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The dimensional absorbency properties of paper towels were studied, focusing on three- and two-dimensional absorption mechanisms. Key factors affecting these absorption mechanisms were identified through a series of experiments and principal component analysis (PCA). The results showed that the water absorption capacity, driven by capillary action (porosity), exhibited differences between two-dimensional surface absorption (in the X and Y directions) and three-dimensional bulk absorption (including the Z direction, or thickness). Porosity analysis revealed that three-dimensional absorbency is highly correlated with porosity, whereas two-dimensional absorbency has a relatively low correlation and is influenced by fiber properties such as length and width, as well as mass-related characteristics including fines content and freeness. The findings highlight the need to balance these dimensional properties to achieve optimal absorbency in paper towel products. Additionally, this study provides a foundation for developing more efficient paper towels and offers valuable insights into the complex mechanisms of paper towel absorbency, which will aid in the development of improved hygiene paper products.

Curl propensity is a critical-to-quality (CTQ) property of paper, as it causes severe problems during printing and other final conversion operations. The main papermaking factor causing the curl phenomenon is the existence of a fiber orientation (FO) gradient across the thickness direction (or ZD), also known as two-sidedness. Therefore, a methodology that characterizes the FO across the ZD is fundamental for papermakers. In this work, we propose and validate an efficient and cost-effective protocol based on sheet splitting and image analysis. Besides assessing the level of FO two-sidedness, the methodology also provides insights into the flow dynamics in the draining zone of the forming section of the paper machine and the drying stresses built into the paper. This information is relevant for monitoring, optimizing, and troubleshooting activities in the paper industry. [ABSTRACT FROM AUTHOR]

One of the most popular and well-established forms of payment in use today is paper money. Handling paper money might be challenging for those with vision impairments. Assistive technology has been reinventing itself throughout time to better serve the elderly and disabled people. To detect paper currency and extract other useful information from them, image processing techniques and other advanced technologies, such as Artificial Intelligence, Deep Learning, etc., can be used. In this paper, we present a meticulously curated and comprehensive dataset named ‘NSTU-BDTAKA’ tailored for the simultaneous detection and recognition of a specific object of cultural significance - the Bangladeshi paper currency (in Bengali it is called ‘Taka’). This research aims to facilitate the development and evaluation of models for both taka detection and recognition tasks, offering a rich resource for researchers and practitioners alike. The dataset is divided into two distinct components: (i) taka detection, and (ii) taka recognition. The taka detection subset comprises 3,111 high-resolution images, each meticulously annotated with rectangular bounding boxes that encompass instances of the taka. These annotations serve as ground truth for training and validating object detection models, and we adopt the state-of-the-art YOLOv5 architecture for this purpose. In the taka recognition subset, the dataset has been extended to include a vast collection of 28,875 images, each showcasing various instances of the taka captured in diverse contexts and environments. The recognition dataset is designed to address the nuanced task of taka recognition providing researchers with a comprehensive set of images to train, validate, and test recognition models. This subset encompasses challenges such as variations in lighting, scale, orientation, and occlusion, further enhancing the robustness of developed recognition algorithms. The dataset NSTU-BDTAKA not only serves as a benchmark for taka detection and recognition but also fosters advancements in object detection and recognition methods that can be extrapolated to other cultural artifacts and objects. We envision that the dataset will catalyze research efforts in the field of computer vision, enabling the development of more accurate, robust, and efficient models for both detection and recognition tasks.

Forensic document examination has been considered one of the most important fields in the forensic science. For this, a quick, effective, and cheap techniques for evaluation of forensic evidence is highly required. This study explores the comparison of A4 document paper by chemical and physical analysis. 16 of white A4 paper from different brands were analyzed by FTIR for investigation of structural characteristics. Video Spectral Comparator 8000 has been used to compare the A4 papers under different light source. The inorganic filler composition in the paper is also explored by ICP‐OES. The calcium carbonate content in document papers play a discriminant role which found to be between 16.02 % and 35.54 %. Also, water contact angle has been utilized in order to examine the surface of paper which is generally related to the inorganic filler and other additives. The WCA results ranged between 83° and 111° that enable quick and easy way to compare A4 document papers. The experimental results have demonstrated that besides FTIR methods, other methods such as ICP‐OES for elemental composition, WCA for wetting test, VSC for visualization under different light of source could have been applied for comparison of document papers in case of criminal cases. [ABSTRACT FROM AUTHOR]

