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Nanotechnology is a powerful tool for engineering functional materials that has the potential to transform textiles into high-performance, value-added products. In recent years, there has been considerable interest in the development of functional textiles using metal nanoparticles (MNPs). The incorporation of MNPs in textiles allows for the obtention of multifunctional properties, such as ultraviolet (UV) protection, self-cleaning, and electrical conductivity, as well as antimicrobial, antistatic, antiwrinkle, and flame retardant properties, without compromising the inherent characteristics of the textile. Environmental sustainability is also one of the main motivations in development and innovation in the textile industry. Thus, the synthesis of MNPs using ecofriendly sources, such as polysaccharides, is of high importance. The main functions of polysaccharides in these processes are the reduction and stabilization of MNPs, as well as the adhesion of MNPs onto fabrics. This review covers the major research attempts to obtain textiles with different functional properties using polysaccharides and MNPs. The main polysaccharides reported include chitosan, alginate, starch, cyclodextrins, and cellulose, with silver, zinc, copper, and titanium being the most explored MNPs. The potential applications of these functionalized textiles are also reported, and they include healthcare (wound dressing, drug release), protection (antimicrobial activity, UV protection, flame retardant), and environmental remediation (catalysts).

The textile industry’s reliance on synthetic dyes contributes significantly to pollution, highlighting the need for sustainable alternatives like biopigments. This study investigates the production and application of the biopigment prodigiosin, which was produced by Pseudomonas putida with a yield of 1.85 g/L. Prodigiosin was prepared under acidic, neutral, and alkaline conditions, resulting in varying protonation states that influenced its affinity for cotton and polyester fibers. Three surfactants (anionic, cationic, non-ionic) were tested, with non-ionic Tween 80 yielding a promising color strength (above 4) and fastness results with neutral prodigiosin at 1.3 g/L. Cotton and polyester demonstrated good washing (color difference up to 14 for cotton, 5 for polyester) and light fastness (up to 15 for cotton, 16 for polyester). Cellulose acetate, used in the conventional printing process as a thickener, produced superior color properties compared to commercial thickeners. Neutral prodigiosin achieved higher color strength, and cotton fabrics displayed halochromic properties, distinguishing them from polyester, which showed excellent fastness. Prodigiosin-printed samples also exhibited strong antimicrobial activity against Pseudomonas aeruginosa and retained halochromic properties over 10 pH cycles. These findings suggest prodigiosin as a sustainable dye alternative and pH sensor, with potential applications in biomedical materials, such as antimicrobial and pH-responsive wound dressings.

Aim Species distribution information is essential under increasing global changes, and models can be used to acquire such information but they can be affected by different errors/bias. Here, we evaluated the degree to which errors in species data (false presences–absences) affect model predictions and how this is reflected in commonly used evaluation metrics. Location Western Swiss Alps. Methods Using 100 virtual species and different sampling methods, we created observation datasets of different sizes (100–400–1,600) and added increasing levels of errors (creating false positives or negatives; from 0% to 50%). These degraded datasets were used to fit models using generalized linear model, random forest and boosted regression trees. Model fit (ability to reproduce calibration data) and predictive success (ability to predict the true distribution) were measured on probabilistic/binary outcomes using Kappa, TSS, MaxKappa, MaxTSS and Somers'D (rescaled AUC). Results The interpretation of models’ performance depended on the data and metrics used to evaluate them, with conclusions differing whether model fit, or predictive success were measured. Added errors reduced model performance, with effects expectedly decreasing as sample size increased. Model performance was more affected by false positives than by false negatives. Models with different techniques were differently affected by errors: models with high fit presenting lower predictive success (RFs), and vice versa (GLMs). High evaluation metrics could still be obtained with 30% error added, indicating that some metrics (Somers'D) might not be sensitive enough to detect data degradation. Main conclusions Our findings highlight the need to reconsider the interpretation scale of some commonly used evaluation metrics: Kappa seems more realistic than Somers'D/AUC or TSS. High fits were obtained with high levels of error added, showing that RF overfits the data. When collecting occurrence databases, it is advisory to reduce the rate of false positives (or increase sample sizes) rather than false negatives.

