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In the field of research on the application of digital printing to textile materials, there are still many research issues that arise from the very demanding interaction of digital printing technology and the complex, heterogeneous surface system of textile materials. This is precisely why the area of pre-treatment of textile materials is in need of research, and the purpose of this research was to establish the level of influence of physical and chemical activation of the textile surface with plasma and the possibility of improving the quality of the print and colour reproduction.

The paper deals with the possibility of applying argon and oxygen cold low-pressure plasma in the processing of cellulose knitted fabrics, with the aim of improving the quality of the print and colour reproduction in digital pigment inkjet printing. The selected raw material samples were 100% raw cotton and lyocell. After plasma treatment, the samples were printed by digital ink jet printing with water-based pigment printing ink. An analysis of the micromorphological structure of untreated and plasma-treated samples before and after printing was carried out, and a comparative analysis of the colour of the printed elements was carried out depending on the pre-treatment.

The conducted research showed a positive influence of plasma pre-treatment on the coverage of the fibre surface with pigments, the uniformity of pigment distribution along the fibre surface and the uniformity of the distribution of the polymeric binder layer. This has a positive effect on colour reproduction. Also, certain improvements in colourfastness to washing were obtained.

Considering the complexity of the topic, although exhaustive, this research is not sufficient in itself, but opens up new questions and gives ideas for further research that must be carried out in this area.

Also, this kind of research contributes to the possibility of adopting the idea of industrial plasma transformation, as an ecologically sustainable functionalisation of textiles, which has not yet been established.

This research is certainly a contribution to the establishment of acceptable textile pre-treatment methods in the field of digital printing, as one of the key quality factors in digital textile printing (DTP). Considering the still large number of obstacles and unanswered questions encountered in the field of digital printing on textiles, this kind of research is a strong contribution to the understanding of the fundamental mechanisms of the complex interaction between printing ink and textile.

The incompatibility of natural fibers with polymer matrices is one of the key obstacles restricting their use in polymer composites. The interfacial connection between the fibers and the matrix was weak resulting in a lack of mechanical properties in the composites. Chemical treatments are often used to change the surface features of plant fibers, yet these treatments have significant drawbacks such as using substantial amounts of liquid and chemicals. Plasma modification has recently become very popular as a viable option as it is easy, dry, ecologically friendly, time-saving and reduces energy consumption. This paper aims to explore plasma treatment for improving the surface adhesion characteristics of sisal fibers (SFs) without compromising the mechanical attributes of the fiber.

A cold glow discharge plasma (CGDP) modification using N₂ gas at varied power densities of 80 W and 120 W for 0.5 h was conducted to improve the surface morphology and interfacial compatibility of SF. The mechanical characteristics of unmodified and CGDP-modified SF-reinforced epoxy composite (SFREC) were examined as per the American Society for Testing and Materials standards.

The cold glow discharge nitrogen plasma treatment of SF at 120 W (30 min) enhanced the SFREC by nearly 122.75% superior interlaminar shear strength, 71.09% greater flexural strength, 84.22% higher tensile strength and 109.74% higher elongation. The combination of improved surface roughness and more effective lignocellulosic exposure has been responsible for the increase in the mechanical characteristics of treated composites. The development of hydrophobicity in the SF had been induced by CGDP N₂ modification and enhanced the size of crystals and crystalline structure by removing some unwanted constituents of the SF and etching the smooth lignin-rich surface layer of the SF particularly revealed via FTIR and XRD.

Chemical and physical treatments have been identified as the most efficient ways of treating the fiber surface. However, the huge amounts of liquids and chemicals needed in chemical methods and their exorbitant performance in terms of energy expenditure have limited their applicability in the past decades. The use of appropriate cohesion in addition to stimulating the biopolymer texture without changing its bulk polymer properties leads to the formation and establishment of plasma surface treatments that offer a unified, repeatable, cost-effective and environmentally benign replacement.

The authors are sure that this technology will be adopted by the polymer industry, aerospace, automotive and related sectors in the future.

Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.

Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.

The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.

This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.

The purpose of this study is to investigate the structural, surface roughness and corrosion properties of the zirconium oxide thin films deposited onto SS304 substrates using the direct current (DC) magnetron sputtering technique.