This study proposes a method to determine the two‐dimensional absorption capacity of paper towels. Individual plies were tested to minimize the effects of transverse wicking on wetting. The method involves arbitrary point source injection of liquid into the towel surface while recording microscopic images of the wetted areas. The samples were selected from the main manufacturing processes: conventional wet pressing and through‐air drying. Wetting graphs that illustrate the dynamic and static wetting behaviors were generated. The driving force was found to have a negligible effect on the maximum wetted area. The maximum wetted area and the paper grammage were used to determine the two‐dimensional absorption capacity. The results were validated by the basket‐immersion test method. [ABSTRACT FROM AUTHOR]

A turn-on fluorescent aptasensor based on a paper-based microfluidic chip was developed to detect arsenite via aptamer competition strategy and smartphone imaging. The chip was prepared by wax-printing hydrophilic channels on filter paper. It is portable, low-cost, and environmentally friendly. Double-stranded DNA consisting of aptamer and fluorescence-labeled complementary strands was immobilized on the reaction zone of the paper chip. Due to the specific strong binding between aptamer and arsenite, the fluorescent complementary strand was squeezed out and driven by capillary force to the detection area of the paper chip, so that the fluorescent signal arose in the detection area under the excitation wavelength of 488 nm. Arsenite can be quantified by using smartphone imaging and RGB image analysis. Under the optimal conditions, the paper-based microfluidic aptasensor exhibited excellent linear response over a wide range of 1 to 1000 nM, with a detection limit as low as 0.96 nM (3σ). [ABSTRACT FROM AUTHOR]

Dried blood spots (DBSs) are formed by collecting a small sample of blood on specialized filter paper and allowing it to dry naturally. Various domains of life sciences and drug research extensively use DBSs as a sampling technique. The "Hematocrit (Ht) effect" affects assay bias, and several strategies have been put forth to deal with it, including the correction of quantified concentrations using an appropriate correction factor. The approach was previously applied, following the utilization of an image processing algorithm developed in Matlab® to derive a reliable equation correlating DBS areas to Ht% values. The present work looks more closely at the application of image analysis to the evaluation of Ht in DBS samples. Utilizing image analysis software, DBS samples with known Ht values were processed. Preparation of cards has followed a previously developed protocol for the appropriate formation of uniform area DBSs, irrespective of Ht. The resulting areas showed close resemblance to the respective theoretical areas calculated by applying the correlation equation. Following that, the equation was utilized to determine the Ht values for each sample, and a comprehensive comparison of measured versus calculated Ht was carried out using various statistical approaches for method comparison. The results demonstrated a strong correlation, suggesting the method's viability in estimating Ht for unknown DBS samples. [ABSTRACT FROM AUTHOR]

BACKGROUND: This work proposes the use of a simple, inexpensive method for the hydrothermal synthesis of fluorescent carbon dot nanoparticles (CDs) from rice starch aimed at the determination of Hg2+ in water. The proposed method involved using a paper‐based analytical device coupled to a 3D plate, with a UV‐LED chamber and a smartphone for the acquisition and analysis of the fluorescence digital images of the CDs. RESULTS: The size of the carbon dots ranged from 0.5 to 3 nm, with an average particle size of ~1 nm. The functionalization of carbon dots with methimazole allowed a high selective for Hg2+ determination. The results obtained showed a linear response R2 of 0.997 and Hg2+ concentration in the range of 0.5–45.0 μmol L−1 with a limit of detection and limit of quantitation of 0.23 and 0.62 μmol L−1, respectively. The results of the study show that there were no significant differences, at 95% confidence level, between the data obtained from the application of the proposed method and the reference method. CONCLUSION: The proposed method is in line with the principles of green chemistry, as it involves the use of renewable sources for starch extraction and a hydrothermal synthesis process that does not employ toxic reagents. In addition, the method employs only 15 μL reagent/sample. © 2024 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

The evaluation of wrinkles in deep-drawn fiber-based materials is crucial for the assessment of product quality and the optimization of manufacturing processes. Wrinkling is a common phenomenon in the deep-drawing process and is caused by tangential compressive stresses on the flanges of the blank. This phenomenon is particularly prevalent for fiber-based materials with high tensile depths and can seriously affect the appearance and mechanical properties of the final product. The objective of this study is to identify the key process parameters affecting wrinkling and to deepen the understanding of their roles and interactions using wrinkle data for deep-drawn paper products. Image analysis techniques are employed, supported by a specially constructed darkroom platform to ensure uniform light intensity for capturing photographs. An automated program is developed for the detection and evaluation of wrinkle characteristics and distribution, which allows the free choice of the region to be detected and the representation of the wrinkle geometry not limited by the number. To enhance the precision of this program, the ellipticity is initially rectified for products without flanges, specifically cup-shaped deep-drawn products. The ellipticity is caused by the pronounced springback effect of the paperboard. The approach is employed to investigate the impact of material properties, blank holder force, drawing depth, drawing clearance, and punch speed on wrinkling formation after the deep-drawing process. The findings reveal that the blank holder force and drawing clearance are critical factors in wrinkle formation, with higher blank holder force generally leading to increased wrinkle numbers. [ABSTRACT FROM AUTHOR]

