Živko Pavlović

The growing demand for recyclable and high-performance paper-based packaging has intensified research on biopolymer coatings as alternatives to conventional petroleum-derived barrier layers. This review examines peer-reviewed studies published primarily between 2017 and 2026, focusing on chitosan, cellulose and nanocellulose, starch and other polysaccharides, proteins, lignin, bio-based and biodegradable polyester systems. Representative studies were selected to cover major material classes, modification strategies, coating methods, functional properties, processing aspects, and end-of-life performance. Particular attention is given to crosslinking, polymer blending, hydrophobic modification, nanocomposite reinforcement, and multilayer coating design. Relationships between coating composition, morphology, continuity, filler dispersion, coating weight, and processing conditions are discussed in relation to water resistance, water-vapor and oxygen barrier performance, grease resistance, mechanical properties, surface characteristics, and printability. Because experimental and testing conditions differ among studies, the results are compared primarily in terms of general trends and improvements relative to control samples rather than by direct numerical ranking. The reviewed literature shows that no single material class provides optimal performance across all functional requirements. Recent developments increasingly focus on low-coat-weight and scalable coating systems, waste-derived and fully bio-based raw materials, multifunctional coatings, end-of-life criteria such as biodegradability, laboratory-scale repulpability, fiber recovery, and potential compatibility with paper recycling. Future progress will depend on balancing functional performance, processability, resource efficiency, safety, and potential compatibility with existing paper-recycling streams, to be confirmed under industrial conditions.

The simultaneous presence of synthetic dyes and microplastics in industrial wastewaters represents an emerging environmental challenge that may significantly influence the performance of advanced oxidation processes. In this study, the efficiency of Fenton-like oxidation for the removal of Cyan printing dye in the presence of polyethylene terephthalate (PET) microplastics was investigated. Process optimization was performed using a three-level Definitive Screening Design (DSD) by evaluating the effects of five operating parameters, namely initial dye concentration, green-synthesized nano zero-valent iron (nZVI) dosage, PET concentration, solution pH and hydrogen peroxide concentration. The developed statistical model exhibited excellent predictive capability with a coefficient of determination (R²) of 0.977 and an adjusted coefficient of determination (R²adj) of 0.967. Analysis of variance confirmed the statistical significance of the model (p < 0.0001). Statistical analysis revealed that solution pH was the most influential factor affecting Cyan dye removal, followed by nZVI dosage, while H₂O₂ concentration, initial dye con- centration and PET microplastic concentration also showed statisticaly significant effects. In addition, a significant interaction between nZVI dosage and H₂O₂ concentration confirmed the importance of their balanced ratio, wheras increasing PET concentration slightly reduced decolorization efficiency. The maximum experimental decolorization efficiency reached 85%, while model optimization predicted a maximum efficiency of 96% under the following operating conditions: 108.9 mgL⁻¹ initial dye concentration, 48.7 mgFeL⁻¹ nZVI dose, 5.3 gL⁻¹ PET concentration, pH of 5.82 and 4.78 mM hydrogen peroxide. The obtained results demonstrate that efficient treatment of Cyan printing wastewater in the presence of PET microplastics requires simultaneous optimization of all process variables and provide valuable insight into the combined effects of adsorption and oxidation occurring during the Fenton process.

The growing demand for sustainable packaging and printed products has increased interest in biodegradable polymeric materials as environmentally friendly alternatives to conventional plastics. However, their printability and color reproduction characteristics remain insufficiently investigated. This study evaluates the suitability of biodegradable polymer films based on poly (lactic acid) (PLA) and its blends with poly (butylene succinate) (PBS), poly (hydroxybutyrate) (PHB), and poly (hydroxybutyrate-co-valerate) (PHBV) for UV inkjet printing applications. Biopolymer films were produced by flat-die extrusion under controlled processing conditions. The printed samples were obtained using a UV inkjet printing system under standardized printing parameters. Print quality was assessed through optical density and CIELAB color measurements using a spectrophotometer, while the chemical structure of the prepared blends was examined by FTIR spectroscopy to verify the absence of chemical reactions during processing. The obtained results indicate that all investigated biopolymer blends provide high optical density values with only minor variations among formulations. Although differences in lightness (L*) were observed between individual blends, chromatic coordinates (a* and b*) remained relatively stable, demonstrating consistent color reproduction across the tested materials. FTIR analysis confirmed that no new chemical bonds were formed during extrusion, indicating that the prepared materials represent physical polymer blends. The findings demonstrate that PLA-based biodegradable films and their blends exhibit promising printability for UV inkjet technology while maintaining their chemical integrity after processing. These results contribute to the development of sustainable printed products and provide a basis for further optimization of biodegradable substrates intended for graphic and packaging applications

Source-grounded generative AI assistants can improve access to disciplinary literature, but citations do not ensure technical correctness. We evaluated two textile- and offset-printing assistants in Google NotebookLM (now Gemini Notebook), using 20 controlled prompts per assistant. One subject-matter expert rated technical accuracy, completeness, source support, and contextualization and manually checked citations. Mean overall scores were 4.73/5 and 4.61/5, respectively; because prompt sets differed, these are descriptive, not comparative. Source support exceeded technical accuracy in 35 of 40 original responses. In eight explicitly prompted knowledge-boundary tests, six responses showed appropriate abstention and two showed abstention with reservations; this evaluates prompted handling, not spontaneous recognition. Of seven repeated pairs with preserved identical wording, six received unchanged overall scores (mean absolute difference, 0.036/5); content comparison found recurring errors and differences across the ten pairs, although three offset pairs were only same-topic comparisons. A recurring textile error labeled source-reported heat-setting temperatures as glass-transition temperatures; a reported fabric-temperature rise applies only to vat-dye all-in cotton printing in saturated steam. With one expert evaluator and 15 original offset prompts preserved only as topic labels, this is an exploratory case study, not a validated benchmark. Student learning outcomes were not assessed.

