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Record W3025222492 · doi:10.1149/ma2020-01231346mtgabs

Investigating the in-Situ Doping Effect of Niobium Pentoxide Nanostructures on Their Electronic Surface Properties

2020· article· en· W3025222492 on OpenAlexaffabout
Hela Kammoun, Kenza Fanidi, Leandro C. Trevelin, Marcos R.V. Lanza, Ana C. Tavares

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPhotodegradationPhotochemistryRadicalMaterials scienceChemistryPhotocatalysisChemical engineeringCatalysisOrganic chemistry

Abstract

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The release of large quantities of effluents to the environment as wastewater affects the quality of drinking water and it is a serious concern for the environment and for the public health. Often wastewater contains substances that are toxic, carcinogenic and resistant to biodegradation [1] . Thereby, the stringent regulations imposed by environmental and governmental agencies to improve the quality of water, require more efficient wastewater treatment techniques. Advanced oxidative processes (AOPs) consist of non-selective and efficient oxidative degradation of organic pollutants using chemical species with high oxidative power such as hydroxyl radicals [1]. In one of the AOP methods, light interacts with semiconductors generating excitons by the transfer of electrons from the valence band to the conduction one. The excitons react with oxygen and water forming hydroxyl radicals, which degrade organic molecules. Actually, photodegradation is an effective-cost method to treat industrial effluents [1] . However, this technology suffers from some challenges such as insufficient utilization of visible-light. Therefore, advances in photodegradation processes require the development of new semiconductors, the evaluation of their electronic and surface properties, to enhance the efficiency of hydroxyl radicals’ generation in presence of UV light and broad spectrum light source . [2][3]. Different metal oxides, were exploited for such a purpose like TiO 2 , SnO 2 , ZnO 2 , Fe 3 O 2 , Al 2 O 3 , showing different performances in terms of degradation rates [1]. Niobium pentoxide has been investigated for different applications such as humidity, gas and chemical sensing, electrochromic devices and catalysis due to its thermodynamic stability, high corrosion resistance, biocompatibility and Bronsted acidic properties [2]. At the same time, is one of the least studied metal oxides, which makes it attractive for fundamental studies. Nb 2 O 5 is a wide band-gap semiconductor, with a band gap of 3.4 - 4 eV depending on its morphological features, meaning that it can be used as a photocatalyst only when activated with ultraviolet irradiation [4][5][6]. In this work, we investigate the impact of the nature and content of the doping element on the electronic surface properties of Nb 2 O 5 nanostructures aiming at widen its photon absorption in the electromagnetic spectrum. The Nb 2 O 5 nanoparticles were synthesized by hydrothermal synthesis at 150°C, for 2h, by mixing ammonium niobium oxalate with the different percentages of dopant precursors: N, Cu, and Ta. The structural and morphological characterization of the samples was carried out by X-ray diffraction (XRD) and Scanning electronic microscopy (SEM). The optical properties were investigated by UV-Visible-NIR diffuse-reflectance spectroscopy (DRS) to define the effective optical band gap, Ultraviolet photoelectron spectroscopy (UPS) to calculate the work function of the materials and by X-ray photoelectron spectroscopy (XPS) to determine the valence band edge, leading to the complete construction of the energy diagram of the different synthesized nanoparticles. The successful doping of Nb 2 O 5 nanostructures with 1 wt% of Ta, Cu or N lead to a narrowing of the optical band gap as illustrated in Figure 1. The reduction of the optical band gap of Nb 2 O 5 is at least of 0.5 EV through the doping with tantalum but around 1 eV with nitrogen. The correlation between the electronic surface properties and the structure of the materials will be discussed. Acknowledgements The authors would like to thank the funding from NSERC (Strategic partnership program, Canada) and FAPESP (BEPE 2018/17279-1, Brazil). References [1] I. Sirés , E. Brillas, M.A. Oturan & al. Environ Sci Pollut Res ,21, 8336 (2014) [2] X. Lang, X. Chen, J. Zhao, Chem. Soc. Rev. 4,473–486 (2014). [3] M.S.A. Sher Shah, A.R. Park, K. Zhang, J.H. Park, P.J. Yoo, ACS Appl. Mater. Interfaces. 4,3893– 3901(2012). [4] YD Wang, LF Yang, ZL Zhou, YF Li, XH Wu. Materials Letters, 49 (5), 277-281 (2001). [5] I. C. M. S. Santos, L. H. Loureiro, M. F. P. Silva and A. M. V. Cavaleiro, Polyhedron. 21,2009-42015 (2002). [6] J.F. Carneiro, M.J. Paulo, M. Siaj, A.C. Tavares, and M.R.V. Lanza, J. Catal. 332, 51–61 (2015). [7] R. Pandiyan, N. Delegan, A. Dirany, P. Drogui, M. A. El Khakani, J. Phys. Chem. C, 120, 631-638, (2016). Figure 1

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.223
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2020
Admission routes2
Has abstractyes

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