Search Results (82)

Electrospun fibers serve as a medium for the targeted release of active compounds, facilitating the desired therapeutic effects in drug administration. The point of this study was to find the best conditions for making electrospun fibers from cellulose acetate (CA) and poly(ε-caprolactone) (PCL), mixed with pure oxyresveratrol extract from Artrocarpus lakoocha Roxberg (Moraceae). Additionally, the study focused on evaluating the antioxidant properties, antityrosinase activity, and freeze–thaw stability of the resulting fibers. We incorporated a concentration of oxyresveratrol at 0.1% w/w into various mass ratios of CA/PCL blended fiber sheets (1:0, 3:1, 1:1, 1:3), utilizing mixed solvents of acetone/DMF (2:1% v/v) and chloroform/DMF (9:1% v/v) for preparation. The fiber sheets displayed a continuous and uniform structure, with fiber diameters ranging from 300 to 1000 nanometers. We investigated the release kinetics of oxyresveratrol from the fibrous substrates using the total immersion technique, specifically in phosphate-buffered saline at a pH of 7.4. The results showed that the fiber sheet with a 3:1 w/w ratio of CA to PCL and a 0.1 w/w loading of oxyresveratrol showed the most significant release of oxyresveratrol at the 2 h mark, and it continued to release consistently at this peak value for up to 24 h. The antioxidant and anti-tyrosinase properties of oxyresveratrol in fiber sheets were more stable than those of free oxyresveratrol at the same concentrations. The fiber sheet presents a promising avenue for a user-friendly transdermal patch application. Full article

The aim of this study is to obtain poly(lactic acid) polymeric nanocomposites and carbon nanotubes for application in drone propellers produced through 3D printing. In this work, a filament based on poly(lactic acid)—PLA/functionalized carbon nanotube (CNT) composites was prepared for the fused deposition modeling (FDM) process. The effects of CNT content (0.2–1.0%), temperature variation, and extruder screw rotation variation were applied in the Design of Experiments (DOE) tool, where the main factors contributing to filament quality, focusing on mechanical strength, were identified. Through this tool, an optimum point for the material’s mechanical strength was reached, showing a value of 48.87 MPa, 43.17% above the initial value of 27.77 MPa. The response surface curve revealed a region where new filaments with similar mechanical strength values to those found in this work could be obtained. The results demonstrate that CNT content, extruder screw rotation, and extruder temperature directly influence filament quality. The data obtained from Thermogravimetry (TG) and Derivative Thermogravimetry (DTG) curves show that the addition of 0.6% CNT by weight does not significantly modify PLA degradation resistance, despite slight differences in temperatures. The main reason for these alterations is the dispersion of CNTs in the PLA matrix and CNT agglomeration. Through the demonstrated simulation, it is possible to confirm the application of the developed material in drone propeller manufacturing, facilitating access and providing new opportunities for users. Full article

The Internet of Things (IoT) has become an integral part of today’s smart and digitally connected world. IoT devices and technologies now connect almost every aspect of daily life, generating, storing, and analysing vast amounts of data. One important use of IoT is in utility management, where essential services such as water are supplied through IoT-enabled infrastructure to ensure fair, efficient, and sustainable delivery. The large volumes of data produced by water distribution networks must be safeguarded against manipulation, theft, and other malicious activities. Incidents such as the Queensland user data breach (2020–21), the Oldsmar water treatment plant attack (2021), and the Texas water system overflow (2024) show that attacks on water treatment plants, distribution networks, and supply infrastructure are common in Australia and worldwide, often due to inadequate security measures and limited technical resources. Lightweight cryptographic algorithms are particularly valuable in this context, as they are well-suited for resource-constrained hardware commonly used in IoT systems. This study focuses on the in-house developed ChaosFortress lightweight cryptographic algorithm, comparing its performance with other widely used lightweight cryptographic algorithms. The evaluation and comparative testing used an Arduino and a LoRa-based transmitter/receiver pair, along with the NIST Statistical Test Suite (STS). These tests assessed the performance of ChaosFortress against popular lightweight cryptographic algorithms, including ACORN, Ascon, ChaChaPoly, Speck, tinyAES, and tinyECC. ChaosFortress was equal in performance to the other algorithms in overall memory management but outperformed five of the six in execution speed. ChaosFortress achieved the quickest transmission time and topped the NIST STS results, highlighting its strong suitability for IoT applications. Full article

