ReviewMembranes2022
Lignocellulosic Biomass-Derived Nanocellulose Crystals as Fillers in Membranes for Water and Wastewater Treatment: A Review.
Review in Membranes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Multifunctional Membranes for Simultaneous Oil/Water Separation and Organic Pollutant Removal: A Review.Membranes · 2026Review
- Composite Membranes Based on MXene and Nanocellulose for Water Purification: Structure, Efficiency, and Future Prospects.Membranes · 2025Review
- Nanocellulose/nanocellulose-based membranes in wastewater treatment: a sustainable path forward for environmental protection.Food chemistry: X · 2025Review
- Agricultural Residues as Raw Materials for Pulp and Paper Production: Overview and Applications on Membrane Fabrication.Membranes · 2023Review
- Effect of Silica Sodalite Loading on SOD/PSF Membranes during Treatment of Phenol-Containing Wastewater.Membranes · 2022Article
- Progress and Prospects of Nanocellulose-Based Membranes for Desalination and Water Treatment.Membranes · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The improvement of membrane applications for wastewater treatment has been a focal point of research in recent times, with a wide variety of efforts being made to enhance the performance, integrity and environmental friendliness of the existing membrane materials. Cellulose nanocrystals (CNCs) are sustainable nanomaterials derived from microorganisms and plants with promising potential in wastewater treatment. Cellulose nanomaterials offer a satisfactory alternative to other environmentally harmful nanomaterials. However, only a few review articles on this important field are available in the open literature, especially in membrane applications for wastewater treatment. This review briefly highlights the circular economy of waste lignocellulosic biomass and the isolation of CNCs from waste lignocellulosic biomass for membrane applications. The surface chemical functionalization technique for the preparation of CNC-based materials with the desired functional groups and properties is outlined. Recent uses of CNC-based materials in membrane applications for wastewater treatment are presented. In addition, the assessment of the environmental impacts of CNCs, cellulose extraction, the production techniques of cellulose products, cellulose product utilization, and their end-of-life disposal are briefly discussed. Furthermore, the challenges and prospects for the development of CNC from waste biomass for application in wastewater treatment are discussed extensively. Finally, this review unraveled some important perceptions on the prospects of CNC-based materials, especially in membrane applications for the treatment of wastewater.
Indexed as
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.