Color Contrast Research Articles

Phase change polymer-based structural color photonic films for contactless and inkless writing irreversibly

Thermal-responsive photonic crystals (PCs) have attracted considerable interest due to their triggerable and adjustable optical capabilities, which are promising for various applications. However, developing a mechanism for rapid and irreversible structural color transformation in PCs that enables contactless and inkless writing has significant research value. Herein, phase change polymers are incorporated into functional optical platforms capable of rapid and irreversible structural color transformation in response to thermal stimuli. In crystalline state, phase change polymers used as inverse opal scaffolds in photonic films produce structural color with high saturation. The collapse of the highly ordered inverse opal structure, driven by thermal-mediated transformation during the crystalline/amorphous conversion of phase change polymers, is identified as the primary mechanism for achieving rapid and irreversible structural color transformation. These significant color variations between the inverse opal and collapsed structures provide high color contrast and resolution. The photonic films exhibit excellent hydrophobicity, solvent resistance, and storage stability. Additionally, incorporating the photothermal material carbon black (CB) enhances color saturation and imparts efficient photothermal conversion ability to films. By modulating the photothermal effect via near-infrared (NIR) laser light, phase change polymer-based structural color photonic films can achieve a permanent configuration at 42 °C, resulting in high-resolution images. These outstanding features offer a feasible method for next-generation innovative inkless writing and printing.

Tunable optoelectronic performances of aminated benzothiadiazole-based D-A-D conjugated electrochromic polymers

Introducing heteroatom groups into benzothiadiazole (BT) unit has proven to be a very effective way to improve the optoelectronic properties of conjugate polymer due to its comprehensive advantages of lowering the HOMO or LUMO energy level, and increasing the interaction force on the polymer backbone. However, at present, little attention was paid to introducing amino groups into the BT unit. Herein, in this work, three D-A-D type aminated monomers (Th–NH2-BT, Th–2NH2-BT, and EDOT-2NH2-BT) with aminated benzothiadiazole as the acceptor unit and thiophene or 3,4-ethylenedioxythiophene (EDOT) as the donor unit were successfully synthesized by Stille coupling reaction, and their corresponding aminated polymers (P(Th–NH2-BT), P(Th–2NH2-BT), and P(EDOT-2NH2-BT)) were facilely achieved by electrochemical polymerization method. The optoelectronic properties of the aminated monomers and their D-A polymers were carefully analyzed, and the electrochemical and electrochromic properties of the resultant aminated polymers were further studied. The finally results show that the absorption and fluorescence spectra of Th–2NH2-BT and EDOT-2NH2-BT are both blue-shifted compared to Th–NH2-BT, and the corresponding optical bandgap are gradually increased (2.41 eV–2.65 eV). EDOT-2NH2-BT has a relatively lower oxidation potential (0.73 V) and P(EDOT-2NH2-BT) also has a wide potential window and better redox stability, which was very beneficial for improving its resultant D-A aminated polymers’ optoelectronic properties. As a result, as-formed P(EDOT-2NH2-BT) shown obvious color change from green in the neutral state to gray in the oxidized state with favorable electrochromic performances, with a high optical contrast (ΔT = 17.17 % at 860 nm) and high coloration efficiency (CE = 223 C−1 cm2 at 860 nm), so it is one of the promising materials that can be used in electrochromic devices.

Bismuth Oxychloride Nanoflakes Based Wearable Colorimetric Sensors for Efficient Monitoring of UV Exposure

