Nanostructured Materials Enabled Biomedical and Environmental Applications

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Synthesis, Interfaces and Nanostructures".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 2410

Special Issue Editor


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Guest Editor
Department of Analytical Chemistry, Reference Materials, Bundesanstalt für Materialforschung und -prüfung (BAM), Richard-Willstätter-Straße 11, 12489 Berlin, Germany
Interests: nanomaterials; synthesis; biofunctionalization; electrochemical biosensors; optical biosensors; additive manufacturing; inkjet printing
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Special Issue Information

Dear Colleagues,

In recent years, the applications of multifunctional nanostructured materials (i.e., nanoparticles, nanotubes, nanocomposites, nanopolymers, quantum dots) have attracted many researchers in different fields, in particular environmental science, human health, and nanomedicine.

This Special Issue of Nanomaterials, entitled “Nanostructured Materials Enabled Biomedical and Environmental Applications”, aims to collect new studies that highlight synthesis, modification, properties, and applications in the biomedical and environmental fields areas related to functional nanosystems and nanomaterials.

The biomedical applications of nanomaterials include but are not limited to bioimaging, biosensors, antioxidant, antibacterial, and antimicrobial characteristics, thermal therapy, cancer therapy, drug delivery, targeting, etc.

The environmental applications of nanomaterials include but are not limited to environmental detection, waste remediation, air treatment, soil treatment, water and air pollutants monitoring, CO2 capture and conversion, heavy-metal extraction, etc.

In this Special Issue, we aim to provide a comprehensive overview of original research articles, reviews, and communications that focus on the synthesis of nanomaterials and their applications in biomedicine and the environment. We look forward to receiving your submissions.

Dr. Chandan Singh
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • nanomaterials
  • biosensors
  • bioimaging
  • drug delivery
  • electrochemical sensors
  • optical detection
  • nanofilters
  • wastewater treatment
  • environmental toxins

Published Papers (2 papers)

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Research

16 pages, 12754 KiB  
Article
Synthesis of Ni-Doped Tremolite Fibers to Help Clarify the Aetiology of the Cytotoxic Outcome of Asbestos
by Andrea Bloise, Eugenia Giorno, Domenico Miriello and Nicolas Godbert
Nanomaterials 2023, 13(8), 1303; https://doi.org/10.3390/nano13081303 - 7 Apr 2023
Viewed by 1178
Abstract
Asbestos fibers act as complex crystal-chemical reservoirs susceptible of releasing potentially toxic elements (such as ions impurities) into the lung cellular environment during permanency and dissolution. To comprehend the exact pathological mechanisms that are triggered upon inhalation of asbestos fibers, in vitro studies [...] Read more.
Asbestos fibers act as complex crystal-chemical reservoirs susceptible of releasing potentially toxic elements (such as ions impurities) into the lung cellular environment during permanency and dissolution. To comprehend the exact pathological mechanisms that are triggered upon inhalation of asbestos fibers, in vitro studies on possible interactions between the mineral and the biological system have been carried out mostly by using natural asbestos. However, this latter comprises intrinsic impurities such as Fe2+/Fe3+ and Ni2+ ions, and other eventual traces of metallic pathogens. Furthermore, often, natural asbestos is characterized by the co-presence of several mineral phases, fiber dimensions of which are randomly distributed in width and in length. For these reasons, it is albeit challenging to precisely identify toxicity factors and to define the accurate role of each factor in the overall pathogenesis of asbestos. In this regard, the availability of synthetic asbestos fibers with accurate chemical composition and specific dimensions for in vitro screening tests would represent the perfect tool to correlate asbestos toxicity to its chemico-physical features. Herein, to palliate such drawbacks of natural asbestos, well-defined Ni-doped tremolite fibers were chemically synthesized in order to offer biologists adequate samples for testing the specific role of Ni2+ in asbestos toxicity. The experimental conditions (temperature, pressure, reaction time and water amount) were optimized to produce batches of asbestos fibers of the tremolite phase, with uniformly distributed shape and dimensions and a controlled content of Ni2+ metal ions. Full article
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17 pages, 1174 KiB  
Article
Machine Learning to Predict the Adsorption Capacity of Microplastics
by Gonzalo Astray, Anton Soria-Lopez, Enrique Barreiro, Juan Carlos Mejuto and Antonio Cid-Samamed
Nanomaterials 2023, 13(6), 1061; https://doi.org/10.3390/nano13061061 - 15 Mar 2023
Cited by 12 | Viewed by 2895
Abstract
Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics [...] Read more.
Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log Kd) using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics. Full article
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