Automated Author ProfileMashayekhi Suzaei, Foad
Mashayekhi Suzaei, Foad
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.9 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Aim: In this study, we evaluated the greenness and whiteness scores for microextraction techniques usedin therapeutic drug monitoring. Additionally, the cons and pros of each evaluated method and theirimpacts on the provided scores are also discussed. Materials & methods: The Analytical Greenness SamplePreparation metric tool and white analytical chemistry principles are used for related published works(2007–2023). Results & conclusion: This study provided valuable insights for developing methods basedon microextraction techniques with a balance in greenness and whiteness areas. Some methods basedon a specific technique recorded higher scores, making them suitable candidates as green analyticalapproaches, and some others achieved high scores both in green and white areas with a satisfactorybalance between principles.
Authors
- Hosseini Pour, Parastoo ;
- Mashayekhi Suzaei, Foad ;
- Mosayeb Daryanavard, Seyed
Aim: In this study, we evaluated the greenness and whiteness scores for microextraction techniques usedin therapeutic drug monitoring. Additionally, the cons and pros of each evaluated method and theirimpacts on the provided scores are also discussed. Materials & methods: The Analytical Greenness SamplePreparation metric tool and white analytical chemistry principles are used for related published works(2007–2023). Results & conclusion: This study provided valuable insights for developing methods basedon microextraction techniques with a balance in greenness and whiteness areas. Some methods basedon a specific technique recorded higher scores, making them suitable candidates as green analyticalapproaches, and some others achieved high scores both in green and white areas with a satisfactorybalance between principles.
Authors
- Hosseini Pour, Parastoo ;
- Mashayekhi Suzaei, Foad ;
- Mosayeb Daryanavard, Seyed