We have achieved a successful synthesis of a label-free, flexible, portable, and colour-indicating biosensor using cholesteric nanostructure liquid crystals (LCs), commonly referred to as cholesteric LCs (CLCs), on paper fibres. This innovative approach holds significant promise for rapid screening in precision medicine and other applications. We incorporated optically active nematic LCs in various configurations within CLCs, allowing them to rotate along different axes. When exposed to light, these LCs reflected light at specific wavelengths. By employing polarised optical microscopy and a charged couple device (CCD), we conducted titration experiments with bovine serum albumin (BSA) on the paper substrate and quantified protein concentrations based on the observed colour shifts. Utilising internal Python pixel analysis software, we evaluated the red, green, and blue colour components, which served as biomarkers for different BSA concentrations. Our results demonstrated a linear correlation between BSA concentration and colour variations. This CLC-based paper-substrate biosensor exhibits the potential to efficiently screen a large number of samples, thereby enhancing the diagnostic accuracy in clinical settings. Combining the colour-indicating and quantifiable attributes of CLCs with the affordability and portability of paper substrates, we have developed a versatile biosensor. Its ease of use, rapid response, and high sensitivity make it a promising tool for future disease detection endeavours. [ABSTRACT FROM AUTHOR]

During the storage of celluloid pictures for animation films over half a century, an interleave paper adhered to acrylic paint. The purpose of this study is to establish a methodology to cleanly remove the paper from the paint. A layered film, a replica of the celluloid pictures, adhered with paper was prepared and immersed in water or ethanol. The effect of these solvents on the peeling behavior was investigated using a peel test. The maximum peel force for the dry layered film in was distributed at ~0.5 N, independently of the peel speed. The peel force was significantly reduced after the layered film was immersed in pure water or ethanol. A morphological observation revealed that the dry paper was peeled off via the cohesive failure of the paper. After the layered film was immersed in pure water, the paper was also peeled off via cohesive failure. The layered film immersed in ethanol was peeled off at the paper/paint interface. To clear the effect of the volume change in the paint on peel behavior, the relative volume was determined via image analysis. The relative volume of paint was 1.56 in pure water and 1.37 in ethanol. It can be considered that the large difference in the volume of paint induces a large shear stress at the paint/paper interface. [ABSTRACT FROM AUTHOR]

A user-friendly, cost-effectively, portable, and environmentally friendly colorimetric sensor for the quantitative determination of formaldehyde was developed based on the combining of microfluidic paper-based analytical device (μPAD), headspace microextraction (HSME), and digital image colorimetry. Coupling HSME and μPAD led to enhancements in selectivity and sensitivity of the sensor through sample cleanup and analyte enrichment. To construct the μPAD-HSME device, two pieces of paper as the sample and detection zone were placed facing each other so that a small common and sealed space was created between them. The color change occurred when the analyte in the gaseous form crossed this gap and reached the detection zone. Colorimetric sensing in the detection zone was performed based on the Hantzsch reaction. The color change in the detection zone was recorded by a smartphone and digital images were processed using image analysis software based on the RGB model. The influence of some key variables on the sensitivity of the method including derivatization reagent composition, sample volume, extraction temperature, and extraction time was studied and optimized. The linear dynamic range of the method was obtained in two ranges of 0.10–0.75 and 0.75–5.0 mg L−1 with a limit of detection of 0.03 mg L−1. The recoveries were in the range 80–126% for the quantification of formaldehyde in textile, milk, and wastewater samples. [ABSTRACT FROM AUTHOR]

A colorimetric sensing method based on a paper-based vapor-test kit was successfully developed for the selective and sensitive real-time monitoring of formalin in food samples. The device was specifically designed to efficiently extract and detect formalin simultaneously. A microcentrifuge tube was used as the sample solution container, with the inner cap serving as the reaction and detection zone. Formalin was converted into gaseous formaldehyde through controlled heating, which was then extracted and collected on a filter paper coated with Nash's reagent. The color change on paper was used for formalin quantification using a smartphone for detection and image analysis. Under optimal conditions, our method provided a linear range of 0.5–75 mg L−1 with a detection limit of 0.11 mg L−1. This method effectively determined formalin in fresh food and vegetable samples, with recoveries ranging from 92 to 111%, demonstrating comparable accuracy to the standard method for practical food quality control and safety. [Display omitted] • A sensitive paper-based vapor-test kit was successfully developed for formalin. • This innovative approach combined headspace extraction, PADs, and image analysis. • An efficient sensing test kit represents a significant advancement for rapid, on-site detection of formalin in food products. • The sensor is cost-effective by utilizing commercially available materials. [ABSTRACT FROM AUTHOR]

