from cv. Malvasia di Candia Aromatica (MCA) were blended with Golden Ale wort before primary fermentation. The addition of MCA grape marc and juice resulted in a significant increase in total fermentable sugar concentration, organic acids, and higher titratable acidity, both in the wort and in the final beer. Furthermore, the CIELab colour space parameters were strongly influenced by MCA, with the additions of marc leading to the most pronounced colour changes in beer, producing a dark yellow hue. Regarding the volatile profile of IGA beers, the MCA additions modulated the profile of IGA beers with key markers that consisted mainly of terpenes (both free and glycosylated forms), higher alcohols and esters. These compounds, as confirmed by sensory analysis, enhanced the aroma of beer samples with floral and fruit notes, making them easily distinguishable from IGA traditional beers. These findings are among the first data available on the effect of white aromatic grape juice and marc in brewing, both from a chemical and sensory point of view.

Strategies for constructing harmonious colour combinations by art students with autism spectrum disorders

Introduction. The survey discussed in the article represents the first experience of an empirical study of the peculiarities characterising the ways of construction and perception of colour harmony by students with autism spectrum disorders – professional art education learners. The relevance of the study is conditioned by new trends in education related to the need to create an inclusive and favourable environment for people with autism. This environment is necessary for their successful integration and professional training in art specialities that are considered a promising area for professional realisation of people with autism. Methods. The survey was carried out using the experimental method involving 21 participants with autism spectrum disorders (mean age = 20.4 years; SD = 2.4; 9 males, 12 females). The participants’ answers in the experimental group were compared with those of an age-, gender-, and education-matched control group (N=21). The participants’ task was to match two given shades with a third one in the palette, for the colour combination to be as harmonious as possible. The experimental palette allowed to change the hue, lightness and chroma of shades. Results. The participants with autism spectrum disorders were twice as likely to choose yellow (23.8% compared to 12.4% in the control group) and purple shades (16.2% compared to 9.5%); hardly used orange (1.9%) and blue (4.8%) and preferred lighter shades (the mean L value was 67.8 compared to the control group’s 63.0). The chroma of the selected shades correlated with their hue and lightness. The stimulus selection strategy depended on the distance (∆E*00) between the given (upper and lower) shades in the CIELAB colour space and was based on a specific “geometry” (a particular ratio of values characterising the angles and side lengths formed by the given and selected shades, as well as a preference for a particular triangle area and its type). Discussion. The experiment showed that the students with autism spectrum disorders differed markedly from neurotypical students in a number of indicators when constructing a colour harmony. The experimental group participants’ responses showed a “preference for sameness” characteristic of autism. The significant inter-group differences were applied more to the lightness and chroma of the chosen shades than to their hue. The shades preferred by the participants with autism spectrum disorders better matched the conditions of the habitual “chromatic diet” and the colour’s statistics in nature. The observed specificity may also reflect the reduced ability to generalise and average out colours in terms of holistic perception, specific to autism. Conclusion. The survey results make an important contribution to the improvement of the professional training methodology for art students, and for the development of scientifically based educational approaches to teaching people with autism spectrum disorders, primarily in the context of the formation of colour-related competencies; in addition, they may provide new impulses for investigating the fundamental mechanisms of cognitive processing of colour-related information.

Soil burial degradation of chemically compatibilized poly(butylene adipate-co-terephthalate)/thermoplastic starch/poly(ε-caprolactone)/cellulose biocomposites

