Most sheet facial masks for skincare are made of nonwovens and loaded with liquid active ingredients, which are usually opaque and require additives for long-term preservation. Herein, a Transparent Additive-Free Fibrous (TAFF) facial mask is reported for skin moisturizing. The TAFF facial mask consists of a bilayer fibrous membrane. The inner layer is fabricated by electrospinning functional components of gelatin (GE) and hyaluronic acid (HA) into a solid fibrous membrane to get rid of additives, the outer layer is an ultrathin PA6 fibrous membrane that is highly transparent, especially after absorbing water. The results indicate that the GE-HA membrane can quickly absorb water and become a transparent hydrogel film. By employing the hydrophobic PA6 membrane as the outer layer, directional water transport is achieved, which enables TAFF facial mask with excellent skin moisturizing effect. The skin moisture content is up to 84% ± 7% after placing the TAFF facial mask on the skin for 10 min. In addition, the relative transparency of the TAFF facial mask on the skin reaches 97.0% ± 1.9% when ultrathin PA6 membrane is used as the outer layer. The design of the transparent additive-free facial mask may serve as a guideline for developing new functional facial masks.
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http://dx.doi.org/10.1002/marc.202300180 | DOI Listing |
Sensors (Basel)
January 2025
School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
This review offers a comprehensive and in-depth analysis of face mask detection and recognition technologies, emphasizing their critical role in both public health and technological advancements. Existing detection methods are systematically categorized into three primary classes: feaRture-extraction-and-classification-based approaches, object-detection-models-based methods and multi-sensor-fusion-based methods. Through a detailed comparison, their respective workflows, strengths, limitations, and applicability across different contexts are examined.
View Article and Find Full Text PDFMedicina (Kaunas)
January 2025
Department of Orthodontics, Faculty of Dentistry, Ataturk University, 25030 Erzurum, Turkey.
: The aim of this prospective study was to assess the effects of rapid maxillary expansion (RME) and/or face mask (FM) treatments on the pharyngeal airway in patients with skeletal Class III malocclusion caused by maxillary deficiency. This study utilized cone beam computed tomography (CIBT) for a three-dimensional (3D) analysis of airway changes, comparing the results with those of a control group consisting of untreated skeletal Class III patients. : The study included 60 participants (34 boys, 26 girls) aged 9 to 14 years, all diagnosed with skeletal Class III malocclusion due to maxillary underdevelopment.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Anesthesiology, The Second Affiliated Hospital, The Army Military Medical University, Chongqing, China.
Background: Rapid sequence induction intubation (RSII) is commonly used in emergency surgeries for patients at high risk of aspiration. However, these patients are more susceptible to hypoxemia during the RSII process. High-flow nasal cannula (HFNC) oxygen therapy has emerged as a potential alternative to traditional face mask (FM) ventilation pre- and apneic oxygenation.
View Article and Find Full Text PDFActa Paediatr
January 2025
Department of Neonatology, University Children's Hospital of Tuebingen, Tuebingen, Germany.
Aim: Face masks and binasal prongs are commonly used interfaces for applying continuous positive airway pressure (CPAP) in neonatology. We aimed to assess CPAP stability in a randomised controlled in vitro study.
Methods: In a simulated resuscitation scenario of a 1000-g preterm infant with respiratory distress, 20 operators (10 with/without neonatology experience) aimed to maintain a CPAP of 5 cmHO as precisely as possible using face masks or binasal prongs in random order.
J Imaging
January 2025
Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
The increasing reliance on deep neural network-based object detection models in various applications has raised significant security concerns due to their vulnerability to adversarial attacks. In physical 3D environments, existing adversarial attacks that target object detection (3D-AE) face significant challenges. These attacks often require large and dispersed modifications to objects, making them easily noticeable and reducing their effectiveness in real-world scenarios.
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