Flow and spontaneous imbibition phenomena in porous media are important for various industrial applications, including printing and medical lateral flow assays. Their quantitative characterization is important to better understand and select the appropriate raw materials. However, standard methods often require time-consuming tests, and/or expensive equipment. Different time scales must be considered, limiting the range of possible characterization tools. A novel experimental approach based on image analysis for characterizing spontaneous imbibition processes is presented. Hence, ultra-fast diffusion may be quantitatively characterized. Models are issued from the literature to consider physical phenomena at small (milliseconds) and medium range (seconds) of time scales. The obtained experimental data fit with theoretical results, providing valuable insights into the understanding of fluid flow behavior at different time scales. Furthermore, the identification of some physical properties for either the fluid, or the substrate, based on the theoretical models are possible, as the contact angle, which remains to be otherwise challenging. This study contributes to bridging the gap between spontaneous imbibition and capillary phenomena at different time scales, their modeling, and a characterization of material and/or fluid properties paving the way for enhanced understanding and control of fluid behavior in porous media. Different papers are considered to illustrate the method. [ABSTRACT FROM AUTHOR]

Paper-based colorimetric spot test for the detection of promethazine misuse designed for onsite forensic applications. [Display omitted] • This work develops a paper-based colorimetric spot test for promethazine detection. • The assay uses potassium persulfate to oxidize promethazine producing a pink color. • The method employs digital image analysis for quantification. • The device's stability is enhanced for a shelf life of three months. • The device effectively detects promethazine in forensic samples. This study introduces a paper-based colorimetric spot test for the rapid and cost-effective detection of promethazine, featuring enhanced stability and user-friendliness. The developed device employs a potassium persulfate reagent encapsulated in a xerogel matrix, facilitating a simple chemical oxidation reaction that produces a pink color upon promethazine detection. Color quantification is efficiently achieved using a digital camera, with image analysis performed through ImageJ software to convert color image into a blue intensity scale. Under optimized conditions, the method demonstrates a linear calibration curve within the range of 100–600 mg/L and a detection limit of 20 mg/L. The paper-based colorimetric spot test exhibited good intra-day and inter-day accuracy of the recoveries of 98–102 % and 99–101 %, respectively. Good precision of measurement was also achieved (5.6 % RSD). The xerogel matrix ensures the long-term storage stability of the reagent for three months when stored at both 25 °C and 4 °C, making the device suitable for field applications. When applied to detection of promethazine in recreational drug beverage samples and pharmaceutical formulations, the performance of the proposed method showed no significant statistical differences compared to gas chromatography–flame ionization detection (GC–FID). This low-cost, portable, and disposable tool is suited for onsite screening of promethazine, representing a significant advancement in rapid drug detection methodologies. [ABSTRACT FROM AUTHOR]

Medieval paper, a handmade product, is made with a mould which leaves an indelible imprint on the sheet of paper. This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet. Extracting these features allows the identification of the paper stock and gives information about the chronology, localisation and movement of manuscripts and people. Most computational work for feature extraction of paper analysis has so far focused on radiography or transmitted light images. While these imaging methods provide clear visualisation of the features of interest, they are expensive and time consuming in their acquisition and not feasible for smaller institutions. However, reflected light images of medieval paper manuscripts are abundant and possibly cheaper in their acquisition. In this paper, we propose algorithms to detect and extract the laid and chain lines from reflected light images. We tackle the main drawback of reflected light images, that is, the low contrast attenuation of chain and laid lines and intensity jumps due to noise and degradation, by employing the spectral total variation decomposition and develop methods for subsequent chain and laid line extraction. Our results clearly demonstrate the feasibility of using reflected light images in paper analysis. This work enables feature extraction for paper manuscripts that have otherwise not been analysed due to a lack of appropriate images. We also open the door for paper stock identification at scale. [ABSTRACT FROM AUTHOR]

We propose a printed 384‐well microtiter paper‐based fluorescent chemosensor array device (384‐well microtiter PCAD) to simultaneously categorize and discriminate saccharides and sulfur‐containing amino acids for food analysis. The 384‐well microtiter PCAD requiring 1 μL/4 mm2 of each well can allow high‐throughput sensing. The device embedded with self‐assembled fluorescence chemosensors displayed a fingerprint‐like response pattern for targets, the image of which was rapidly captured by a portable digital camera. Indeed, the paper‐based chemosensor array system combined with imaging analysis and pattern recognition techniques not only successfully categorized saccharides and sulfur‐containing amino acids but also classified mono‐ and disaccharide groups. Furthermore, the quantitative detectability of the printed device was revealed by a spike and recovery test for fructose and glutathione in a diluted freshly made tomato juice. We believe that the 384‐well microtiter PCAD using the imaging analysis system will be a powerful sensor for multi‐analytes at several categorized groups in real samples. [ABSTRACT FROM AUTHOR]