Developing bio-blends and biocomposites has become a widespread strategy to combat plastic pollution in line with sustainability principles and decarbonization necessities. Although chemically modified ternary and quaternary biocomposites are developing rapidly because of their broader processing and performance windows than single matrix and binary counterparts, a few have been reported about their biodegradation. Herein, diisocyanates-based chemically modified ternary biocomposites based on poly(butylene adipate-co-terephthalate), thermoplastic starch (TPS), poly(ε-caprolactone) (PCL), and cellulose (Mater-Bi/PCL/cellulose) are prepared and undergone soil burial biodegradation providing a broader perspective on biodegradation of complicated systems. The mass gain of sunflower sprouts, weight retention, and the appearance of biocomposites are studied and discussed in the course of biodegradation. The unfilled Mater-Bi/PCL bio-blends presented moderate mass loss over 12 weeks, attributed to the presence of TPS in the Mater-Bi phase. The PCL addition hindered TPS decomposition and featured a noticeably lower degradation rate compared to previous reports. A significant increase in the b* parameter (position on the blue-yellow axis in the CIELAB color space), along with the yellowness and whiteness indices, was observed. Prior to soil burial, roughness differences were negligible. Still, they significantly increased over time due to the higher hydrophilicity of unfilled Mater-Bi/PCL and biocomposite containing unmodified filler.

Application of nano-TiO2 and micro-ZnO on cementitious surfaces for self-cleaning façades by spray coating and dip coating: A comparative study

Building façades are constantly exposed to atmospheric pollution and various external agents that can degrade their aesthetic qualities and introduce degradation patterns that affect the durability and performance of the materials. Façades with self-cleaning properties are important in the modern construction industry and the conservation of historic buildings, as they reduce costs and allow the preservation of original surfaces without the need for invasive interventions that could compromise cultural heritage. This study does a comparative analysis of the application of photocatalytic coatings composed of aqueous dispersions of titanium dioxide nanoparticles (TiO₂) and zinc oxide microparticles (ZnO) on cementitious substrates for use on façades by two functionalisation methods: spray coating and dip coating. A comprehensive characterisation was carried out using techniques such as scanning electron microscopy (SEM), X-ray diffraction (XRD), and diffuse reflectance spectroscopy (DRS) to assess the morphology, crystal structure and light absorption properties of the photocatalyst particles while Energy Dispersive Spectroscopy (EDS) and scanning electron microscopy (SEM) were employed to evaluate the substrate. The self-cleaning performance was evaluated by monitoring the degradation of Rhodamine B (RhB) dye under simulated sunlight. Spectrophotometric analysis was used to assess the colour coordinates using a standard colour system (CIELAB colour space). The results showed that the photocatalytic coatings improved the surfaces' self-cleaning properties while maintaining the substrate's original aesthetics. Spray-applied micro-ZnO-based coatings showed the most significant effectiveness in terms of self-cleaning.

Effect of Enamel Surface Coating on Staining Capability Due to Iron-Containing Supplements on Primary Teeth: An in Vitro Study

ABSTRACT Background: The staining of primary teeth due to iron-containing supplements is a common concern among parents and healthcare providers. Materials and Methods: Forty extracted primary teeth were divided into two groups of 20 each. Group A served as the control and received no enamel coating, while Group B was treated with a commercially available enamel surface coating. Both groups were then exposed to iron-containing supplements for 7 days. The teeth were assessed for color changes using a spectrophotometer before and after exposure. The degree of staining was quantified using the CIELAB color space values, specifically the ΔE* value, which indicates the change in color. Results: The mean ΔE* value in Group A (control) was 12.5 ± 1.8, indicating significant staining. In contrast, Group B (coated) showed a mean ΔE* value of 4.2 ± 1.1, reflecting minimal staining. The difference between the two groups was statistically significant (P &lt; 0.001), demonstrating the effectiveness of the enamel surface coating in reducing staining caused by iron supplements. Conclusion: Enamel surface coatings significantly reduce the staining effect of iron-containing supplements on primary teeth.

