Pittsburg State University

Packaging composed of polysaccharides has emerged as an alternative to petroleum-based commercial products. Thus, this research aimed to develop, optimize, characterize, and apply biodegradable pectin/starch-based films for fresh pears. All film-forming formulations were characterized. Digital images and optical properties suggest stable, transparent, and efficient ultraviolet (UV) blocking films, especially UVC-UVB and partially UVA. Thermal analysis indicated significant mass loss at high temperatures (above 250 °C). The films were permeable to water vapor (0.305-0.255 g × mm/h × m² × kPa) and impermeable to oil. Scanning electron microscopy revealed surface and cross-sectional changes in the material that influenced its mechanical properties, including tensile strength (0.029-0.041 MPa), Young's modulus (1.42-2.21 MPa), and elongation at break (1.69-2.98%). The films showed a water solubility of around 74% (w/w) with a maximum swelling of 233%. In addition, statistical tools facilitated optimization and data interpretation. The Simplex lattice mixture design indicated that the film containing 100% pectin (Pec-100) was more resistant and less permeable to water vapor, while principal component analysis attributed high transparency and rigidity. In this sense, the Pec-100 material showed potential for film application on fresh pears. Digital images recorded during storage suggested that coated fruits appeared fresh after 15 days. On the other hand, uncoated pears displayed degradation points starting from the tenth day. Therefore, the renewable-source-based film demonstrated promising fruit preservation results.

Frequent Ebola outbreaks on an unprecedented scale in resource-limited countries have resulted in higher fatality rates for the human population. Thereby, the development of a biosensor platform that can be used for point-of-care (PoC) tests and simultaneously features high sensitivity and selectivity is urgently needed. Herein, an approach for formulating multifunctional nanocomposite materials, plasmonic nanoceria (PNC), is presented, and its application as a sensing platform for the detection of Ebolavirus glycoprotein (EGP) of the Zaire strain is demonstrated. The synthetic strategy for PNC allows optical tunability, a unique approach to amplify detection sensitivity introduced by encapsulating gold nanoparticles (GNPs) within the polymeric coatings of cerium oxide nanoparticles (NC). Through altered optical characteristics of GNPs within the PNC, which include changes in localized surface plasmon resonance (SPR), higher detection sensitivity is achieved. Following surface conjugation of PNC with EGP-specific antibodies, a quantitative detection limit as low as 10 pM (0.7 ng/mL) is achieved. Moreover, antibody-functionalized PNC exhibits faster, reproducible, and highly sensitive colorimetric readouts, with a detectable SPR shift in the presence of EGP. Importantly, the limit of detection of EGP evaluated in complex sample matrices was comparable to as attained in a simple buffer. Specificity studies suggest that the developed PNC nanoplatform allows for both detection and differentiation between Ebola virus subtypes. Overall, the formulated PNC holds great potential for the rapid, ultrasensitive, and on-site detection of biomarker EGP of the Zaire strain and can be customized for the detection of other pathogens.

The function of advanced characterization methods in materials science is discussed in this review article, which primarily focuses on how these methods are used in structural composites, energy storage materials, and semiconductors.Discovering the intricate chem. and structural features of these materials has been greatly aided by the combination of methods like SEM (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Fourier-transform IR spectroscopy (FTIR).Understanding materials′ thermal stability and behavior under operating settings is essential for maximizing performance.Technol. such as thermogravimetric anal. (TGA) and differential scanning calorimetry (DSC) have further contributed to this understanding.A key area where in situ and operando characterization techniques are becoming increasingly important is the study of energy storage devices.Researchers can gain invaluable insights into degradation mechanisms and failure points by monitoring real-time material changes under working conditions.The interaction of a material′s thermal, chem., and structural characteristics can be better understood when spectroscopy and microscopy are used together.This study focuses on a significant material characterization trend involving combining high-throughput screening with machine learning (ML) and artificial intelligence (AI).AI can shorten the time it takes from ideation to implementation by improving the accuracy and speed of material discovery using massive data sets produced by sophisticated methods.Researchers can better anticipate material behavior using this data-driven method, particularly in operational or severe situations that are hard to replicate exptl.Researchers in the field of materials science have concluded that improving characterization methods is crucial to the field′s future progress.Critical will be multidimensional and hybrid approaches that integrate several types of anal.In addition, studying and forecasting the behavior of next-gen materials, especially those used in energy storage, semiconductor technol., and nanocomposites, will rely heavily on AI-driven research.New opportunities for creating sustainable, high-functioning materials will arise because of these advancements.