Especially in the field of paper and print post-processing on an industrial scale, continuous cutting operations generate paper dust. Despite preventive measures like dedusting units, a high paper web velocity induces the release of particulate dust. As a result, the particles settle and accumulate on machine components and in the surrounding area. Resuspended particles around a potential ignition source represent a fire hazard. The prevention of such fire incidents requires frequent servicing and maintenance. However, the shutdown time on industrial scale applications remains cost-intensive. In this research, a cutting plotter executes reproducible knife edge cutting sequences to identify significant parameters affecting the paper dust formation on a macro and micro scale. For this purpose, an extraction system collects the exposed particles along the cutting line. The applied methods include gravimetric determination of the dust mass and dynamic image analysis to characterise particle size and shape. Final results indicate a significant influence of the blade wear condition. [ABSTRACT FROM AUTHOR]

The responses of the paper-based colorimetric sensor arrays are typically recorded by an imaging device. The color values of the images are subjected to chemometrics data analysis, with a view to extract the relevant information. As is the case with data extracted from other analytical instruments, these data must undergo pre-processing prior to undergoing further analysis. This study represents the first comprehensive and systematic investigation into the impact of data pre-processing techniques on the quality of subsequent data analysis methods applied to imaging data collected from paper-based colorimetric sensor arrays. The use of color difference data (calculated by subtracting the images of the sensors before exposure from those after exposure) revealed that pre-treatment of the data was not a critical factor, although it could reduce the complexity of the model. For example, the number of principal components in the principal component-linear discriminant analysis model was reduced from eight (for data that had not been pre-processed) to three (for pre-processed data) to achieve the same level of accuracy (92 %). Nevertheless, the pivotal role of data pre-processing was elucidated through the examination of data sets collected immediately following exposure to the samples' vapor. It was demonstrated that the use of an appropriate pre-processing method allows for the elimination or significant reduction of between-sensor variations, obviating the necessity for the inclusion of data from images taken prior to exposure. With regard to the objective of classification, the object pre-processing methods that demonstrated particular promise were mean (or median) centering, Pareto scaling and standard normal variate. To illustrate, in the analysis of volatile organic compounds by an array of metallic nanoparticles, the cross-validation classification accuracy of the unprocessed data, which was 70 %, increased to 95 % when unit variance scaling and range scaling were applied to objects and variables, respectively. In the calibration phase, the majority of pre-processing methods enhanced the quality of the regression models. Using suitable pre-processing methods for both objects and variables, eliminated the need for using the before exposing image of the CSAs. • Pre-processing can eliminate or significantly decrease the between sensor variations. • Mean-centering, Pareto scaling and standard normal variate showed to be more promising. • By choosing a suitable pre-processing, the need for capturing before exposing image is eliminated. • For classification, object scaling and for regression analysis, variable scaling played a more significant role. [ABSTRACT FROM AUTHOR]

Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep convolutional neural networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the work exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pretrained models and general adversarial networks that aid in improving convolutional networks' performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on COVID-19 detection and child bone age prediction. [ABSTRACT FROM AUTHOR]

Sensor arrays, which draw inspiration from the mammalian olfactory system, are fundamental concepts in high-throughput analysis based on pattern recognition. Although numerous optical sensor arrays for various targets in aqueous media have demonstrated their diverse applications in a wide range of research fields, practical device platforms for on-site analysis have not been satisfactorily established. The significant limitations of these sensor arrays lie in their solution-based platforms, which require stationary spectrophotometers to record the optical responses in chemical sensing. To address this, this review focuses on paper substrates as device components for solid-state sensor arrays. Paper-based sensor arrays (PSADs) embedded with multiple detection sites having cross-reactivity allow rapid and simultaneous chemical sensing using portable recording apparatuses and powerful data-processing techniques. The applicability of office printing technologies has promoted the realization of PSADs in real-world scenarios, including environmental monitoring, healthcare diagnostics, food safety, and other relevant fields. In this review, we discuss the methodologies of device fabrication and imaging analysis technologies for pattern recognition-driven chemical sensing in aqueous media. [Display omitted] • Cross-reactive sensors are applied to paper-based arrays for simultaneous detection. • Portable digital recorders can be used for detecting various responses. • Imaging analysis techniques can accelerate accurate data processing. [ABSTRACT FROM AUTHOR]