Molecular tool for efficient breeding of DOMINANT Greenshell laying hens and significant refinement of phenotypic selection focused on eggshell color

In recent decades, interest in non-traditionally colored eggs has increased. For breeders, this market interest means breeding lines of laying hens that lay eggs of varied colors, such as the blue-green eggshells (Dominant Greenshell) in this study. This study presents the results of genotyping the polymorphism of the O locus responsible for shell coloration and photometric measurement of eggshell color based on the CIELAb system, which was carried out on the unique Czech breeding population Dominant Greenshell. The aim was to use a combination of phenotyping using the CIELab System method and genotyping of the O locus using the end-point PCR approach with the main focus on the accuracy of distinguishing shell color genotypes, streamlining the selection of dominant homozygotes in the O locus, optimizing this technology for the most efficient and cost-effective selection procedure in practical hen breeding. The optometric method was able to reliably distinguish only dominant and recessive phenotypes and eliminate from the population only undesirable recessive homozygotes with a white colored shell. The parameter a* (redness/greenness) from the CIELab color space turned out to be absolutely key for distinguishing dominant and recessive phenotypes. Using the CART methodology, a classification tree built on discriminating optometric characteristics a-blunt was obtained, however, for the group of desirable O/O homozygotes, the selection approach would result in incorrect genotyping of 31% of individuals. Therefore, a combined approach based on rapid and simple elimination of recessive homozygotes using phenotyping (CIELab photometric measurement) and molecular identification of the EAV-HP insertion in the SLCO1B3 gene in dominant phenotypes, regardless of color intensity affected by laying time/order, and allowing reliable elimination, has proven to be the most effective method to distinguish heterozygotes from the breeding population. The combination of optometric and molecular selection methods then leads to more efficient selection, reduction of overall selection costs. This process led to the stabilization of the breeding population within one generation and the achievement of a pure homozygous line with regard to eggshell color.

CLASSIFICATION OF X-RAY AND CT IMAGES IN DIFFERENT COLOR SPACES USING ROBUST CNN

Since deep learning models have been successfully used in many fields, they have been used to identify sick and healthy people in X-ray or Computed Tomography (CT) chest radiology images. In this study, Covid-19 and pneumonia classification is performed on both X-ray and CT images using the robust Convolutional Neural Network (CNN). BGR, HSV, and CIE LAB color space transformations are applied to X-ray and CT images to show that the model performs a successful classification independent of dataset characteristics. The binary classification accuracy rates of Covid-19 and pneumonia for X-ray images and CT images are 98.7% and 98.4%, 97.6% and 99.4%, respectively. Precision, Recall, Specificity, F1 score, and Mean Squared Error metrics are calculated for each X-ray and CT dataset. In addition, 5-fold cross-validation proved accuracy of the model. Although X-ray and CT chest radiology images are transformed into different color spaces, the proposed model performed a successful classification. Thus, even if the image characteristics of the radiology device brands change, the computer-based system will be able to make successful disease diagnoses at low cost where expert personnel are insufficient.

Leveraging multi-output modelling for CIELAB using colour difference formula towards sustainable textile dyeing

Textile dyeing requires optimizing combinations of ingredients and process parameters to achieve target colour properties. Modelling the complex relationships between these factors and the resulting colour is challenging. In this case, a physics-informed approach for multi-output regression to model CIELAB colour values from dyeing ingredient and process inputs is proposed. Leveraging attention mechanisms and multi-task learning, the model outperforms baseline methods at predicting multiple colour outputs jointly. Specifically, the Transformer model’s attention mechanism captures the complex interactions between dyeing ingredients and process parameters, while the multi-task learning framework exploits the intrinsic correlations among the L*, a*, and b* dimensions of the CIELAB colour space. In addition, the incorporation of physical knowledge through a physics-informed loss function integrates the CMC colour difference formula. This loss function, along with the attention mechanisms, enables the model to learn the nuanced relationships between the dyeing process variables and the final colour output, thereby improving the overall prediction accuracy. This reduces trial-and-error costs and resource waste, contributing to environmental sustainability by minimizing water and energy consumption and chemical emissions.

