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A one-step analysis method was developed for four types of amino acids using a microfluidic paper-based analytical device fabricated from chromatography filtration paper and laminate films. Aminoacyl-tRNA synthetase was used to detect each amino acid. The obtained laminated paper-based analytical device (LPAD) contained four enzymatic reaction areas. Colorimetric detection was performed based on the molybdenum blue reaction. A model method for the simple, easy, and simultaneous detection of several amino acid concentrations was suggested, in contrast to the conventional methods such as HPLC or LC–MS. The method provided a selective quantification at the ranges of 3.6–100 μM for tryptophan, 10.1–100 μM for glycine, 5.9–100 μM for histidine and 5.6–100 μM for lysine with a detection limit of 1.1 μM, 3.3 μM, 1.9 μM and 1.8 μM, respectively. LPAD fabrication was considerably simple, and the subsequent detection process was easy and required a short period of time (within 15 min).

Many studies have been conducted on measuring avian influenza viruses and their hemagglutinin (HA) antigens via electrochemical principles; most of these studies have used gold electrodes on ceramic, glass, or silicon substrates, and/or labeling for signal enhancement. Herein, we present a paper-based immunosensor for label-free measurement of multiple avian influenza virus (H5N1, H7N9, and H9N2) antigens using flexible screen-printed carbon nanotube-polydimethylsiloxane electrodes. These flexible electrodes on a paper substrate can complement the physical weakness of the paper-based sensors when wetted, without affecting flexibility. The relative standard deviation of the peak currents was 1.88% when the electrodes were repeatedly bent and unfolded twenty times with deionized water provided each cycle, showing the stability of the electrodes. For the detection of HA antigens, approximately 10-μl samples (concentration: 100 pg/ml–100 ng/ml) were needed to form the antigen–antibody complexes during 20–30 min incubation, and the immune responses were measured via differential pulse voltammetry. The limits of detections were 55.7 pg/ml (0.95 pM) for H5N1 HA, 99.6 pg/ml (1.69 pM) for H7N9 HA, and 54.0 pg/ml (0.72 pM) for H9N2 HA antigens in phosphate buffered saline, and the sensors showed good selectivity and reproducibility. Such paper-based sensors are economical, flexible, robust, and easy-to-manufacture, with the ability to detect several avian influenza viruses.

In this work heat transfer papers were loaded with a new core–shell pigment based on precipitating thin shell of titanium dioxide on a core of rice husk silica ash TiO2/RHSA to be applied in dye sublimation printing of textile fabrics. Besides, 0.1% (w/w) cationic polyacrylamide (CPAM) and 1% (w/w) bentonite (Bt) were also added sequentially to improve drainage and filler retention of the paper hand-sheets made from bleached kraft bagasse pulps. The effect of the new core–shell pigment on the mechanical and barrier properties, thermal stability and surface morphology of modified paper sheets were investigated. In addition, the study of transfer printability and ease of dye release from paper to fabric in this heat transfer printing of polyester fabrics using silk-screen printing under different transfer parameters were studied. Also, fastness measurements including washing, light and perspiration of printing polyester fabric were also estimated.

Oily water contamination has been sighted as one of the most global environmental pollution. Herein, copper hydroxide nanorods layer was constructed onto cellulosic filter paper surface cured with polydopamine, Ag nanoparticles, and Cu NPs through immersion method. This work has been aimed to produce a superhydrophobic and superoleophilic cellulosic filter paper. The structure, crystalline, and morphological properties of these modified cellulosic filter paper were investigated. Scanning electron microscope images confirmed that the modified surface was rougher compared with the pristine surface. The contact angle measurement confirmed the hydrophobic nature of these modified surfaces with a water contact angle of 169.7°. The absorption capacity was 8.2 g/g for diesel oil and the separation efficiency was higher than 99%. It was noted that the flux in the case of low viscosity solvent as n-hexane was 9663.5 Lm−2 h−1, while for the viscous oil as diesel was 1452.7 Lm−2 h−1.