[Display omitted] • Assessment of ash from co-combustion of bark and sludges for nutrient recovery. • P amounts indicate that ash from co-combustion can be useful as a soil improver. • Morphology of slag particles was analysed with X-ray micro-tomography. • Overall, a high number of small and discrete pores. • Relatively thick particle walls and few pore openings indicates a need of grounding. Recycling phosphorus from waste streams for fertilization purposes could contribute to a sustainable society. The production in the pulp and paper industry results in several waste streams, among others nutrient-rich sludges in different forms. This study presents a detailed chemical and 3D characterization of ash from co-combustion of bark and two types of sludges from a paper mill; mixed sludge and biosludge. The combustion performance was investigated for these experiments and advanced analysis methods were used to characterise the ashes to correlate chemical and physical properties relevant for nutrient recycling. The elemental composition was determined by energy-dispersive X-ray spectroscopy; dominating crystalline phases by X-ray diffraction; and morphology, porosity, pore size distribution and active surface area of the slag were analysed with synchrotron-based X-ray micro-tomography and image analysis. Slag was formed in all combustion experiments to a large extent with increasing amounts with a higher proportion of sludge. Nutrient amounts indicate that slag particles from co-combustion of both biosludge and mixed sludge can be useful either as a soil improvement directly or for recovery processes. Slag from combustion of 30 wt% biosludge and 70 wt% bark contained the highest amount of phosphorus, 9 at % on a C and O free basis. Evaluation of tomography data showed that discrete and open pores could be distinguished on a micrometre scale. The porosity of the slag varied between the replicates and fuel mixtures, on average between 17 and 23 vol% for the bark and sludge mixtures. Open pore volume displayed large variations, on average 39–56 vol% of the pores were open pores connected to the surrounding volume. For all samples, 90 % of the pores were small, with an equivalent diameter under 30 μm, but the largest pore volume (80–90 %) consists of pores with an equivalent diameter over 75 μm. In soils, pores with a minimum equivalent diameter over 30 μm generally transmit water and the smaller pores store water. The slag particles have relatively thick walls, with few pore openings to the surroundings, indicating that the slag needs to be pre-treated by milling or crushing before application in the soil. [ABSTRACT FROM AUTHOR]

The ARMID@ Venezia project aims to safeguard extra-ordinary collections. both handwritten and printed, preserved in the library of the Benedetto Marcello Conservatory of Venice, heavily damaged by the exceptionally high tide of November, 2019. The project was moreover conceived to integrate various competencies from art history, conservation science, archival systems, and music philology, in order to propose a comprehensive and innovative perception on the conservation of documents. The project involves digitalization (through high-resolution photographic reproduction), virtual restoration and a detailed investigation of the materials used (supports, inks, pigments, dyes, etc.) for future physical restoration. Only non-invasive and non-destructive techniques are being employed, such as imaging analysis in the visible (including macro - and micro-photographic examination), near infrared (NIR), and near ultraviolet (UV-induced fluorescence or UVF, and reflected UV or UVR) range, on representative artefacts. In addition, non-invasive spectroscopic techniques, such as Fiber Optics Reflectance Spectroscopy (FORS), p.-Raman and X-Ray (XRF) were also applied for a detailed chemical characterization of materials used to support the results obtained through imaging analyses. In this article, the results of multispectral imaging (MSI) conducted in three different categories of music sources, paper manuscripts, parchment manuscripts, and printed books, are discussed and compared with spectroscopic techniques, confirming the benefits of photographic investigation in these artworks. The great assortment of available documents, in terms of constitutive materials, historical period of production and state of conservation, is a strong point of this project in order to test the potential holistic approach to safeguard the documents collected in the library. [ABSTRACT FROM AUTHOR]

The article presents a new technique for analyzing phenomena occurring during the measurement of the strength properties of paper in the conditions of compression of the tested samples with forces acting in the paper plane. The technique is based on collecting data on the current distance of the clamps holding the tested sample and the force exerted on the sample using a universal testing machine and on the simultaneous recording of image sequence of the sample during the measurement. Next, the resulting images are subjected to processing and analysis, the purpose of which is to extract information about the shape of the sample edge in all phases of the measurement. Its advantage is the ability to determine the deflection arrow of the sample and describe its shape using the selected function given by the analytical parametric formula. It will be helpful in further research on the development of an analytical model describing the phenomena occurring during paper compression, and a method to determine the mechanism of paper destruction and the corresponding maximum force that destroys a paper sample. [ABSTRACT FROM AUTHOR]