Combining UAV Multi-Source Remote Sensing Data with CPO-SVR to Estimate Seedling Emergence in Breeding Sunflowers

In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned aerial vehicles is proposed. Visible and multispectral images of sunflower seedlings were acquired using a UAV. The thresholding method was used to segment the excess green image of the visible image into vegetation and non-vegetation, to obtain the center point of the vegetation to generate a buffer, and to mask the visible image to achieve weed removal. The components of color models such as the hue–saturation value (HSV), green-relative color space (YCbCr), cyan-magenta-yellow-black (CMYK), and CIELAB color space (L*A*B) models were compared and analyzed. The A component of the L*A*B model was preferred for the optimization of K-means clustering to segment sunflower seedlings and mulch using the genetic algorithm, and the segmentation accuracy was improved by 4.6% compared with the K-means clustering algorithm. All told, 10 geometric features of sunflower seedlings were extracted using segmented images, and 10 vegetation indices and 48 texture features of sunflower seedlings were calculated based on multispectral images. The Pearson’s correlation coefficient method was used to filter the three types of features, and the geometric feature set, the vegetation index set, the texture feature set, and the preferred feature set were constructed. The construction of a sunflower plant number estimation model using the crested porcupine optimizer–support vector machine is proposed and compared with the sunflower plant number estimation models constructed based on decision tree regression, BP neural network, and support vector machine regression. The results show that the accuracy of the model based on the preferred feature set is higher than that of the other three feature sets, indicating that feature screening can improve the accuracy and stability of models; assessed using the CPO-SVR model, the accuracy of the preferred feature set was the highest, with an R² of 0.94, an RMSE of 5.16, and an MAE of 3.03. Compared to the SVR model, the value of the R2 is improved by 3.3%, the RMSE decreased by 18.3%, and the MAE decreased by 18.1%. The results of the study can be cost-effective, accurate, and reliable in terms of obtaining the seedling emergence rate of sunflower field breeding.

DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.

Diabetic foot ulcers (DFUs) are a severe complication among diabetic patients, often leading to amputation or even death. Early detection of infection and ischemia is essential for improving healing outcomes, but current diagnostic methods are invasive, time-consuming, and costly. There is a need for non-invasive, efficient, and affordable solutions in diabetic foot care. We developed DFUCare, a platform that leverages computer vision and deep learning (DL) algorithms to localize, classify, and analyze DFUs non-invasively. The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. Additionally, deep-learning models were implemented to classify infection and ischemia in DFUs. The preliminary performance of the platform was tested on wound images acquired using a cell phone. DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. The system successfully measured wound size and performed tissue color and textural analysis for a comparative assessment of macroscopic wound features. In clinical testing, DFUCare localized wounds and predicted infected and ischemic with an error rate of less than 10%, underscoring the strong performance of the platform. DFUCare presents an innovative approach to wound care, offering a cost-effective, remote, and convenient healthcare solution. By enabling non-invasive and accurate analysis of wounds using mobile devices, this platform has the potential to revolutionize diabetic foot care and improve clinical outcomes through early detection of infection and ischemia.

Exploring the impact of logo colors on the perceived tastes of tea beverages—A study on hand-shaken beverages in Taiwan

Color visually communicates the product’s flavors to consumers and further influences their taste perception. This study explores the perceived taste of tea beverages caused by the logo’s principal colors, using hand-shaken tea beverages in Taiwan as an example. To identify the linkage between the logo color and tea tastes, this study divides the taste of tea beverages into four categories: sweetness, freshness, bitterness, and astringency. Then, the 69 tea beverage logos are allocated into the 14 color sections in the CIELAB color space according to their primary colors. The Correspondence Analysis method is employed to visualize the relationships between the logos and the perceived tastes. The tea tastes are then mapped into the color sections in the CIELAB color space. The analysis results reveal that the sweetness links to logos in the Warm Scheme colors (hue angle from 0 to 59 degrees). The fresh taste is bound with the logo with the Cool White Scheme colors (hue angle from 90 to 149 degrees and brightness &gt;80). Finally, the bitter and astringent tastes link to the logo colors in the Cold Black Scheme colors (hue angle from 60 to 89 degrees, 150 to 329 degrees, and brightness &lt;25). This study expands the color and taste association literature from general food to tea beverages. Our obtained empirical results can be applied to hand-shaken beverage companies to select principal colors for designing logos and packages that align with tea beverages’ perceived tastes to convey brand recognition accurately.