Ultraviolet radiation from sunlight is essential for human health in many ways such as facilitating vitamin D synthesis while excessive exposure can cause adverse effects such as premature aging, sunburn, and skin cancers. In the USA, between 2007 and 2011, nearly five million adults were treated for skin cancer annually with average treatment costs of $8.1 billion each year. Hence, monitoring daily exposure to sunlight is essential to avoid developing health risks. Colorimetric UV sensors change color as a function of UV exposure, provide onsite information about the level of exposure, and can act as a personal wearable device to assist in making informed decisions on sunlight exposure. These sensors utilize radiation-induced reactions on a dye to produce a color change. We developed a BiOCl based wearable UV sensor for monitoring UV exposure where a white-to-black color transformation is induced due to the creation of oxygen vacancies in BiOCl. This color transformation is not quantifiable in bulk BiOCl; hence BiOCl was purposely dispersed on filter paper to form petal-like nanostructures that help quantify the color change. Four different dye molecules, 6-carboxyfluorescein, 5-carboxyfluorescein, rhodamine-B, and Prussian blue were evaluated, where 6-carboxyfluorescein gave the best color contrast. The sensor responds to UVA, UVB, and UVC and with proper UV filters can be made selective for specific UV radiations. This unique colorimetric UV sensor can help manage personal UV exposure and reduce the risk of sun exposure ailments.Reference: Kyle Troche, Kannan Ramaiyan, Timothy J. Boyle, Fernando H. Garzon, “Bismuth Oxychloride Nanoflakes Enabled High Sensitivity Colorimetric UV Dosimetry” ACS Appl. Nano. Mater. 2023, 6, 7.

Bornean Orangutan Nest Classification using Image Enhancement with Convolutional Neural Network and Kernel Multi Support Vector Machine Classifier

Preserving wildlife habitats is crucial in mitigating climate change. Species like orangutans and monkeys contribute to fruiting and planting in forests. The World Wide Fund Sabah Malaysia faces challenges in manually identifying and classifying orangutan nests for studying their behaviour and conserving their habitats. To address this, we propose automating the classification of captured images using machine learning algorithms. This research involves three key components: image processing, feature extraction, and image classification. Our proposed image processing includes several steps, such as image pre-processing and enhancement techniques like local contrast enhancement, sharpening, intensity adjustment, histogram equalization, and colour thresholding. We applied four different Convolutional Neural Networks (CNNs) to extract and identify orangutan nests’ features. Subsequently, we utilize Support Vector Machine (SVM) for image classification. The results reveal that the Inception Residual Network Version 2 (ResNet-v2) achieves the best performance. This architecture is then combined with a kernel SVM to classify Bornean orangutan nests. Our approach demonstrates impressive results, boasting an accuracy of 96.60%, an F1-score of 96.60%, a precision of 96.59%, and a recall of 96.58%. These metrics underscore the high accuracy and effectiveness of our proposed methodology for classifying Bornean orangutan nests. By reducing the need for extensive human intervention in image analysis, our method presents a valuable tool for conservationists and researchers committed to studying and safeguarding these endangered orangutans and their habitats. In future work, we aim to develop orangutan nest detector, contributing to wildlife conservation research.

Development of silica gel from Lapindo volcanic mud as fluorescent fingerprint powder based on methyl orange

Fingerprint powder remains one of the most effective techniques for identifying individuals from their latent fingerprints. Visualizing latent fingerprints requires powder with high color contrast and strong adhesive to be easily applied on various substrates. The utilization of silica gel extracted from local materials of Lapindo volcanic mud can be applied for fluorescent fingerprint powder based on methyl orange. The powder was synthesized by the sol-gel method, followed by the impregnation of methyl orange as a dye with varying loads. The powders exhibit an amorphous structure and nanoparticle size with an average particle diameter of 80.93 nm by spherical morphology interconnected to form agglomerations. The powder contains silanol, siloxane, azo, and carboxylate functional groups derived from its precursors. The dusting method proves the performance of fluorescent fingerprint powder on porous and non-porous substrates hereafter observed under white light and UV light. The powder containing 0.05 gs of methyl orange per gram of silica gel is the most effective in revealing fingerprint patterns on non-porous surfaces. It has mesoporous properties with a specific surface area of 7.95 m2g−1 and a pore diameter of 23.14 nm. SiMO retained its full capability after two years of storage, indicating it is a great choice for forensic investigations.

Multiple Stimuli-Responsive Color-Changing Polymer Materials for Reversible Writing and Anti-Counterfeiting.

Polymer materials with multiple stimuli-responsive properties have demonstrated many potential and practical applications. By covalently introducing spiropyran (SP1) and spirothiopyran (STP) into the polyurethane backbone, photochromic, mechanochromic, and thermally discolored polymer materials have been prepared. In this work, we report for the first time that white light (violet, blue, and green light) above a certain intensity can activate STP to green color. Based on the above discovery, the polyurethane with SP1 and STP can exhibit reversible three-color changes (brown, green, and purple) in response to four stimuli: ultraviolet irradiation, white light irradiation, mechanical stress, and heat. The color-changing polymer materials have high color contrast and excellent reversibility, and can be used for reversible writing, anticounterfeiting and information encryption, etc.