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate a simple and low-cost colorimetric method based on Fe(III) chloride and potassium hexacyanoferrate(III) for determination of acetaminophen, ascorbic acid and phenylephrine, which can be used in small laboratories using restricted materials and resources. A paper based sensor using the reagent was introduced. Significance of different variables in determination of drugs were studied by Placket-Burman design and optimal values for concentration of hydrochloric acid, Fe(III) chloride and potassium hexacyano ferrate(III), 0.01 M, 0.004 M and 0.004 M, respectively were obtained. Without using any sophisticated instrument and only by image analysis the concentration of acetaminophen, ascorbic acid and phenylephrine was determined in pharmaceutical samples. In this way we were able to determine by analyzing the final color of the reactions. The linear range in ppm for all the analytes was obtained as 0.00–50.00 and limit of detection of 2.52, 2.48 and 2.48 were obtained for acetaminophen, ascorbic acid and phenylephrine, respectively. Percent relative standard deviation (RSD %) and percent relative error (RE %) of the proposed method for analysis of real samples of acetaminophen tablet were 4.77 and 7.64, ascorbic acid tablet, 4.73 and 1.41, and phenylephrine syrup, 2.44 and − 1.61, respectively. [ABSTRACT FROM AUTHOR]

A novel green fabrication approach is being proposed based on radiation assisted modification of Whatman filter paper 1 (WFP) for development of Acid Free-Paper based Analytical Devices (A f -PADs). A f -PADs exude immense potential as handy tools for on-site detection of toxic pollutants such as, Cr(VI), boron, etc., which have established detection protocols involving acid mediated colorimetric reactions that necessitate external acid addition. The proposed A f -PAD fabrication protocol asserts its novelty through elimination of external acid addition step, making the detection process safer and simpler. To achieve this, poly(acrylic acid) (PAA) was grafted onto WFP via a single step, room temperature process of gamma radiation induced simultaneous irradiation grafting, introducing acidic -COOH groups in the paper thereon. Grafting parameters namely, absorbed dose and concentrations of monomer, homopolymer inhibitor and acid were optimized. The -COOH groups incorporated in PAA-grafted-WFP (PAA- g -WFP) provide localized acidic conditions for colorimetric reactions between pollutants and their sensing agents, anchored on the PAA- g -WFP. A f -PADs loaded with 1,5-diphenylcarbazide (DPC) have been ably demonstrated for visual detection and quantitative estimation of Cr(VI) in water samples using RGB image analysis, with LOD value of 1.2 mg.L−1 and a measurement range comparable to that of commercially available PADs based Cr(VI) visual detection kits. [Display omitted] [ABSTRACT FROM AUTHOR]

This article introduces a novel approach by coupling paper points with hollow fiber membrane for electroextraction (PP–HF-EE). The method was innovatively applied to extract methylene blue (MB) from large water volumes (up to 580 mL). A comprehensive study of six key parameters – organic filter, acceptor and donor phase composition, extraction time, applied voltage, and sample volume – was conducted using conventional flatbed scanning and digital image analysis. Our results revealed that extraction performance was primarily influenced by time, with low voltages (50 V) and low-conductivity organic filters (1-decanol) yielding comparable results to higher settings (300 V or 1-pentanol). Under optimized conditions (50 V, 60 min, 1-decanol as the organic filter), analytical performance parameters were assessed, demonstrating acceptable precision (RSD <18% for intra- and inter-day measurements) within a linear range of 5–100 μg L−1 (r = 0.98). PP–HF–EE demonstrated reliability through stable and reproducible electric current measurements during all extraction studies. Utilizing an extremely cost-effective detection system, PP–HF–EE achieved detection limits in the low ppb range, highlighting its potential as a promising variation of electromembrane extraction for environmental sample analysis. [Display omitted] • A new electroextraction approach for environmental samples was presented. • Paper point after extraction was subsequently examined by digital image analysis. • Quantification of methylene blue in tap water was achieved in the low ppb range. • The new electroextraction approach might be promising for environmental analysis. [ABSTRACT FROM AUTHOR]