Estimating SPAD, Nitrogen Concentration, and Chlorophyll Content in Rice Leaves using Calibrated Smartphone Digital Image

Laboratory analysis is commonly used to determine nitrogen and chlorophyll content. However, smartphones can serve as rapid, mobile, and non-destructive tools for this purpose. An equation can be created to calculate nitrogen and chlorophyll content by analyzing color parameters from digital images of rice leaves. An examination was performed on 86 rice leaf samples from the maximum tillering and mature stages. Rice leaf photos were taken with a smartphone in natural outdoor lighting. Color calibration with Spydercheckr was needed to adjust for lighting conditions. Uncalibrated and calibrated image data were analyzed to determine RGB values converted into CIELAB color space. The L*, a*, and b* values had a significant correlation with SPAD parameters, nitrogen concentration, chlorophyll a, b, and total chlorophyll content. This connection was higher after image calibration. The study found that smartphone images could predict SPAD values with 87.9% to 92.3% precision, depending on color space. Using a smartphone digital picture of L* and a* values, N content could be estimated with 84.7% and 81.9% accuracy. Average accuracy for chlorophyll a, b, and total chlorophyll content was 65% to 76%. This study shows smartphone images can estimate rice leaf SPAD and nitrogen content.

Design, synthesis, and characterization of a novel pH-responsive azo dye incorporating a 1,3,4-thiadiazole ring for advanced textile applications

This study presents the synthesis and comprehensive characterization of a novel azo dye derived from 2-(5-amino-1,3,4-thiadiazol-2-yl) phenol (1a). The synthesis was initiated by reacting salicylic acid with thiosemicarbazide in the presence of phosphorus oxychloride (POCl3), yielding an 89 % conversion to compound 1a. The subsequent diazotization and coupling of 1a under alkaline conditions led to the formation of the azo dye (1b). Structural elucidation was confirmed through Fourier Transform Infrared Spectroscopy (FTIR) and Nuclear Magnetic Resonance (NMR) spectroscopy. The incorporation of the 1,3,4-thiadiazole ring is significant due to its known influence on enhancing dye stability, reactivity, and potential biological activities, while the salicylic acid moiety contributes to the dye's pH responsiveness. A notable pH-dependent color transition from yellow (pH < 6.0) to red (pH > 8.0) was observed, driven by protonation-induced resonance changes. Computational studies, including Frontier Molecular Orbital (FMO) analysis, revealed a Highest Occupied Molecular Orbital (HOMO)-Lowest Unoccupied Molecular Orbital (LUMO) gap of 1.94 eV, indicating efficient electronic transitions critical for its color properties. Molecular Electrostatic Potential (MEP) and Fukui function analyses provided further insights into the dye's reactivity and potential applications. The dyeing properties were rigorously assessed through K/S values, CIELAB color space, and fastness properties on cotton fabric, demonstrating varied color depths and hues influenced by different mordants, with copper and manganese significantly enhancing color intensity. Colorfastness tests revealed good to fair durability, with untreated fabrics showing optimal performance. This thorough characterization underscores the promising potential of this newly synthesized azo dye for applications in pH-responsive textiles and functional materials, highlighting its relevance for advanced textile and material science applications.

CIELAB Color Space as a Field for Tracking Color-Changing Chemical Reactions of Polymeric pH Indicators.