Determining the efficacy of visual inspections at detecting non-biosecurity-compliant goods.

Examination of imported commodities by trained inspectors searching for pest organisms is a common practice that phytosanitary regulatory agencies use to mitigate biosecurity risks along trade pathways. To investigate the effects of target size and color on the efficacy of these visual assessments, we affixed square decals to polystyrene models of mandarins. Sample units of 100 model fruit containing up to 10 marked models were examined by inspectors. Six sizes in six shades of brown were tested across two prevalence levels. The experiment consisted of five inspection rounds where 11 inspectors examined 77 sample units within an allocated time. The probability that decals were detected increased with mark size and color contrast. Smaller, low-contrast marks were mainly missed. The prevalence rate did not affect the detectability. Through the experiment, the false-positive rate dropped from 6% to 3%, whereas false-negative rates were constant throughout. Large, dark targets were readily found with a mean recall of >90%, whereas small, pale marks had a mean recall of 9%. Increased experience made inspectors more competent at recognizing decals, reducing the false positive rate. However, constant false-negative rates indicate that experience did not prevent inspectors from overlooking targets they could not perceive.

A fuzzy control algorithm based on artificial intelligence for the fusion of traditional Chinese painting and AI painting

Recently, artificial intelligence (AI)-generated resources have gained popularity because of their high effectiveness and reliability in terms of output and capacity to be customized and broadened, especially in image generation. Traditional Chinese paintings (TCPs) are incomplete because their color contrast is insufficient, and object reality is minimal. However, combining AI painting (AIP) with TCP remains inadequate and uncertain because of image features such as patterns, styles, and color. Hence, an algorithm named variational fusion-based fuzzy accelerated painting (VF2AP) has been proposed to resolve this challenge. Initially, the collected TCP data source is applied for preprocessing to convert it into a grayscale image. Then, the feature extraction process is performed via fuzzy-based local binary pattern (FLBP) and brushstroke patterns to enhance the fusion of intelligent fuzzy logic to optimize the local patterns of textures in a noisy image. Second, the extracted features are used as inputs to the variational autoencoder (VAE), which is used to avoid latent space irregularities in the image and the reconstructed image by maintaining minimum reconstruction loss. Third, fuzzy inference rules are applied to avoid variation in the fusion process of the reconstructed and original images. Fourth, the feedback mechanism is designed with evaluation metrics such as area under the curve-receiver operating characteristic (AUC-ROC) analysis, mean square error (MSE), structural similarity index (SSIM), and Kullback‒Leibler (KL) divergence to enhance the viewer's understanding of fused painting images.

Automated Cobb Angle Measurements for Scoliosis Diagnosis and Assessment: AI Applications and Accuracy Enhancement Through Image Processing Techniques.

Introduction Scoliosis is characterized by an abnormal curvature of the spine in the coronal plane. Idiopathic scoliosis is the most prevalent type, though specific causes are sometimes identifiable. Genetic factors significantly influence adolescent idiopathic scoliosis (AIS), which is diagnosed through clinical and radiographic evaluations, primarily using the Cobb angle to measure curvature severity. The classification of scoliosis severity ranges from mild scoliosis, where sometimes the absence of pain is encountered, to moderate and severe, which is usually associated with lancinating pain. Early onset and high progression rates in idiopathic scoliosis are indicative of poorer prognoses. Methods The study analyzed 197 radiographic images from a private clinic database between December 2023 and April 2024. Inclusion criteria focused on anteroposterior images of the thorax and abdomen, excluding unclear and non-spinal images. Manual Cobb angle measurements were performed using RadiAnt DICOM Viewer 2020.2, followed by automated measurements using the Cobb Angle Calculator software. Discrepancies led to further image processing with enhanced color contrast for improved visualization. Data were analyzed using GraphPad InStat to assess error margins between manual and automated measurements. Results Initial results indicated discrepancies between manual and automated Cobb angle measurements. Enhanced image processing improved accuracy, demonstrating the efficacy of both manual and automated techniques in evaluating spinal deformities. Statistical analysis revealed significant error margins, prompting a refined approach for minimizing measurement errors. Discussion The study highlights the importance of accurate Cobb angle measurement in diagnosing and classifying scoliosis. Manual measurements, while reliable, are time-consuming and prone to human error. Automated methods, particularly those enhanced by machine learning algorithms, offer promising accuracy and efficiency. The integration of image processing techniques further enhances the reliability of scoliosis evaluation. Conclusion Accurate assessment of scoliosis through Cobb angle measurement is crucial for effective diagnosis and treatment planning. The study demonstrates that combining manual techniques with advanced automated methods and image processing significantly improves measurement accuracy. Such an approach is intended to support better clinical outcomes. Future research should focus on refining these technologies for broader clinical applications.