Innovations in artificial intelligence (AI) are shaping the future of almost every industry, and the printing industry is no exception. Applications of AI in the printing industry can be found in prepress optimization, production automation, variable data printing, quality control, predictive maintenance, customer engagement, workflow improvement, and waste reduction. Prepress is the first, crucial and critical stage in the printing process where files are prepared for the actual printing process. It has always been adaptable to changes due to technological requirements and improvements. The introduction of AI in the production phase of prepress has already demonstrated numerous benefits, such as seamless file management, comprehensive preflight checks, advanced PDF editing and automation of repetitive and time-consuming tasks such as file conversion, layout adjustments, trapping and color correction. AI-powered color management systems, which are essential in prepress, are becoming more sophisticated and can predict and compensate for color differences between different substrates and devices. Although the benefits of AI in prepress are obvious, there are still certain challenges and considerations, including the initial investment, data security, complexity of integration and lack of expertise. This paper aims to explore the integration of AI elements in prepress applications and provide a comprehensive overview of the benefits, challenges and future prospects in this area.

The paper presents a comparative pilot analysis of an AI-based visual attention prediction system with the traditional eye-tracking method in the context of UI/UX design. The study assesses the accuracy and reliability of AI-generated attention heatmaps compared to empirical eye-tracking data. The results indicate that AI systems show a strong ability to approximate human attention, achieving ROC-AUC values between 0.75 and 0.84. The study highlights AI's potential in early-stage design iterations, offering a suitable alternative to time-consuming eye-tracking testing sessions, but recommends combining AI tools with traditional methods for critical design decisions.

Colour is a complex and integral aspect of human visual experience, significantly impacting our daily lives by influencing emotions, behaviours, decisions, and even physical well-being. Colour perception is highly subjective, varying widely among individuals due to factors such as biological differences, psychological influences, and contextual conditions. To ensure accurate colour monitoring, objective colorimetric analysis is essential, particularly in the graphic industry where colour reproduction quality is paramount. This necessity becomes even more pronounced in the reproduction of skin tones, a critical area of research due to its association with memory colours and its influence on the perceived impression of people in images. Recent research has demonstrated that individuals with normal trichromatic vision can accurately differ subtle changes in skin colour, including slight shifts in chroma and hue towards more reddish tones compared to the actual skin shade. Objective colour reproduction analysis typically employs standard colorimetric measurement procedures, devices, and software solutions. However, commercially available devices are often expensive and robust, particularly in the objective quantification of colour reproduction on materials with specific surface properties and shapes. There is growing interest in quantitative colour measurements derived from low-cost, user-friendly software solutions paired with inexpensive imaging technologies, making objective colorimetric detection more accessible. In this paper, we investigate the potential of using two area-based open-source image analysis tools, ImageJ and Trigit, to assess skin tone colour reproduction accuracy. Targeted skin colour tones were ink-jet printed, scanned, and analyzed to extract colour coordinates using these tools, with manual measurements taken directly from the prints using a spectrophotometer as well. By calculating colour differences, we characterized the proposed colour measurement procedures. The research reviled certain advantages as well as limitations of usage of area-based open-source image analysis tools in characterizing skin colour reproduction.

Print nonuniformity is the variation of optical density (reflection) on the print and can occur in several different types that can be classified into two basic groups: random and systematic variations. This article examines two types from each group of variations that are most commonly found in digital printing systems: blotches and streaks, where amplitude and size were varied. Three most commonly used methods for measuring the print nonuniformity were used: Grey Level Co‐occurrence Matrix (GLCM), ISO 13660 and Integration (also Improved Integration) method. The results obtained by measuring were compared with a visual assessment to find the link between objective and subjective analysis and to define and determine which method/parameter is the best for the measurement of which type of print (non)uniformity. The obtained results indicate the possibility of selecting the measurement parameter, but that the choice of the parameter depends on the type of nonuniformity. It is concluded that in the case of the GLCM method, the entropy and energy parameters are strongly related to the visual assessment for all three sets of samples. These parameters can be used regardless of the type of nonuniformity. The ISO G parameter can be used to measure both graininess and mottling, while the ISO M parameter is only suitable for measuring mottling. Integration method could be used to measure systematic error. On the basis of conclusions of the research, it is possible to propose the development of an industrially applicable solution for measuring print nonuniformity of digitally printed samples.

The folding process is one of the most commonly used print finishing operations in graphical production. Therefore, surface damages on coated papers and paperboards, which are frequently appearing during this process, can have significant negative economic and environmental impacts. To reduce the adverse effects, fold crack resistance has become an active field of research. In the last decades, there were several computer-aided visual assessment methods introduced for fold-crack evaluation. These techniques were based on similar concepts of digital image analysis to quantitatively characterise the surface damage, but they had differences in utilisation as well as in the used image feature. In this study, fold crack distribution has been introduced as a new digital image feature for quality assessment. Fold crack distribution can be determined as a measure of crack scattering over the folding line. In this paper descriptive statistics, mean value, standard deviation and coefficient of variance have been used for qualitative characterisation of surface damage. The obtained results for mean value demonstrated increasing tendency by increasing the basis weight and had slightly lower value for samples in machine than cross direction. These results confirm the basic assumptions that on thicker substrates (i.e. on samples with higher basis weight) the cracks are larger, longer or grouped and that the folding process generates higher surface destruction is cross than machine direction. In the case of standard deviation, results suggest that in a case of varying mean values, crack distribution should be defined via coefficient of variation. Based on this analysis, the proposed approach to the calculation of crack distribution can serve as a new image characteristic for the qualitative measurement of the fold-crack resistance of coated papers.