Detecting volatile organic compounds (VOCs) is essential for health, environmental protection, and industrial safety. VOCs contribute to air pollution, pose health risks, and can indicate leaks or contamination in industries. Applications include air quality monitoring, disease diagnosis, and food safety. This paper focuses on polymer-based hybrid sensor arrays (HSAs) utilizing interdigitated electrode (IDE) geometries for VOC detection. Achieving high selectivity and sensitivity in gas sensing remains a challenge, particularly in complex environments. To address this, we propose HSAs as an innovative solution to enhance sensor performance. IDE-based sensors are designed and fabricated using the Polysilicon Multi-User MEMS process (PolyMUMPs). Experimental evaluations are performed by exposing sensors to VOCs under controlled conditions. Traditional multi-sensor arrays (MSAs) achieve 82% prediction accuracy, while virtual sensor arrays (VSAs) leveraging frequency dependence improve performance: PMMA-VSA and PVP-VSA predict compounds with 100% and 98% accuracy, respectively. The proposed HSA, integrating these VSAs, consistently achieves 100% accuracy in compound identification and concentration estimation, surpassing MSA and VSA performance. These findings demonstrate that proposed polymer-based HSAs and VSAs, particularly with advanced IDE geometries, significantly enhance selectivity and sensitivity, advancing e-Nose technology for more accurate and reliable VOC detection across diverse applications. Full article

To address the environmental hazards posed by oil spills and the limitations of conventional hydrocarbon monitoring techniques, a cost-effective and user-friendly gas sensor system was developed for the real-time detection and quantification of hydrocarbon contaminants in soil. This system utilizes carbon black (CB)-filled poly(methyl methacrylate) (PMMA) and poly(vinyl chloride) (PVC) nanocomposites to create chemoresistive sensors. The CB-PMMA and CB-PVC composites were synthesized and deposited as thin films onto interdigitated electrodes, with their morphologies characterized using scanning electron microscopy. The composites, optimized at a composition of 10% w/w CB and 90% w/w polymer, exhibited a sensitive response to hydrocarbon vapors across a tested range from C₂₀ (99 ppmV) to C₈ (8750 ppmV). The sensor’s response mechanism is primarily attributed to the swelling-induced resistance change of the amorphous polymer matrix in hydrocarbon vapors. These findings demonstrate the potential use of CB–polymer composites as field-deployable gas sensors, providing a rapid and efficient alternative to traditional gas chromatography methods for monitoring soil remediation efforts and mitigating the environmental impact of oil contamination. Full article

PolyJet™ 3D printing is an additive manufacturing (AM) technology from Stratasys^TM. It has been used for applications such as tissue mimicking, printing anatomical models, and surgical planning. The materials available from Stratasys^TM have the inherent capabilities of producing a number of PolyJet™ materials with a range of physical properties that can be utilized for representing realistic tissue behavior mechanically. The preset materials available in the PolyJet™ printing software version 1.92.17.44384 GrabCAD^TM Print allow the user to manufacture materials similar to biological tissue, but the combinations of possibilities are limited and might not represent the broad spectrum of all tissue types. The purpose of this study was to determine the combination of PolyJet™ materials that most accurately mimicked a particular biological tissue mechanically. A detailed Design of Experiment (DOE) methodology was used to determine the combination of material mixtures and printing parameters and to analyze their mechanical properties that best matched the biological tissue properties available in the literature of approximately 50 different tissue types. Uniaxial tensile testing was performed according to the ASTM standard D638-14 of samples printed from Stratasys J850 digital anatomy printer to their determined stress–strain properties. The obtained values were subsequently validated by comparing them with the corresponding mechanical properties of biological tissues available in the literature. The resulting model, developed using the DOE approach, successfully produced artificial tissue analogs that span a wide range of mechanical characteristics, from tough, load-bearing tissues to soft, compliant tissues. The validation confirmed the effectiveness of the model in replicating the diverse mechanical behavior of various human tissues. Overall, this paper provides a detailed methodology of how materials and settings were chosen in GrabCAD^TM Print software and Digital Anatomy Creator^TM (DAC) to achieve an accurate artificial tissue material. Full article