Poly(styrene-methyl methacrylate-acrylic acid) photonic crystals (PCs), with five different sizes (170, 190, 210, 230 and 250 nm), were applied onto three plain fabrics, namely polyamide, polyester and cotton. The PC-coated fabrics were analyzed using scanning electronic microscopy and two UV/Vis reflectance spectrophotometric techniques (integrating sphere and scatterometry) to evaluate the PCs’ self-assembly along with the obtained spectral and colors characteristics. Results showed that surface roughness of the fabrics had a major influence on the color produced by PCs. Polyamide-coated fabrics were the only samples having an iridescent effect, producing more vivid and brilliant colors than polyester and cotton samples. It was observed that as the angle of incident light increases, a hypsochromic shift in the reflection peak occurs along with the formation of new reflection peaks. Furthermore, color behavior simulations were performed with an illuminant A light source on polyamide samples. The illuminant A simulation showed greener and yellower structural colors than those illuminated with D50. The polyester and cotton samples were analyzed using scatterometry to check for iridescence, which was unseen upon ocular inspection and then proven to be present in these samples. This work allowed a better comprehension of how structural colors and their iridescence are affected by the textile substrate morphology and fiber type.

Various methods have been used to model the time-varying curves within the global positioning system (GPS) position time series. However, very few consider the level of noise a priori before the seasonal curves are estimated. This study is the first to consider the Wiener filter (WF), already used in geodesy to denoise gravity records, to model the seasonal signals in the GPS position time series. To model the time-varying part of the signal, a first-order autoregressive process is employed. The WF is then adapted to the noise level of the data to model only those time variabilities which are significant. Synthetic and real GPS data is used to demonstrate that this variation of the WF leaves the underlying noise properties intact and provides optimal modeling of seasonal signals. This methodology is referred to as the adaptive WF (AWF) and is both easy to implement and fast, due to the use of the fast Fourier transform method.

One of the most important measures implemented to reduce SARS-CoV-2 transmission has been the use of face masks. Yet, most mask options available in the market display a passive action against the virus, not actively compromising its viability. Here, we propose to overcome this limitation by incorporating antiviral essential oils (EOs) within polycaprolactone (PCL) electrospun fibrous mats to be used as intermediate layers in individual protection masks. Twenty EOs selected based on their antimicrobial nature were examined for the first time against the Escherichia coli MS2 virus (potential surrogate of SARS-CoV-2). The most effective were the lemongrass (LGO), Niaouli (NO) and eucalyptus (ELO) with a virucidal concentration (VC) of 356.0, 365.2 and 586.0 mg/mL, respectively. PCL was processed via electrospinning, generating uniform, beadless fibrous mats. EOs loading was accomplished via two ways: (1) physisorption on pre-existing mats (PCLaEOs), and (2) EOs blending with the polymer solution prior to fiber electrospinning (PCLbEOs). In both cases, 10% v/v VC was used as loading concentration, so the mats’ stickiness and overwhelming smell could be prevented. The EOs presence and release from the mats were confirmed by UV-visible spectroscopy (≈5257–631 µg) and gas chromatography-mass spectrometry evaluations (average of ≈14.3% EOs release over 4 h), respectively. PCLbEOs mats were considered the more mechanically and thermally resilient, with LGO promoting the strongest bonds with PCL (PCLbLGO). On the other hand, PCLaNO and PCLaELO were deemed the least cohesive combinations. Mats modified with the EOs were all identified as superhydrophobic, capable of preventing droplet penetration. Air and water-vapor permeabilities were affected by the mats’ porosity (PCL < PCLaEOs < PCLbEOs), exhibiting a similar tendency of increasing with the increase of porosity. Antimicrobial testing revealed the mats’ ability to retain the virus (preventing infiltration) and to inhibit its action (log reduction averaging 1). The most effective combination against the MS2 viral particles was the PCLbLGO. These mats’ scent was also regarded as the most pleasant during sensory evaluation. Overall, data demonstrated the potential of these EOs-loaded PCL fibrous mats to work as COVID-19 active barriers for individual protection masks.

