Data · dataset · 2025
BET Surface Area, Pore Volume, and Pore Size Distribution of Polyols
Listed in Riga Stradins University dataverse
Pharmaceutically relevant polyols vary significantly in terms of their textural properties, including surface area, pore volume, and pore size distribution, which can impact their behaviour in formulation development, drug loading capacity, and overall performance in solid-state delivery systems.
Description
Among the most widely applied and reliable techniques for textural analysis is the Brunauer–Emmett–Teller (BET) method, which provides valuable insight into the physical characteristics of porous materials.
For this purpose, a Quantachrome NOVA instrument was employed, operating under NOVAwin software version 11.03 for data acquisition and reduction. Commercially available polyol samples were subjected to BET analysis to determine specific surface area (SSA), total pore volume (TPV), and average pore diameter (APD). Degassing conditions for each sample were applied based on supplier datasheet specifications to ensure complete removal of moisture and volatile contaminants before measurement.
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To characterise the specific surface area, total pore volume, and average pore diameter of selected polyols including sucrose, dextrin, inulin, xylitol, sorbitol, isomalt, mannitol, oligofructose, polyethylene glycol and caffeine. This data provides critical support for assessing the physicochemical and formulation-related attributes of these excipients. Each analysis was conducted under controlled conditions to ensure reproducibility and comparability.
Each sample was weighed into a glass sample tube with a 1 cc bulb volume and degassed under vacuum at elevated temperatures for a specific duration (as indicated in individual sample datasheets) using the Quantachrome degassing station. After degassing, samples were transferred to the analysis port, and Nitrogen gas adsorption measurements were conducted at 273 K using a liquid nitrogen bath to maintain isothermal conditions.
Surface area was calculated using the BET method over a relative pressure (P/P₀) range selected to fit the linear region of the BET plot. Pore volume and pore size distribution were derived using the Barrett–Joyner–Halenda (BJH) method based on the adsorption branch of the isotherm. All measurements were processed using Quantachrome NovaWin software version 11.03.
A comprehensive dataset was prepared in the form of an Excel spreadsheet containing the calculated surface area (m²/g), total pore volume (cm³/g), and average pore diameter (nm) for each polyol sample. Additionally, isotherm profiles (adsorbed volume vs. relative pressure) and pore size distribution graphs (dV/dlogD vs. pore diameter) were included on separate sheets for visual representation of the data. A summary table of BET and BJH parameters was added for simplified comparative analysis.
Links
Where it is published
- Dataverse dataset page dataverse.rsu.lv/dataset.xhtml?persistentId=doi%3A10.48510%2FFK2%2F96WYKV ↗
landing page · from dataverse rsu lv
- DOI doi.org/10.48510/fk2/96wykv ↗
DOI / persistent id · from dataverse rsu lv
Catalogue records · 1
- Dataverse API dataverse.rsu.lv/api/datasets/:persistentId/?persistentId=doi%3A10.48510%2FFK2%… ↗
metadata API · from dataverse rsu lv
Topics
- Stated by source
- Chemistry · Medicine, Health and Life Sciences
- From keywords
- Chemistry · Life Sciences · Medicine & Health
- Inferred from text
- Tabular 65%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Riga Stradins University dataverse | doi:10.48510/FK2/96WYKV | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].dataverse_subject:chemistry | source · dataverse rsu lv | connector:dataverse_rsu_lv@1.0.0 | /subjects |
| concepts[field].dataverse_subject:medicine-health-and-life-sciences | source · dataverse rsu lv | connector:dataverse_rsu_lv@1.0.0 | /subjects |
| concepts[field].local:field:chemistry | mapping · dataverse rsu lv | connector:dataverse_rsu_lv@1.0.0 | /subjects |
| concepts[field].local:field:life-sciences | mapping · dataverse rsu lv | connector:dataverse_rsu_lv@1.0.0 | /subjects |
| concepts[field].local:field:medicine-health | mapping · dataverse rsu lv | connector:dataverse_rsu_lv@1.0.0 | /subjects |
| concepts[modality].local:modality:tabular | enrichment · dataverse rsu lv | keyword-concept-rules@1.0.0 | title+description (65%) |
| created_date | source · dataverse rsu lv | connector:dataverse_rsu_lv@1.0.0 | |
| description | source · dataverse rsu lv | connector:dataverse_rsu_lv@1.0.0 | /description |
| publication_date | source · dataverse rsu lv | connector:dataverse_rsu_lv@1.0.0 | |
| title | source · dataverse rsu lv | connector:dataverse_rsu_lv@1.0.0 | /name |
| updated_date | source · dataverse rsu lv | connector:dataverse_rsu_lv@1.0.0 | |
| version_label | source · dataverse rsu lv | connector:dataverse_rsu_lv@1.0.0 |