ArticleACS omega2024
New Excipient For Oral Drug Delivery: CNC Derived From Sugarcane Bagasse-Derived Microcrystalline Cellulose.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- A Data-Driven Semi-Quantitative Framework for Comparative Screening and Tiering of Cellulose Nanocrystal Isolation Pathways: Implications for Process Assessment and Future Optimization.Molecules (Basel, Switzerland) · 2026Review
- Biorefinery and Valorization Strategies for Sugarcane Bagasse: Integrating Food, Health, Economic, and Industrial Applications.Food science & nutrition · 2025Review
- Transport Mechanisms of 2D Nanoparticles across a Human Follicle-Associated Epithelium Model.ACS omega · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanocrystalline cellulose (CNC) has emerged as a subject of researcher's interest because of its diverse attributes encompassing biocompatibility, sustainability, a high aspect ratio, and an abundance of -OH groups suitable for modifications. Sugarcane bagasse microcrystalline cellulose (SCBMCC) was used as the raw material for the preparation of CNC due to its pure cellulose content, which is mildly compromised by the pectin, hemicellulose, lignin, and other lignocellulosic components. In the present work, CNC was extracted from SCBMCC and used as a disintegrant. The classic hydrolysis technique was used for the preparation of CNC. Hydrolytic conditions were optimized using the response surface methodology (RSM). The optimized batch of CNC was characterized using techniques such as field-emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. Notably, CNC prepared under a hydrolysis time of 90 min exhibited the highest crystallinity of 69.9%. The average particle size and zeta potential were found to be 145 nm and -34.4 mV, respectively. Thermal analysis suggested that an intermediate hydrolysis time resulted in CNC with enhanced thermal stability, showcasing its potential for pharmaceutical applications. Diclofenac potassium was used as the model drug to evaluate the disintegrant properties of CNC as an excipient. Tablets were prepared using the direct compression method. SCBMCC and CNC were used as disintegrants and were compared with the commercial product. The disintegration times (DTs) attained for the tablets prepared using CNC and SCBMCC are 219 and 339.83 s, respectively. The dissolution study of CNC showed a dissolution efficacy (DE%) of 66 and a mean dissolution time (MDT) of 12. The research findings showed that tablets prepared using CNC as disintegrants exhibited the fastest disintegration compared to other formulations.
Identifiers
What OpenQuestion holds
Registered trials
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.