Dysfunctional mitochondria are present in many neurodegenerative diseases, such as spinocerebellar ataxia type 3 (SCA3), also known as Machado–Joseph disease (MJD). SCA3/MJD, the most frequent neurodegenerative ataxia worldwide, is caused by the abnormal expansion of the polyglutamine tract (polyQ) at ataxin-3. This protein is known to deubiquitinate key proteins such as Parkin, which is required for mitophagy. Ataxin-3 also interacts with Beclin1 (essential for initiating autophagosome formation adjacent to mitochondria), as well as with the mitochondrial cristae protein TBK1. To identify other proteins of the mitophagy pathway (according to the KEGG database) that can interact with ataxin-3, here we developed a pipeline for in silico analyses of protein–protein interactions (PPIs), called auto-p2docking. Containerized in Docker, auto-p2docking ensures reproducibility and reduces the number of errors through its simplified configuration. Its architecture consists of 22 modules, here used to develop 12 protocols but that can be specified according to user needs. In this work, we identify 45 mitophagy proteins as putative ataxin-3 interactors (53% are novel), using ataxin-3 interacting regions for validation. Furthermore, we predict that ataxin-3 interactors from both Parkin-independent and -dependent mechanisms are affected by the polyQ expansion. Full article

The increasing adoption of distributed recycling via additive manufacturing (DRAM) has facilitated the revalorization of materials derived from waste streams for additive manufacturing. Recycled materials frequently contain impurities and mixed polymers, which can degrade their properties over multiple cycles. This degradation, particularly in rheological properties, limits their applicability in 3D printing. Consequently, there is a critical need for a tool that enables the rapid assessment of the flowability of these recycled materials. This study presents the design, development, and manufacturing of an open-source melt flow index (MFI) apparatus. The open-source MFI was validated with tests on virgin polylactic acid pellets, shredded recycled poly(ethylene) terephthalate glycol flakes, and high-density polyethylene/poly(ethylene) terephthalate blends to demonstrate the range of polymer types and recyclability. The proposed MFI tool offers a user-friendly and cost-effective solution for evaluating the flow properties of materials from waste streams, thereby enhancing their viability for additive manufacturing applications. Full article

Maintaining user privacy in machine learning is a critical concern due to the implications of data collection. Federated learning (FL) has emerged as a promising solution by sharing trained models rather than user data. However, FL still faces several challenges, particularly in terms of security and privacy, such as vulnerability to inference attacks. There is an inherent trade-off between communication traffic across the network and computational costs on the server or client, which this paper aims to address by maneuvering between these performance parameters. To tackle these issues, this paper proposes two complementary frameworks: PolyFLAM (“Polymorphic Federated Learning Aggregation of Models”) and PolyFLAP (“Polymorphic Federated Learning Aggregation of Parameters”). These frameworks provide two options to suit the needs of users, depending on whether they prioritize reducing communication across the network or lowering computational costs on the server or client. PolyFLAM reduces computational costs by exchanging entire models, eliminating the need to rebuild models from parameters. In contrast, PolyFLAP reduces communication costs by transmitting only model parameters, which are smaller in size compared to entire models. Both frameworks are supported by polymorphic encryption, ensuring privacy is maintained even in cases of key leakage. Furthermore, these frameworks offer five different machine learning models, including support vector machines, logistic regression, Gaussian naïve Bayes, stochastic gradient descent, and multi-layer perceptron, to cover as many real-life problems as possible. The evaluation of these frameworks with simulated and real-life datasets demonstrated that they can effectively withstand various attacks, including inference attacks that aim to compromise user privacy by capturing exchanged models or parameters. Full article