Assessing Relations Among Visual Variables in Hotel Lobbies Using Deep Learning

To understand the relationships between cognitive variables like visual complexity, coherence, and colour contrast in interior spaces, direct numerical analysis is crucial. Conventional approaches are limited due to the brain's struggle to process visual information without cognitive manipulation. However, advancements in artificial intelligence enable direct examinations. This study used a convolutional neural network to assess the intensity of colour contrasts in images of 5-star hotel lobby interiors with high levels of coherence and complexity. The results indicated that visually complex lobby interiors have less warm-cool and dark-light contrast but more pronounced complementary contrast than coherent lobby interiors. Additionally, a negative correlation was identified between complementary and warm-cool contrasts across all perspectives. These results underscore the potential influence of specific colour contrast types on the cognitive experience of interiority.

Microscopic study on characteristic decorative black and white porcelain produced in Shanxi province, Jin and Yuan dynasties (ad 1115–1368), China

AbstractBlack and white porcelain plays a critical role in Chinese decorative porcelain history. It is famous for its decorative styles and techniques, which create a strong black and white color contrast in its appearance. In this study, representative black and white porcelains produced in Shanxi province were analyzed by X‐ray fluorescence, Raman spectroscopy, and SEM‐EDS. The results show that both Ca‐rich (~5.33 wt%) and Ca‐poor (~1.99 wt%) glazes were used leading to quite different microstructures. The pigment particles of Ca‐rich glazes are characterized by small size (≤2 μm), wide distribution, and tightly wrapped by anorthite. In contrast, they are larger (≥2 μm) and tightly cumulated together in Ca‐poor glazes. Hematite is the major crystal in pigment, double‐substituted by Al and Ti. The Al/Fe ratios are similar, but Ti/Fe ratios are quite different: They do not exceed 0.03% in Ca‐poor glazes, whereas they reach 0.12% in Ca‐rich glazes, suggesting that the origin of the pigments must be different. Al‐rich mineral such as kaolin was added to the pigment preparation in Ca‐rich glazes. The color of the pattern is mainly influenced by the size, quantity, and concentration of brown Ti‐doping hematite, as well as the thickness of the glaze layer and other crystals and Fe ions. The fired atmosphere of Ca‐rich glaze type seems less oxidizing than for Ca‐poor glaze type. Overall, the results confirm that black and white porcelain has a variety of production techniques, which is attributed to the craftsman adjusting the techniques according to the composition of raw materials.

Advanced Functional Photochromic Wearables with Superior Durability and Stability for Sustainable Applications

AbstractThe rewritable wearables based on photochromism have emerged as attractive candidates in inkless printing applications. Among various photochromic materials, polyoxometalates have great potential for rewritable wearables, with major advantages in fast response upon UV irradiation, long‐term photochemical stability, and excellent fatigue resistance. However, the development of rewritable wearables based on polyoxometalates is limited by low combining fastness, weak stability, and poor scalability. Here, a scalable strategy is reported to fabricate an ideal rewritable wearable based on fundamental charge balance mechanism. The pristine cotton substrate is grafted by cationic polymer brushes to incorporate photochromic phosphomolybdic acid (PMoA) anions through electrostatic attraction, and then the cationic surfactants with alkyl chains are introduced to encapsulate the PMoA anions to achieve charge balance subsequently. The resultant rewritable wearables display the long‐awaited properties, such as high color contrast, favorable reversibility (>10 cycles), long color retention (>15 days) and high stability against detergent and sweat (pH 6.5–8.0) during repeated washing (50 cycles) and wearing. As a demonstration, a rewritable T‐shirt is scalably fabricated, and excellent antibacterial activity and biocompatibility are demonstrated as well, which is expected to be a sustainable solution for regular fabric printing in household products and public display.

