Separating cotton and polyester in a blended waste can be challenging.
However, several chemical methods have been explored to facilitate the
separation of cotton and polyester in textile waste.
Caustic Treatment:
One approach involves treating the blended waste with a caustic soda (sodium
hydroxide) solution. The caustic soda selectively attacks the cotton fibers,
causing them to degrade while leaving the polyester fibers intact. This
process, known as mercerization, weakens the cotton fibers, making it possible
to separate them from the polyester through mechanical means such as agitation
or high-pressure water jets.
Enzymatic Treatment:
Enzymes can be used to selectively degrade cotton fibers in a blended waste.
Specific enzymes, such as cellulases, are capable of breaking down the
cellulose present in cotton, while polyester remains unaffected. This
enzymatic treatment weakens the cotton fibers, enabling their separation from
polyester through mechanical processes.
Solvent Treatment:
Certain solvents have been explored to selectively dissolve either the cotton
or polyester component of the blended waste. For example, a solvent like
N-methyl-2-pyrrolidone (NMP) has been used to dissolve and extract the cotton
fibers, while leaving the polyester fibers intact. This allows for the
separation of the two components, which can then be further processed
individually.
Selective Depolymerization:
Researchers have also investigated methods to selectively depolymerize either
the cotton or polyester component in blended waste. This involves breaking
down the polymer chains of one fiber while leaving the other unaffected.
Various chemical treatments, such as acid hydrolysis or alkaline hydrolysis,
can be used to achieve this selective depolymerization, enabling subsequent
separation of the two fibers.
Achieving complete and efficient separation of cotton and polyester in blended
waste remains a complex task. Researchers and experts in the field of textile
recycling continue to explore and refine these chemical methods to improve
their effectiveness and scalability.
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