This article analyses Italian illustrated editions of Little Women published between 1908 and 1945. After an overview of the publishing history of the novel in Italy, the paper will examine Italian illustrations as hermeneutic tools in order to examine their ideological function in the representation of gender. The belated reception of Alcott's novel in Italy, caused by its representation of a nonconformist educational model for girls, is mirrored in the tension between the nationalistic drive and the influence of foreign models that can be pinpointed in the illustrated editions. By analyzing adaptations of American illustrations, original Italian creations, and omissions of iconic illustrations, this paper will unveil how much the visual element is embedded in the Italian historical and cultural context, as the choice (or omission) of particular images impacted the interpretation of the book. By examining the Italian editions of Little Women published in this time frame, we will also be able to retrace the links with the American illustrated history of the book and bear testimony to the powerful impact of the 1934 film version on printed editions. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based analytical devices (μPADs) have been extensively proposed as ideal tools for point-of-care (POC) testing with minimal user training and technical requirements. However, most μPADs use dried bioreagents, which complicate production, reduce device reproducibility and stability, and require transport and storage under temperature and humidity-controlled conditions. In this work, we propose a μPAD produced using an affordable craft-cutter and stored at room temperature, which is used to partially automate a single-step colorimetric magneto-immunoassay. As a proof-of-concept, the μPAD has been applied to the quantitative detection of Plasmodium falciparum lactate dehydrogenase (Pf-LDH), a biomarker of malaria infection. In this system, detection is based on a single-step magneto-immunoassay that consists of a single 5-min incubation of the lysed blood sample with immuno-modified magnetic beads (MB), detection antibody, and an enzymatic signal amplifier (Poly-HRP). This mixture is then transferred to a single-piece paper device where, after on-chip MB magnetic concentration and washing, signal generation is achieved by adding a chromogenic enzyme substrate. The colorimetric readout is achieved by the naked eye or using a smartphone camera and free software for image analysis. This μPAD afforded quantitative Pf-LDH detection in <15 min, with a detection limit of 6.25 ng mL−1 when the result was interpreted by the naked eye and 1.4 ng mL−1 when analysed using the smartphone imaging system. Moreover, the study of a battery of clinical samples revealed concentrations of Pf-LDH that correlated with those provided by the reference ELISA and with better sensitivity than a commercial rapid diagnostic test (RDT). These results demonstrate that magneto-immunoassays can be partly automated by employing a μPAD, achieving a level of handling that approaches the requirements of POC testing. [ABSTRACT FROM AUTHOR]

Cyanide anion can be found in foodstuffs, tobacco smoke and a variety of types of waters, mainly originating from anthropogenic activities. Due to its highly toxic nature, several agencies have established limits for cyanide levels in water. Additionally, monitoring cyanide levels in biological samples, such as blood and urine, is crucial for obtaining clinical information about the health condition of patients. Therefore, there is a pressing need for the development of simple, cost-effective, and reliable analytical methods capable of quantifying cyanide at low concentrations. This study presents a novel analytical method for the selective and sensitive determination of cyanide based on analyte volatilization, pre-concentration via single-drop microextraction (SDME) using a selective reagent, and colorimetric quantification using a paper-based analytical device. For this, 10 mL of a liquid sample was acidified with phosphoric acid and the generated HCN was collected using a single drop of 3 μL of a palladium dimethylglyoximate solution (Pd (DMG) 2 2−) positioned in the flask headspace using a syringe. The reaction of Pd (DMG) 2 2− leads to the formation of Pd(CN) 4 2− and the demasking of the organic ligand. After 15 min of extraction time, the reagent drop was added to a paper-based analytical device that has been previously impregnated with 3 μL of nickel chloride, resulting in the formation of a red precipitate of nickel (II) dimethylglyoximate. Digital images of the paper-based device were captured and the red channel (R) was used for quantification purposes. Under optimized conditions, the method demonstrates a suitable linear relation (r2 > 0.99) ranging from 26 to 286 μg L−1 and a limit of detection of 5 μg L−1. As a proof of concept, cyanide levels were quantified in water and urine samples using this method. The proposed approach offers high sensitivity and selectivity while requiring only a small volume of reagents. Furthermore, it exhibits a high degree of portability for in-situ applications. A selective and sensitive method for the determination of cyanide using HS-SDEM. [Display omitted] • Cyanide in liquid samples was determined by combining HS-SDME and digital image analysis. • Colorimetric reaction is performed on a microfluidic paper-based analytical device. • The reagent is easy to prepare and highly selective for cyanide. • Under optimized conditions, a limit of detection of 5 μg L-1 was achieved. • The point-of-care device is suitable for cyanide determination in water and urine samples. [ABSTRACT FROM AUTHOR]

Wearable eye‐tracking technology is an essential component in AR/VR devices. Conventional Pupil‐Corneal Reflection (PCR) eye‐tracking technology often presents challenges due to high costs and bulky designs, particularly for compact AR and smart glasses. To overcome these limitations, our next‐generation eyetracking technology relies on AI image analysis, significantly reducing the number of required IR LEDs and image sensors. This simplification enhances flexibility and lowers production costs while maintaining accuracy. Our breakthrough solution makes eye tracking more accessible for the masses in AR/VR devices in the new era of spatial computing and the metaverse. [ABSTRACT FROM AUTHOR]