Reservoir heterogeneities due to diagenesis in Jurassic Samana Suk Formation Kahi section Nizampur Basin North West Himalayas Pakistan

The porosity and permeability determine the reservoir quality of sedimentary rocks, which is fundamentally influenced by both depositional and diagenetic processes. The Jurassic carbonates are targeted in many regions for oil and gas exploration. The current study is carried out to elaborate on the diagenetic alterations and their effect on reservoir properties. A thick outcrop of the Jurassic Samana Suk Formation is studied in the Kahi section of Nizampur Basin Northwest Himalayas, Pakistan, to study and relate the trend of lithological variations and depositional settings. Field investigation revealed that the Samana Suk Formation is extensively distributed in the area and primarily made up of interbedded limestone and dolomite units. The original unaltered limestone has a thick-bedded and oolitic to bioclastic nature. Different types of dolomites have been recognized based on colour contrast and sedimentary features. Moreover, saddle dolomite cement, calcite cementation, and mechanical and chemical compaction have also been observed. The petrographic studies show different types of diagenetic alterations that affected the Samana Suk Formation, including micritization, bioturbation, mechanical and chemical compaction in the form of fractures and stylolites, various calcite cementation, which includes the various cement types that range from isopachous, blocky, granular equant, fibrous, and dog tooth cementation, along with dissolution that occurred in different diagenetic realms. Pyritization was rarely observed. Moreover, different phases of dolomites were identified, ranging in size and shape, i.e., finely crystalline to coarse crystalline and planar euhedral to non-planar anhedral. The stable oxygen isotope values of these dolomites show depletion from original marine signatures and suggest burial-related fault-controlled dolomitization events. Overall, diagenetic processes like dissolution, fracturing and dolomitization increased the reservoir potential. On the contrary, the overburden and cement precipitation result in a decrease in the reservoir properties.

A novel deep CNN model with entropy coded sine cosine for corn disease classification

Corn diseases significantly impact crop yields, posing a major challenge to agricultural productivity. Early and accurate detection of these diseases is crucial for effective management and mitigation. Existing methods, mostly relying on analyzing corn leaves, often lack the precision to identify and classify a wide range of diseases under varying conditions. This study introduces a novel approach to detecting corn diseases using image processing and deep learning techniques, aiming to enhance detection accuracy through pre-processing, improved feature extraction and selection, and classification algorithms. A new deep Convolutional Neural Network (CNN) model named TreeNet, with 35 layers and 38 connections, is proposed. TreeNet is pre-trained using the Plant Village imaging dataset. For image pre-processing, the YCbCr color space is utilized to improve color representation and contrast. Feature extraction is performed using TreeNet and two pre-trained models, Darknet-53, and DenseNet-201, with features fused using a serial-based fusion method. The Entropy-coded Sine Cosine Algorithm is applied for feature selection, optimizing the feature set for classification. The selected features are used to train Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers, with extensive experiments conducted using both 5-fold and 10-fold cross-validation, and feature sizes ranging from 200 to 1150. The proposed method achieves classification accuracy, precision, recall, and F1-score of 99.8%, 99%, 100%, and 99%, respectively, surpassing existing benchmarks. The integration of TreeNet with Darknet-53 and DenseNet-201, along with robust pre-processing and feature selection, significantly improves corn disease detection, highlighting the potential of advanced CNN architectures in agriculture.