Laboratories and industries that handle chemicals are ubiquitously prone to leakages. These may occur in storage rooms, cabinets or even in temporary locations, such as workbenches and shelves. A relevant number of these chemicals are corrosive, thus commercial products already exist to prevent material damage and injuries. One strategy consists of the use of absorbing mats, where few display neutralizing properties, and even less a controlled neutralization. Nevertheless, to the authors’ knowledge, the commercially available neutralizing mats are solely dedicated to neutralizing acid or alkali solutions, never both. Therefore, this work describes the development and proof of a completely novel concept, where a dual component active mat (DCAM) is able to perform a controlled simultaneous neutralization of acid and alkali leakages by using microencapsulated active components. Moreover, its active components comprise food-grade ingredients, embedded in nonwoven polypropylene. The acid neutralizing mats contain sodium carbonate (Na2CO3) encapsulated in sodium alginate microcapsules (MC-ASC). Alkali neutralizing mats possess commercial encapsulated citric acid in hydrogenated palm oil (MIRCAP CT 85-H). A DCAM encompasses both MC-ASC and MIRCAP CT 85-H and was able to neutralize solutions up to 10% (v/v) of hydrochloric acid (HCl) and sodium hydroxide (NaOH). The efficacy of the neutralization was assessed by direct titration and using pH strip measurement tests to simulate the leakages. Due to the complexity of neutralization efficacy evaluation based solely on pH value, a thorough conductivity study was performed. DCAM reduced the conductivity of HCl and NaOH (1% and 2% (v/v)) in over 70%. The composites were characterized by scanning electron microscopy (SEM), differential calorimetry (DSC) and thermogravimetric analysis (TGA). The size of MC-ASC microcapsules ranged from 2 μm to 8 μm. Finally, all mat components displayed thermal stability above 150 °C.

A key component necessary to improve the performance of climate and weather forecasting models is understanding the physical mechanisms controlling tropical deep convection. In this study, the thermodynamic variables linked to deep convection within this equatorial sea-breeze convective regime are analyzed. A range of data sets are employed: GNSS-based PWV and surface precipitation data, lightning and daily radiosonde observations, and GOES-13/16 and GPM satellite products. Our results indicate that the convective indices of CAPE and CIN, often used as predictors of deep convection, do not clearly distinguish deep-convective and non-convective days. In contrast, the variables representative of the atmospheric water vapor content, PWV and vertical water vapor distribution as well as an entrainment-based buoyancy measure, are better markers of potential deep convection. For this region, however, the water vapor/deep convection relationship with precipitation does not appear as strong as over tropical oceans and tropical continental regions. Finally, our results show that there is no strong link between daily average precipitation intensity and daily lightning count. However, deep-convective days without lightning had higher water vapor at the beginning of the day, as compared to days with lightning, which resulted in convective showers earlier in the day.

Aim: Modelling species distributions at the community level is required to make effective forecasts of global change impacts on diversity and ecosystem functioning. Community predictions may be achieved using macroecological properties of communities (macroecological models, MEM), or by stacking of individual species distribution models (stacked species distribution models, S-SDMs). To obtain more realistic predictions of species assemblages, the SESAM (spatially explicit species assemblage modelling) framework suggests applying successive filters to the initial species source pool, by combining different modelling approaches and rules. Here we provide a first test of this framework in mountain grassland communities. Location: The western Swiss Alps. Methods: Two implementations of the SESAM framework were tested: a probability ranking' rule based on species richness predictions and rough probabilities from SDMs, and a trait range' rule that uses the predicted upper and lower bound of community-level distribution of three different functional traits (vegetative height, specific leaf area, and seed mass) to constrain a pool of species from binary SDMs predictions. Results: We showed that all independent constraints contributed to reduce species richness overprediction. Only the probability ranking' rule allowed slight but significant improvements in the predictions of community composition. Main conclusions: We tested various implementations of the SESAM framework by integrating macroecological constraints into S-SDM predictions, and report one that is able to improve compositional predictions. We discuss possible improvements, such as further understanding the causality and precision of environmental predictors, using other assembly rules and testing other types of ecological or functional constraints.

We construct a first-order local model for Poisson manifolds around a large class of Poisson submanifolds and give conditions under which this model is a local normal form. The resulting linearization theorem includes as special cases all the known linearization theorems for fixed points and symplectic leaves. The symplectic groupoid version of these results gives a solution to the groupoid coisotropic embedding problem.