Special Issue "Experimental Nanosciences, Computational Chemistry, and Data Analysis"

A special issue of Nanomaterials (ISSN 2079-4991).

Deadline for manuscript submissions: 30 November 2017

Special Issue Editors

Guest Editor
Prof. Dr. Humberto González-Díaz

1 Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Bizkaia, Leioa, Sarriena w/n, Spain
2 IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Bizkaia, Spain
Website1 | Website2 | E-Mail
Phone: +3494 601 3547
Fax: +3494 601 2748
Interests: computational chemistry, cheminformatics; data analysis; network science; computational materials and nanosciences
Guest Editor
Prof. Dr. Bakhtiyor Rasulev

North Dakota State University, Department of Coatings and Polymeric Materials, Fargo, USA
Website | E-Mail
Interests: cheminformatics; computational nanosciences; materials informatics
Guest Editor
Dr. Khader Shameer

Director of Bioinformatics, Data Science and Precision Medicine, Healthcare Integration Services, Philips, 2 Canal Park, Cambridge MA 02141, USA
Website | E-Mail
Interests: bioinformatics; oncology; cardiology; healthcare data science; digital health; drug discovery; precision medicine; nano-biotechnology
Guest Editor
Prof. Dr. Hrvoje Kušić

Faculty of Chemical Engineering and Technology, University of Zagreb, Zagreb, Croatia
Website | E-Mail
Interests: nanomaterials; nanotechnology; environmental chemistry; statistics

Special Issue Information

Dear Colleagues,

The huge amounts of data obtained by researches worldwide in the last few years has driven the development of new computational chemistry and data sciences methods for the analysis of nanomaterials.

In this sense, the present Special Issue focuses on experimental methods for the acquisition of data in nanotechnology, as well as on the development of computational methods for the posterior analysis of this data. Fields of interest include, but are not limited to, experimental methods for data acquisition on nanotechnology, nanotoxicology, chemistry of materials, along with chemometrics, computational chemistry, QSAR/QSPR models, data analysis, machine learning, database development, software design, and other computational methods for the analysis of recorded experimental data. Accepted papers will be published in the journal Nanomaterials, which is an open access publication journal of MDPI in the field of Molecular and Biomedical Sciences (http://www.mdpi.com/journal/nanomaterials). The Special Issue also includes full versions of proceedings published in MOL2NET International Conference Series on Multidisciplinary Sciences, 2016 (closed) and 2017 (open), with an official website on the SciForum platform: http://sciforum.net/conferences/mol2net.

Prof. Dr. Humberto González-Díaz
Prof. Bakhtiyor Rasulev
Dr. Khader Shameer
Prof. Hrvoje Kušić
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nanomaterials is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 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 characterization
  • experimental nanotoxicology
  • computational materials science
  • computational nanoscience
  • quantum-chemical study of new materials
  • quantum-chemical study of new catalysts
  • nano-QSAR models for biological activity of nanoparticles
  • nano-QSTR models for toxicity and ADME properties of nanoparticles
  • machine learning in cheminformatics and materials informatics
  • computational methods in risk assessment of nanomaterials
  • data mining in nanosciences

Published Papers (4 papers)

View options order results:
result details:
Displaying articles 1-4
Export citation of selected articles as:

Research

Open AccessArticle Stability and Synergistic Effect of Polyaniline/TiO2 Photocatalysts in Degradation of Azo Dye in Wastewater
Nanomaterials 2017, 7(12), 412; doi:10.3390/nano7120412
Received: 20 October 2017 / Revised: 16 November 2017 / Accepted: 17 November 2017 / Published: 23 November 2017
PDF Full-text (5021 KB) | HTML Full-text | XML Full-text
Abstract
The polyaniline/TiO2 (PANI/TiO2) composite photocatalysts were prepared by the in situ chemical oxidation of aniline (An) in the presence of TiO2 particles. For this purpose, photocatalysts with different amounts of PANI polymer were prepared and analysed. Fourier-transform infrared (FT-IR)
[...] Read more.
The polyaniline/TiO2 (PANI/TiO2) composite photocatalysts were prepared by the in situ chemical oxidation of aniline (An) in the presence of TiO2 particles. For this purpose, photocatalysts with different amounts of PANI polymer were prepared and analysed. Fourier-transform infrared (FT-IR) spectroscopy and thermogravimetric (TG) analysis indicated successful synthesis of the PANI polymer and its conductivity was also determined. The micrographs of field emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM) were used to explain the impact of the aniline amount on the aggregation process during the synthesis of the composites. The smallest size of aggregates was obtained for the photocatalysts with 15% of PANI (15PANI/TiO2) due to the formation of homogenous PANI. The photocatalytic activity of studied PANI/TiO2 photocatalysts was validated by monitoring the discoloration and mineralization of Reactive Red azo dye (RR45) in wastewater. The 15PANI/TiO2 sample presented the highest photocatalytic efficiency under ultraviolet A (UVA) irradiation, in comparison to pure TiO2. This was explained by the formation of uniformly dispersed PANI on the TiO2 particles, which was responsible for the synergistic PANI-TiO2 effect. Full article
(This article belongs to the Special Issue Experimental Nanosciences, Computational Chemistry, and Data Analysis)
Figures