A Lightweight Neural Network for the Real-Time Dehazing of Tidal Flat UAV Images Using a Contrastive Learning Strategy

In the maritime environment, particularly within tidal flats, the frequent occurrence of sea fog significantly impairs the quality of images captured by unmanned aerial vehicles (UAVs). This degradation manifests as a loss of detail, diminished contrast, and altered color profiles, which directly impact the accuracy and effectiveness of the monitoring data and result in delays in the execution and response speed of monitoring tasks. Traditional physics-based dehazing algorithms have limitations in terms of detail recovery and color restoration, while neural network algorithms are limited in their real-time application on devices with constrained resources due to their model size. To address the above challenges, in the following study, an advanced dehazing algorithm specifically designed for images captured by UAVs over tidal flats is introduced. The algorithm integrates dense convolutional blocks to enhance feature propagation while significantly reducing the number of network parameters, thereby improving the timeliness of the dehazing process. Additionally, an attention mechanism is introduced to assign variable weights to individual channels and pixels, enhancing the network’s ability to perform detail processing. Furthermore, inspired by contrastive learning, the algorithm employs a hybrid loss function that combines mean squared error loss with contrastive regularization. This function plays a crucial role in enhancing the contrast and color saturation of the dehazed images. Our experimental results indicate that, compared to existing methods, the proposed algorithm has a model parameter size of only 0.005 M and a latency of 0.523 ms. When applied to the real tidal flat image dataset, the algorithm achieved a peak signal-to-noise ratio (PSNR) improvement of 2.75 and a mean squared error (MSE) reduction of 9.72. During qualitative analysis, the algorithm generated high-quality dehazing results, characterized by a natural enhancement in color saturation and contrast. These findings confirm that the algorithm performs exceptionally well in real-time fog removal from UAV-captured tidal flat images, enabling the effective and timely monitoring of these environments.

Highly defended nudibranchs "escape" to visually distinct background habitats.

The "escape and radiate" hypothesis predicts that once species have evolved aposematism, defended species can utilize more visually diverse visual backgrounds as they "escape" the need to be well camouflaged. This enables species to explore new ecological niches, resulting in increased diversification rates. To test this hypothesis "escape" component, we examined whether the background habitats of 12 nudibranch mollusk species differed among species depending on the presence and strength of chemical defenses. We obtained a rich array of color pattern statistics using quantitative color pattern analysis to analyze backgrounds viewed through the eyes of a potential predator (triggerfish, Rhinecanthus aculeatus). Color pattern analysis was done at viewing distances simulating an escalating predation sequence. We identified 4 latent factors comprising 17 noncorrelated color pattern parameters, which captured the among-species variability associated with differences in chemical defenses. We found that chemically defended species, indeed, were found on visually distinct backgrounds with increased color and luminance contrast, independent of viewing distance. However, we found no evidence for increased among-species background diversity coinciding with the presence and strength of chemical defenses. Our results agree with the "escape and radiate" hypothesis, suggesting that potent chemical defenses in Dorid nudibranchs coincide with spatiochromatic differences of visual background habitats perceived by potential predators.

On the enhanced electrochromic performance of nano-structured V2O5 thin films developed by substrate temperature induced orientated Growth

Electrochromic society is keen to develop devices with multicolor displays with high stability, using eco-friendly inorganic materials. In the present research, Influence of substrate temperature on the electrochromic V2O5 active electrodes deposited using automated nebulizer spray pyrolysis was examined to demonstrate V2O5 nanostructured films with good cyclic stability, high color contrast, and a robust memory effect. The structural characterization of the V2O5 thin films was conducted through R-ray diffraction and thermogravimetric analysis to assess material stability. Nanostructure morphology under different conditions was analysed using SEM and TEM. The electrochromic studies revealed that the higher optical modulation (ΔT) was achieved (with 70.8 % at 550 nm) in a very short switching time. Feature studies on V2O5 under different potentials were conducted to understand the capacitive and diffusion effects. The electrochromic response V2O5 thin films to various potentials was analysed using ex-situ XRD, elucidates phase changes from V2O5 to LiVO5 and V4O9, and a 2000-cycle CV measurement comprehended the material stability. The significantly improved electrochromic performance is primarily attributed to the nanosheet structure of the V2O5 thin films, since the structures enhance the material stability during lithium-ion intercalation/deintercalation processes.

