Evidence map›Paper›PMID 25482417›Full record

ReviewBiomatter2014

A brief review of mathematical models of thin film growth and surfaces. A possible route to avoid defects in stents.

Fabricio L Forgerini, Roberto Marchiori

Abstract readReview
In one paragraph

Review in Biomatter, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. On ballistic deposition process on a strip.Journal of statistical physics · 2019
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Fabricio L Forgerinia Federal University of Amazonas (UFAM); Amazonas, Brazil.
Roberto Marchiori

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The morphology of thin films has been extensively studied in the last years. The properties of a thin film are closely related to its microstructure, especially to its morphology and surface roughness. Optical reflectivity, conductivity, and porosity are characteristics that depend on the film structure. The knowledge of atomistic details of the thin film growth process is useful for the development of new techniques and the control of thin films and new materials. Models of growth process are very powerful tools that can help researchers to predict and control physical, chemical, and mechanical properties. In this work we briefly summarize the theoretical models that have been used in the studies of thin films growth. By describing the deposition process of atoms/molecules on the surface of the substrate, one can study the evolution of the bulk and the surface roughness of a thin film. If an experimental growth process is appropriately described by a theoretical model (or even a combination of one or more different models), it can also provide indications to control the surface roughness and porosity of the film. Controlling the growth process one can obtain materials with a set of desired properties, namely tribological, porosity, and electrical ones. These characteristics are necessary for example, for hosting a solid lubricant on the surface of the material. We believe that the models presented in this work can be very useful in understanding the mechanisms of control and adherence of electrodeposited films which are commonly used in medical applications such as stent devices. We also believe that the models can be helpful to the understanding surface problems related to the superficial defects in stents.

Indexed as

Models, TheoreticalStentsSurface PropertiesMaterials TestingPorositymathematical modelingporositystent devicessurface growthsurface roughnessthin films

Identifiers

PMID25482417
PMCPMC4122570

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.