Two types of cross-linked polymeric dye films that exhibit reversible color changes in response to pH were prepared. Upon protonation, films with a nitrophenol dye moiety (NP) changed from yellow to colorless, whereas films with an azobenzene dye moiety (AB) changed from yellow to reddish-purple. The colored light transmitted through these films from a D65 white-light source was measured colorimetrically, and the transition of the chromaticity point in CIELAB upon protonation of the polymeric dyes was observed. At low dye concentrations ([NP] < 4.7 × 10-5 and [AB] < 2.7 × 10-5 mol L-1), linear loci of the chromaticity points were displayed for both polymeric dye films, indicating perceptual linearity. This linearity is caused by the avoidance of nonlinear perceptual phenomena such as the Abney effect and the Bezold-Brücke phenomenon. The perceptual linearity of space is a necessary condition for achieving stoichiometric linearity, which requires an additional linear relationship between the dye concentration and each component, a*, b*, and L*. A geometric equation that included the AB concentration and the protonation ratio of AB was used to successfully depict, on the basis of stoichiometric linearity, the protonation of the polymeric AB films three-dimensionally in the restricted area of CIELAB.

Assessment of a color measurement-based method for the characterization of polymer thermo-oxidation

Epoxy resins are used in various applications where environmental factors can interact and degrade the material. Thermo-oxidation is one of the degradation processes that can lead to both mechanical and chemical changes. This work aims to present a technique for characterizing thermo-oxidative degradation based on color analysis. The UV–vis spectroscopy reveals the direct link between the chemical modification and the color variation. The color difference ΔEab* between a virgin and an aged sample (in CIELAB color space) provides an excellent indicator of oxidation degree. Calibration correlations have been developed based on reference samples aged under known conditions of temperature and pressure, translating color differences into an oxidation equivalent duration, represented as an equivalent time t* or to directly estimate mechanical properties. The t* parameter is the time that the sample should be exposed to the reference conditions to undergo the same oxidation level (equivalent to the same color difference change and then, degradation). Nanoindentation measurements were performed to validate the color measurement method. Some limitations were identified, including the poor correlation under non-equivalent time-temperature-pressure conditions, poor relevance for assessing high oxidation levels, and the impact of light scattering in areas with strong color gradients. The spatial resolution of color measurement is ten times higher than nanoindentation. Furthermore, the color measurement is non-destructive, can be conducted in situ, and is suitable for monitoring the aging of industrial components.

Fabrication of multifunctional wool textile using the synthesis of silver-modified N-doped ZnO/TiO2 photocatalysts

Photocatalysts and noble metals have attracted considerable attention for their potential in addressing global environmental pollution through photochemical processes. At low temperatures, multifunctional self-cleanable wool fabric was developed through green photo-sonosynthesis of N–Ag/TiO2/ZnO. A narrower bandgap of the hybrid photocatalyst, the surface plasmonic resonance effect of silver nanostructures, and nitrogen doping resulted in synergistically enhanced self-cleaning activity. The self-cleaning activity was evaluated by monitoring the discoloration of methylene blue stains on the wool fabric exposed to natural sunlight, using CIELAB color space and ΔE measurements. The ΔE value of the N–Ag/TiO2/ZnO-sonicated wool was superior, showing a value of 45.9 compared to 15.7 for the control and 28.7 for the sample coated by the stirrer. Furthermore, the nanocomposite construction improved tensile strength, enhanced fabric hydrophilicity, and reduced the yellowness index. Additionally, the synthesis of TiO2 and silver particles on ZnO particles increased surface resistance to acid, reducing ZnO acid solubility. The reflectance of the non-treated wool ranged from 5 to 20 % within 200–380 nm, while the reflectance of the Ag/TiO2/ZnO-sonicated sample remained constant at 4 %, exhibiting protection against UV rays. AATCC test revealed 100 % bacteria reduction against E. coli and S. aureus and 99 % against C. albicans fungus for N–Ag/TiO2/ZnO-sonicated sample. Moreover, cell culture assays demonstrated a viability of over 70 %, indicating non-cytotoxicity.