High-performance color and fluorescence switching of cotton fabrics via light-induced proton transfer strategy of spiropyran sulfonate molecule

Light-responsive color-switching materials have garnered persistent interest recently due to their passive response to external stimuli and manifestation of notable visual color variations. Nonetheless, their development into high-end products has been hindered by unsatisfactory overall performance, characterized by challenges such as the inability to combine color contrast and retention time, poor repeatability, and reliance on a single coloring mode. Herein, we present a negative photochromic cotton fabric (NPCF) capable of real-time dual output of optical signals (color change or fluorescence emission), featuring high color contrast and resolution, excellent reversibility, and facile multicolor printing. These attributes stem from the utilization of a unique photoinduced proton transfer (PPT) strategy exhibited by the designed and synthesized spiropyran sulfonate molecule (MC-NO2). Guided by this strategy, NPCF demonstrates significant color changes and fluorescence switching in a real-time and highly reversible manner, leveraging the remote non-contact and non-destructive nature of visible light. Moreover, direct blending of MC-NO2 with commercially available water-soluble non-stimuli-responsive dyes enables customization of various photosensitive color systems for straightforward multi-color printing, aligning well with the environmental principles of green printing. This study serves as a valuable reference for the advancement and design of diverse high-performance, switchable materials suitable for the production of high-end products.

The modelling of flower colour: spectral purity or colour contrast as biologically relevant descriptors of flower colour signals for bees depending upon the perceptual task.

Flower colour is an important mediator of plant-pollinator interactions. While the reflectance of light from the flower surface and background are governed by physical properties, the perceptual interpretation of such information is generated by complex multilayered visual processing. Should quantitative modelling of flower signals strive for repeatable consistency enabled by parameter simplification, or should modelling reflect the dynamic way in which bees are known to process signals? We discuss why colour is an interpretation of spectral information by the brain of an animal. Different species, or individuals within a species, may respond differently to colour signals depending on sensory apparatus and/or individual experience. Humans and bees have different spectral ranges, but colour theory is strongly rooted in human colour perception and many principles of colour vision appear to be common. We discuss bee colour perception based on physiological, neuroanatomical and behavioural evidence to provide a pathway for modelling flower colours. We examine whether flower petals and floral guides as viewed against spectrally different backgrounds should be considered as a simple colour contrast problem or require a more dynamic consideration of how bees make perceptual decisions. We discuss that plants such as deceptive orchids may present signals to exploit bee perception, whilst many plants do provide honest signalling where perceived saturation indicates the probability of collecting nutritional rewards towards the centre of a flower that then facilitates effective pollination.

The influence of the textile substrate color on the contrast of thermochromic prints

Thermochromic inks have attracted much attention due to their application in various fields, including the textile industry. The application of these colors in sports clothing enables the monitoring of the athlete’s physiological state through the possibility of changing the color of the material under the influence of skin temperature. Due to the printing of thermochromic inks on textile materials used for visual control, there is a need to analyze the contrast of the printed elements. Contrast plays a significant role in visually highlighting shapes and details and also facilitates the recognition of printed parts and information on textile materials. Contrast analysis of thermochromic inks on different colors of textile materials plays a role in determining the optimal combination of base colors and thermochromic inks that will result in the most noticeable color change. This analysis allows designers and manufacturers to make good decisions about choosing the colors of textile materials when printing with thermochromic inks because, in this way, a high level of aesthetics and functionality is achieved. This work aims to compare and analyze the contrast of thermochromic inks on different colors of the substrate of different types of materials, as well as to determine whether the color of the fabric on which the thermochromic ink is printed affects the color contrast when it is changed, i.e., its discoloration and return to its original state. Magenta reversible thermochromic water-based leuco dye was used to print the samples. The work used textile materials for printing with different structures and different colors. The colors were chosen to be bright and to provide a good contrast with the magenta color so that their combination attracts the viewer’s attention and can create a strong visual effect. Colorimetric and contrast analyses determined that the color of the substrate is dependent on the contrast value. In the case of polyester textile material, the white color of the substrate showed the highest contrast values. On the other hand, in the case of printed samples on a material made of a mixture of polyester and elastane, it can be concluded that the gray color of the substrate gives the highest contrast values. In conclusion, the research highlights the importance of choosing colors and materials to achieve optimal results in the design and production of textile materials with thermochromic inks.