Cystic fibrosis is caused by biallelic pathogenic variants in the CFTR gene, which contains a polymorphic (TG)_mT_n sequence (the “poly-T/TG tract”) in intron 9. While T₉ and T₇ alleles are benign, T₅ alleles with longer TG repeats, e.g., (TG)₁₂T₅ and (TG)₁₃T₅, are clinically significant. Thus, professional medical societies currently recommend reporting the TG repeat size when T₅ is detected. Sanger sequencing is a cost-effective method of genotyping the (TG)_mT_n tract; however, its polymorphic length substantially complicates data analysis. We developed CFTR-TIPS, a freely available web-based software tool that infers the (TG)_mT_n genotype from Sanger sequencing data. This tool detects the (TG)_mT_n tract in the chromatograms, quantifies goodness of fit with expected patterns, and visualizes the results in a graphical user interface. It is broadly compatible with any Sanger chromatogram that contains the (TG)_mT_n tract ± 15 bp. We evaluated CFTR-TIPS using 835 clinical samples previously analyzed in a CLIA-certified, CAP-accredited laboratory. When operated fully automatically, CFTR-TIPS achieved 99.8% concordance with our clinically validated manual workflow, while generally taking less than 10 s per sample. There were two discordant samples: one due to a co-occurring heterozygous duplication that confounded the tool and the other due to incomplete (TG)_mT_n tract detection in the reverse chromatogram. No clinically significant misclassifications were observed. CFTR-TIPS is a free, accurate, and rapid tool for CFTR (TG)_mT_n tract genotyping using cost-effective Sanger sequencing. This tool is suitable both for automated use and as an aid to manual review to enhance accuracy and reduce analysis time. Full article

Synthetic cannabinoids are a rapidly evolving, diverse class of new psychoactive substances. Synthetic cannabinoid use results in a higher likelihood of adverse events and hospitalization when compared to cannabis use. The mechanisms behind synthetic cannabinoid toxicity remain elusive. Furthermore, poly-substance use may be a significant contributing factor in many cases. This scoping review aimed to identify the key characteristics of synthetic cannabinoid co-exposure cases and discuss the potential implications of poly-substance use in humans. There were 278 human cases involving 64 different synthetic cannabinoids extracted from the databases. Cases involved a total of 840 individual co-exposures, with an average of four substances involved in each case. The most common co-exposures were alcohol (11.4%), opioids (11.2%), and cannabis (11.1%). When analyzed by case outcome, co-exposure to either antipsychotics/antidepressants, alcohol, or tobacco were significantly associated with mortality as an outcome (p < 0.05). Drug-use history (63.4%), mental illness (23.7%), and hypertensive and atherosclerotic cardiovascular disease (20.1%) were prevalent patient histories in the case cohort. There are several potential pharmacodynamic and pharmacokinetic interactions between co-exposure drugs and synthetic cannabinoids that could worsen clinical presentation and toxicity in synthetic cannabinoid users. Individuals with substance-use disorders or psychiatric illness would be especially vulnerable to these multi-drug interactions. Further research into these complex exposures is needed for the successful prevention and treatment of synthetic cannabinoid-related harms. Full article