DC sputtering at different powers – 80, 100 and 120 W – was used to deposit ZrO₂ thin films onto different substrates (Si/SS304) without annealing of the substrate. Atomic force microscope (AFM), energy-dispersive X-ray spectroscopy (EDS), Tafel extrapolation and contact angle techniques were applied to investigate the surface roughness, chemical compositions, corrosion behavior and hydrophobicity of these films.

Results showed that the thickness of the deposited film increased with power increase, while the corrosion current decreased with power increase. AFM images indicated that the surface roughness decreased with an increase in DC power. EDS analysis showed that the thin film has a stoichiometric ZrO₂ (Zr:O 1:2) composition with basic uniformity. Water contact angle measurements indicated that the hydrophobicity of the synthesized films decreased with an increase in surface roughness.

DC magnetron sputtering technique is infrequently used to deposition thin films. The obtained thin films showed good hydrophobic and anticorrosion properties. Finally, results are compared with other deposition techniques.

The purpose of this paper is to study the correlation between the thicknesses of the C–Ni films that have been prepared by RF-magnetron sputtering on quartz substrates and their three-dimensional (3D) micro morphology. In this work by AFM images, this paper studied stereo metric analysis of these films.

The C–Ni films have been prepared by RF-magnetron sputtering on quartz substrates using a mosaic target consisting of pure graphite and strips of pure nickel approximately 2 cm² attached to the graphite race track. The field emission scanning electronic microscopy (FESEM) images were used for the morphological characterization.

The histogram peaks are zero for all samples and the histograms are almost symmetric around zero. Temperature did not have much effect on the degree of isolation, so all four diagrams have similar results. The qualitative observations through statistical parameters of the 3D surface texture revealed that the smoothest surface has been obtained for C-Ni films annealed at 500 °C (Sa, Sq, Sz and Sv have the lower values), while the most irregular topography has been found for C-Ni films annealed at 300 °C (the fractal dimension D = 2.01 ± 0.131).

As shown in FESEM images, the size of the particles was increased for films deposited from 300 ºC to 800ºC; however, at 1000ºC, it decreased significantly. The histogram peaks are zero for all samples and the histograms were almost symmetric around zero. Also, the largest and lowest root mean heights (Sq) belong to films at 300 °C and 500 °C. Furthermore, the more irregular surface was found at 300 °C, and the more regular surface was found at 500 °C. As the temperature was increased to 800 °C, the values of the IAPSD function increased systematically, and then the values of the IAPSD function was decreased in the fourth sample. The surface skewness of samples annealed at 1000 °C was positive which confirms the lack of dominance of cavities on their surface with the highest amount of C-Ni films at 800 °C.

This study aims to investigate the ammonia-sensing performance of polyaniline/polyethylene oxide (PANI/PEO) and polyaniline/polyethylene oxide/zinc oxide (PANI/PEO-ZnO) composite nanofibers at room temperature.

Gas sensor structures were fabricated using microfabrication techniques. First, onto the SiO₂ wafer, gold electrodes were fabricated via thermal evaporation. PANI/PEO nanofibers were produced by the electrospinning method, and the ZnO layer was deposited by using radio frequency (RF) magnetron sputtering on the electrospun nanofibers as a sensing layer. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and X-ray diffraction were performed to characterize the analysis of nanofibers. After all, gas sensing analysis of PANI/PEO and PANI/PEO/ZnO nanofibers was conducted using an experimental setup at room temperature conditions. Furthermore, the impact of humidity (17%–90% RH) on the sensor resistance was actively investigated.

FTIR analysis confirms the presence of functional groups of PANI, PEO and ZnO in nanofiber structure. SEM micrographs demonstrate beads-free, thinner and smooth nanofibers with ZnO contribution to electrospun PANI/PEO nanofibers. Moreover, according to the gas sensing results, the PANI/PEO nanofibers exhibit 115 s and 457 s response time and recovery time, respectively. However, the PANI/PEO/ZnO nanofibers exhibit 245 s and 153 s response time and recovery time, respectively. PANI/PEO/MOx composite nanofibers ensure stability to the NH₃ gas owing to the high surface/volume ratio and decrease in the humidity dependence of gas sensors, making gas sensors more stable to the environment.

In this study, ZnO was deposited via RF magnetron sputtering techniques on PANI/PEO nanofibers as a different approach instead of in situ polymerization to investigate and enhance the sensor response and recovery time of the PANI/PEO/ZnO and PANI/PEO composite nanofibers to ammonia. These results indicated that ZnO can enhance the sensing properties of conductive polymer-based resistive sensors.