ArticleScientific reports2018
Identification of Magnesium Oxychloride Cement Biomaterial Heterogeneity using Raman Chemical Mapping and NIR Hyperspectral Chemical Imaging.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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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.
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Who cites it
5 citing papers in PubMed.
- Magnesium Oxychloride Cement: A Low-Carbon Binder as an Alternative to Portland Cement.Materials (Basel, Switzerland) · 2026Review
- Anharmonic DFT Study of Near-Infrared Spectra of Caffeine: Vibrational Analysis of the Second Overtones and Ternary Combinations.Molecules (Basel, Switzerland) · 2021Article
- Simulated NIR spectra as sensitive markers of the structure and interactions in nucleobases.Scientific reports · 2019Article
- Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals.Journal of visualized experiments : JoVE · 2019Article
- Breakthrough Potential in Near-Infrared Spectroscopy: Spectra Simulation. A Review of Recent Developments.Frontiers in chemistry · 2019Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The present study investigated spatial heterogeneity in magnesium oxychloride cements within a model of a mould using hyperspectral chemical imaging (HCI). The ability to inspect cements within a mould allows for the assessment of material formation in real time in addition to factors affecting ultimate material formation. Both macro scale NIR HCI and micro scale pixel-wise Raman chemical mapping were employed to characterise the same specimens. NIR imaging is rapid, however spectra are often convoluted through the overlapping of overtone peaks, which can make interpretation difficult. Raman spectra are more easily interpretable, however Raman imaging can suffer from slower acquisition times, particularly when the signal to noise ratio is relatively poor and the spatial resolution is high. To overcome the limitations of both, Raman/NIR data fusion techniques were explored and implemented. Spectra collected using both modalities were co-registered and intra and inter-modality peak correlations were investigated while k-means cluster patterns were compared. In addition, partial least squares regression models, built using NIR spectra, predicted chemical-identifying Raman peaks with an R
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