Polyamide-6 (PA) is a popular textile polymer having desirable mechanical and thermal properties, chemical stability, and biocompatibility. However, PA nanofibers are prone to bacterial growth and user discomfort. ε-Poly-L-lysine (PL) is non-toxic, antimicrobial, and hydrophilic but lacks spinnability due to its low molecular weight. Given its similar backbone structure to PA, with an additional amino side chain, PL was integrated with PA to develop multifunctional nanofibers. This study explores a simple, scalable method by which to obtain PL nanofibers by utilizing the structurally similar PA as the base. The goal was to enhance the functionality of PA by addressing its drawbacks. The study demonstrates spinnability of varying concentrations of PL with base PA while exploring compositions with higher PL concentrations than previously reported. Electrospinning parameters were studied to optimize the nanofiber properties. The effects of PL addition on morphology, hydrophilicity, thermal stability, mechanical performance, and long-term antimicrobial activity of nanofibers were evaluated. The maximum spinnable concentration of PL in PA-based nanofibers resulted in super hydrophilicity (0° static water contact angle within 10 s), increased tensile strength (1.02 MPa from 0.36 MPa of control), and efficient antimicrobial properties with long-term stability. These enhanced characteristics hold promise for the composite nanofiber’s application in medical and protective textiles. Full article

Poly(ethylene terephthalate) (PET) is a thermoplastic polyester with excellent thermal and mechanical properties, widely used in a variety of industrial fields. It is a semicrystalline polymer, and most of the industrial success of PET derives from its easily tunable crystallization kinetics, which allow users to produce the polymer with a high crystal fraction for applications that demand high thermomechanical resistance and barrier properties, or a fully amorphous polymer when high transparency of the product is needed. The main properties of the polymer are presented and discussed in this contribution, together with the literature data on the crystal structure and morphology of PET. This is followed by an in-depth analysis of its crystallization kinetics, including both primary crystal nucleation and crystal growth, as well as secondary crystallization. The effect of molar mass, catalyst residues, chain composition, and thermo-mechanical treatments on the crystallization kinetics, structure, and morphology of PET are also reviewed in this contribution. Full article

In robot-assisted microsurgery (RAMS), surgeons often face the challenge of operating with minimal feedback, particularly lacking in haptic feedback. However, most traditional desktop haptic devices have restricted operational areas and limited dexterity. This report describes a novel, lightweight, and low-budget wearable haptic controller for teleoperated microsurgical robotic systems. We designed a wearable haptic interface entirely made using off-the-shelf material-PolyJet Photopolymer, fabricated using liquid and solid hybrid 3D co-printing technology. This interface was designed to resemble human soft tissues and can be wrapped around the fingertips, offering direct contact feedback to the operator. We also demonstrated that the device can be easily integrated with our motion tracking system for remote microsurgery. Two motion tracking methods, marker-based and marker-less, were compared in trajectory-tracking experiments at different depths to find the most effective motion tracking method for our RAMS system. The results indicate that within the 4 to 8 cm tracking range, the marker-based method achieved exceptional detection rates. Furthermore, the performance of three fusion algorithms was compared to establish the unscented Kalman filter as the most accurate and reliable. The effectiveness of the wearable haptic controller was evaluated through user studies focusing on the usefulness of haptic feedback. The results revealed that haptic feedback significantly enhances depth perception for operators during teleoperated RAMS. Full article

Negative features in microdevices find a wide range of applications. The process of 3D printing has revolutionized their fabrication due to the combination of good resolution and integration capability. Herein, we report on a systematic study of the effects of materials and print directions on the 3D printing of microfluidic channels as negative features under PolyJet technology. Specifically, the Statasys Objet500 printer was used for this study. We printed two sets of chips ($n=10$ each), each of which contains channel pairs of a high-contrast reference material and a sacrificial material, respectively. Both materials were embedded in a clear photopolymer resin. The channel pairs ranged in planned width from 64 to 992 μm. To explore the effect on print orientation, channels were printed either parallel or perpendicular with respect to the jetting head’s movement. The width of each channel of a pair was compared for each planned width and each combination of materials. The effect of print orientation on channel morphology was also investigated. We found that reproducibility and accuracy were highest at a planned channel width of approximately ≥600 μm and that channel morphology was most suitable when the jetting head of the printer moved parallel to the channel’s longitudinal axis. The results should be of interest to any users who wish to create negative features using PolyJet 3D technology. Full article