We aimed to investigate differences in patients’ anxiety and satisfaction between patients undergoing paper-based patient decision aid (PDA) for shared decision-making (SDM) and those receiving computer-based PDA. We retrospectively collected questionnaires before and after SDM. Basic demographic data as well as anxiety, satisfaction, knowledge acquisition, and participation in SDM were recorded. We divided our population into subgroups according to use of paper-based or computer-based PDA. In addition, Pearson correlation analysis was applied to assess the relationships among variables. In total, 304 patients who visited our Division of Nephrology were included in the final analysis. Overall, over half of the patients felt anxiety (n = 217, 71.4%). Near half of the patients felt a reduction in anxiety after SDM (n = 143, 47.0%) and 281 patients (92.4%) were satisfied with the whole process of SDM. When we divided all the patients based on use of paper-based or computer-based PDA, the reduction of anxiety level was greater in the patients who underwent paper-based PDA when compared with that of those who underwent computer-based PDA. However, there was no significant difference in satisfaction between the two groups. Paper-based PDA was as effective as computer-based PDA. Further studies comparing different types of PDA are warranted to fill the knowledge gaps in the literature.

Recycled paper and some hardwood paper often display poorer mechanical properties, which hinder its practical applications and need to be addressed. In this work, cellulose nanofibrils (CNFs) obtained by a combined process of enzymatic hydrolysis and grinding (EG-CNFs), grinding and microfluidization (GH-CNFs) or TEMPO-mediated oxidation and grinding (TE-CNFs) were characterized by scanning electron microscopy (SEM) and atomic force microscopy (AFM). Moreover, CNFs were made into films on which some characterizations including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and UV–Vis transmittance spectroscopy were implemented. Results showed that CNF fibrillation was promoted as times of passes increased in microfluidization, and CNFs pretreated by enzyme possessed shorter length. Crystallinity of CNFs was related to CNF manufacturing methods, while CNF films’ transparency was correlated to CNF diameter distributions. Moreover, CNFs were applied with different dosages on recycled and hardwood paper. Lengths of CNFs, strength of CNF network, and pulp properties were critical factors affecting the mechanical strength of CNFs-enhanced paper. GH-CNFs showed better strengthened effect on tensile strength of paper than TE-CNFs and EG-CNFs. The best overall improvement was achieved at GH-CNF10 dosage of 5.0 wt% on hardwood paper. The increment of tensile index, burst index, and folding endurance were 108.32%, 104.65%, and 600%, respectively. This work aims to find out the relationship between production methods and morphologies of CNFs and how the morphological characteristics of CNFs affecting the mechanical performance of paper when they are added as strength additives.

This study performs a chemical investigation of blood plasma samples from patients with and without fibromyalgia, combined with some of the symptoms and their levels of intensity used in the diagnosis of this disease. The symptoms evaluated were: visual analogue pain scale (VAS); fibromyalgia impact questionnaire (FIQ); Hamilton anxiety rating scale (HAM); Tampa Scale for Kinesiophobia (TAMPA); quality of life Questionnaire—physical and mental health (QL); and Pain Catastrophizing Scale (CAT). Plasma samples were analyzed by paper spray ionization mass spectrometry (PSI-MS). Spectral data were organized into datasets and related to each of the symptoms measured. The datasets were submitted to multivariate classification using supervised models such as principal component analysis with linear discriminant analysis (PCA-LDA), successive projections algorithm with linear discriminant analysis (SPA-LDA), genetic algorithm with linear discriminant analysis (GA-LDA) and their versions with quadratic discriminant analysis (PCA/SPA/GA-QDA) and support vector machines (PCA/SPA/GA-SVM). These algorithm combinations were performed aiming the best class separation. Good discrimination between the controls and fibromyalgia samples were observed using PCA-LDA, where the spectral data associated with the CAT symptom achieved 100% classification sensitivity, and associated with the VAS symptom achieved 100% classification specificity, with both symptoms at the moderate level of intensity. The spectral variable at 579 m/z was found to be substantially significant for classification according to the PCA loadings. According to the human metabolites database, this variable can be associated with a LysoPC compound, which comprises a class of metabolites already evidenced in other studies for fibromyalgia diagnosis. This study proposed an investigation of spectral data combined with clinical data to compare the classification ability of different datasets. The good classification results obtained confirm this technique is as a good analytical tool for the detection of fibromyalgia, and provides theoretical support for other studies about fibromyalgia diagnosis.

