Niobium Pentoxide Nanoparticles and Their Self-Assembled in Building Blocks for Gas Sensors
Bibliographic record
Abstract
Niobium pentoxide (Nb2O5) is characterized by an outstanding chemical stability, high corrosion resistance and color changing chemistry. Nb2O5 can adopt different crystal structures (pseudohexagonal, orthorhombic and monoclinic), depending on the temperature [1]. Still, it is one of the least studied metal oxides. [2], [3]. Thus, exploring new synthetic methods and the physicochemical properties of the resulting oxide nanoparticles (NPs) could open new opportunities in different applications such as electrocatalysis, electrochromic displays, Surface-enhanced Raman spectroscopy and gas, humidity, and biological/chemical sensing. For gas sensing applications, nanostructured individual NPs of Nb2O5 with uniform shape, narrow size distribution along with high surface area are required to facilitate the adsorption of the target gas molecules. [1], [3], [4], [5]. Hydrothermal and solvothermal methods are widely used to produce metal oxides NPs with various morphologies. [1] These methods are easily scaled up and produce materials with high purity. Due to the high autogenous pressure inside the reactor, the nanoparticles are usually crystalline; and depending on the temperature and the time of reaction, the crystal phase can be controlled. However, the control of the size and the shape of the NPs with hydrothermal synthesis is a challenge because after nucleation, the nuclei precipitate and agglomerate forming microstructures with ill-defined shapes. [3] ,[5] In this work, we report the synthesis of spherical-like niobium oxide nanoparticles by one-pot hydrothermal synthesis using as a precursor ammonium niobium oxalate aqueous solution, Figures 1.A and 1.B. The kinetics of the NPs growth was investigated by Dynamic Light Scattering (DLS), Scanning electron microscopy (SEM) and Transmission electron microscopy (TEM). Figure 1.B. shows that the Nb2O5 NP grow according to the Oswald ripening mechanism, with the size of the nanoparticles increasing from 2 nm to 80 nm with the reaction time. However, the nanoparticles tend to coalesce forming larger nanostructures with uncontrollable size landing to unstable suspensions. A comparative study between different charged ligands, revealed citric acid as the best ligand to ensure the stability of the Nb2O5 suspensions and to control the nanoparticles’ size. Indeed, using citric acid the size of the size of the aggregates in the suspensions is reduced by the factor of 10, from 200 to around 20 nm of diameter. The control of the size and shape of the NPs was found to be critical to form 3D superlattices and to maximize the surface area once the nanoparticles form thin films, Figure 1.C. The kinetic of the NPs growth and the physicochemical properties of the NPs and films will be discussed in detail. Acknowledgements The authors would like to thank the funding from NSERC (Strategic partnership program, Canada). References [1] YD Wang, LF Yang, ZL Zhou, YF Li, XH Wu. Materials Letters, 49 (5), 277-281 (2001). [2] I. C. M. S. Santos, L. H. Loureiro, M. F. P. Silva and A. M. V. Cavaleiro, Polyhedron. 21,2009-42015 (2002). [3] J.F. Carneiro, M.J. Paulo, M. Siaj, A.C. Tavares, and M.R.V. Lanza, J. Catal. 332, 51–61 (2015). [4] A. K.Rosmalini, A. R. Rozina, M. M. Y. A. Alsaif, & al, ACS Appl. Mater. Interfaces, 7, 8, 4751-4758 (2015). [5] J. A. Darr, J. Zhang, N. M. Makwana, X. Weng, Chem. Rev., 117, 17, 11125-11238 (2017). Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".