Figure 1

Open AccessFeature PaperArticle Carbon Nanotubes’ Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra
Nanomaterials 2017, 7(11), 386; doi:10.3390/nano7110386
Received: 7 October 2017 / Revised: 6 November 2017 / Accepted: 8 November 2017 / Published: 11 November 2017
PDF Full-text (2469 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra
[...] Read more.
This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments) and perturbation theory. The experimental measures of Jm showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria. The best model for the prediction of Jm for other CNTs was provided by random forest using eight features, obtaining test R-squared (R2) of 0.863 and test root-mean-square error (RMSE) of 0.0461. The results demonstrate the capability of encoding CNT information into spectral moments of the Raman star graphs (SG) transform with a potential applicability as predictive tools in nanotechnology and material risk assessments. Full article
(This article belongs to the Special Issue Experimental Nanosciences, Computational Chemistry, and Data Analysis)
Figures

Open AccessFeature PaperArticle Modeling of Interactions between the Zebrafish Hatching Enzyme ZHE1 and A Series of Metal Oxide Nanoparticles: Nano-QSAR and Causal Analysis of Inactivation Mechanisms
Nanomaterials 2017, 7(10), 330; doi:10.3390/nano7100330
Received: 19 September 2017 / Revised: 12 October 2017 / Accepted: 12 October 2017 / Published: 16 October 2017
PDF Full-text (2013 KB) | HTML Full-text | XML Full-text
Abstract
The quantitative relationships between the activity of zebrafish ZHE1 enzyme and a series of experimental and physicochemical features of 24 metal oxide nanoparticles were revealed. Vital characteristics of the nanoparticles’ structure were reflected using both experimental and theoretical descriptors. The developed quantitative structure–activity
[...] Read more.
The quantitative relationships between the activity of zebrafish ZHE1 enzyme and a series of experimental and physicochemical features of 24 metal oxide nanoparticles were revealed. Vital characteristics of the nanoparticles’ structure were reflected using both experimental and theoretical descriptors. The developed quantitative structure–activity relationship model for nanoparticles (nano-QSAR) was capable of predicting the enzyme inactivation based on four descriptors: the hydrodynamic radius, mass density, the Wigner–Seitz radius, and the covalent index. The nano-QSAR model was calculated using the non-linear regression tree M5P algorithm. The developed model is characterized by high robustness R2bagging = 0.90 and external predictivity Q2EXT = 0.93. This model is in agreement with modern theories of aquatic toxicity. Dissolution and size-dependent characteristics are among the key driving forces for enzyme inactivation. It was proven that ZnO, CuO, Cr2O3, and NiO nanoparticles demonstrated strong inhibitory effects because of their solubility. The proposed approach could be used as a non-experimental alternative to animal testing. Additionally, methods of causal discovery were applied to shed light on the mechanisms and modes of action. Full article
(This article belongs to the Special Issue Experimental Nanosciences, Computational Chemistry, and Data Analysis)
Figures

Figure 1

Open AccessArticle Effects of Various Surfactants on the Dispersion of MWCNTs–OH in Aqueous Solution
Nanomaterials 2017, 7(9), 262; doi:10.3390/nano7090262
Received: 16 July 2017 / Revised: 23 August 2017 / Accepted: 30 August 2017 / Published: 6 September 2017
PDF Full-text (5638 KB) | HTML Full-text | XML Full-text
Abstract
Dispersion of carbon nanotubes (CNTs) is a challenge for their application in the resulting matrixes. The present study conducted a comparison investigation of the effect of four surfactants: Alkylphenol polyoxyethylene ether (APEO), Silane modified polycarboxylate (Silane-PCE), I-Cationic polycarboxylate (I-C-PCE), and II-Cationic polycarboxylate (II-C-PCE)
[...] Read more.
Dispersion of carbon nanotubes (CNTs) is a challenge for their application in the resulting matrixes. The present study conducted a comparison investigation of the effect of four surfactants: Alkylphenol polyoxyethylene ether (APEO), Silane modified polycarboxylate (Silane-PCE), I-Cationic polycarboxylate (I-C-PCE), and II-Cationic polycarboxylate (II-C-PCE) on the dispersion of hydroxyl functionalized multi-walled carbon nanotubes (MWCNTs–OH). Among the four surfactants, APEO and II-C-PCE provide the best and the worst dispersion effect of CNTs in water, respectively. Dispersion effect of MWCNTs–OH has been characterized by optical microscope (OM), field emission-scanning electron microscope (FE-SEM), and Ultraviolet–visible spectroscopy (UV–Vis).The OM images are well consistent with the UV–Vis results. Based on the chemical molecular structures of the four surfactants, the mechanism of MWCNTs–OH dispersion in water was investigated. For each kind of surfactant, an optimum surfactant/MWCNTs–OH ratio has been determined. This ratio showed a significant influence on the dispersion of MWCNTs–OH. Surfactant concentration higher or lower than this value can weaken the dispersion quality of MWCNTs–OH. Full article
(This article belongs to the Special Issue Experimental Nanosciences, Computational Chemistry, and Data Analysis)
Figures

Back to Top