Nowadays, due to environmental pollution and natural energy consumption caused by waste paper, many researches are being conducted on the reuse of printed-paper. To recycle the paper, de-inking has to be performed. In this article, in order to reduce the use of the commonly used de-inking chemicals, the effect of an atmospheric pressure helium plasma treatment on the de-inking enhancement of printed-paper is studied. Through colorimeter and UV-visible spectrometer measurements the plasma treatment is shown to speed up the de-inking. While SEM observations and FTIR measurements suggest that the paper quality is retained upon plasma treatment, the increase of surface hydrophilicity measured by water contact angle measurements, compared to non-treated paper, is believed to enhance the fiber swelling of the paper and lead to a faster ink removal.

Numerous scientific, health care, and industrial applications are showing increasing interest in developing optical pH sensors with low-cost, high precision that cover a wide pH range. Although serious efforts, the development of high accuracy and cost-effectiveness, remains challenging. In this perspective, we present the implementation of the machine learning technique on the common pH paper for precise pH-value estimation. Further, we develop a simple, flexible, and free precise mobile application based on a machine learning algorithm to predict the accurate pH value of a solution using an available commercial pH paper. The common light conditions were studied under different light intensities of 350, 200, and 20 Lux. The models were trained using 2689 experimental values without a special instrument control. The pH range of 1: 14 is covered by an interval of ~ 0.1 pH value. The results show a significant relationship between pH values and both the red color and green color, in contrast to the poor correlation by the blue color. The K Neighbors Regressor model improves linearity and shows a significant coefficient of determination of 0.995 combined with the lowest errors. The free, publicly accessible online and mobile application was developed and enables the highly precise estimation of the pH value as a function of the RGB color code of typical pH paper. Our findings could replace higher expensive pH instruments using handheld pH detection, and an intelligent smartphone system for everyone, even the chef in the kitchen, without the need for additional costly and time-consuming experimental work.

In medical institutions, radiation shielding is an effective strategy to protect medical personnel and patients from exposure. Reducing the weight of the shield worn by medical personnel in the radiation generating area plays a key role in improving their productivity and mobility. In this study, a new lightweight radiation shield was developed by electrospinning a polymer-tungsten composite material to produce nanofibers with a multi-layered thin-film structure similar to that of a morpho butterfly wing. The fabricated shield was in the form of 0.1 mm thick flexible shielding paper. The multi-layer structure of the thin shielding paper was obtained through nanofiber pattern formation via electrospinning a dispersion of tungsten particles. At 0.1 mm thickness, the paper’s shielding rate was 64.88% at 60 keV. Furthermore, at 0.3 mm thick and arranged in a laminated structure, the shielding rate was 90.10% and the lead equivalent was 0.296 mmPb. When used as an apron material, the weight can be reduced by 45% compared to existing lead products. In addition, the material is highly processable and can be used to manufacture various flexible products, such as hats, gloves, underwear, and scarves used in medical institutions.

The study's goal was to develop a spore-based paper strip biosensor for detecting ß-lactam antibiotics in milk using the enzyme induction principle. A new spore-based paper strip biosensor has been developed after important operating parameters such as spore volume, substrate volume, exposure time and temperature, and incubation time and temperature were optimised. The limit of detection for various ß-lactam antibiotics, including amoxicillin, penicillin, ampicillin, carbenicillin, cloxacillin, nafcillin, oxacillin, cephalothin, cefalexin, cefoxitin, cefazolin, and cefuroxime, was determined in milk with detection sensitivity of 1 ppb, 2 ppb, 2 ppb, 10 ppb, 10 ppb, 10 ppb, 20 ppb, 10 ppb 1000 ppb, 10 ppb 300 ppb and 100 ppb, respectively. It was also tested with other contaminants such non-ß-lactam antibiotics, pesticides, aflatoxin, heavy metals, and other chemical contaminants, and no interference was found, indicating that the created biosensor had a low rate of false positive and negative results. In comparison to the AOAC-approved CHARM-ROSA ß-lactam strip test, which identified 7 raw milk and zero pasteurised milk samples positive for ß-lactam antibiotics, the sensor was further analysed and verified using 200 raw milk and 105 pasteurised milk samples. This indicates a perfect match between our biosensor and the AOAC-approved CHARM-ROSA ß-lactam strip test. The developed spore-based paper strip biosensors are expected to be useful in the rapid and cost-effective detection of ß-lactam antibiotic residues in milk samples at the dairy farm, reception dock, and production units, respectively.

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60–70% and the average correlation of 3-by-1 ECGs achieved 80–90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects.