Research leads to viable solution for polycotton textile waste recycling

Chemical processing of blended fabrics yields renewable feedstock for bio-based plastics

Universiteit van Amsterdam

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Polyester residue of postconsumer polycotton waste textiles after processing

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Credit: Image: HIMS / Avantium

In a paper just published in Nature Communications, researchers at the Industrial Sustainable Chemistry group of the University of Amsterdam (UvA) present a solution to the challenging problem of recycling polycotton textile waste. The process, developed in cooperation with the company Avantium, starts with fully removing all cotton from the fabric using superconcentrated hydrochloric acid at room temperature. The cotton is converted into glucose, which can be used as a feedstock for biobased products such as renewable plastics. The remaining polyester fibres can be reprocessed using available polyester recycling methods.

The research was led by Prof. Gert-Jan Gruter, who heads the Industrial Sustainable Chemistry group at the UvA’s Van ‘t Hoff Institute for Molecular Sciences (HIMS) as a parttime professor. Gruter is Chief Technology Officer at Avantium where he leads the development of renewable and circular polymer materials and technologies that are key to transforming our fossil-based economy into a renewable, bio-based economy. “Being able to recover glucose from the cotton in textile waste is a crucial contribution to this, as glucose is a key bio-based feedstock. Currently, it is produced from starch from corn and wheat. If and when we will be producing plastics from biomass on a large scale, the world will need a lot of non-food glucose.”

Equally important, the process now presented in the Nature Communications paper provides a solution to the mammoth problem of recycling textile waste. According to Gruter, it is the first effective method for recycling both cotton and polyester components of polycotton with high efficiency. Gruter’s PhD student Nienke Leenders, first author of the paper, performed many tests under the four-year MiWaTex project that has been funded by the Dutch Research Council NWO and is now about halfway. The project entails cooperation with textile sorting and recycling company Wieland, workwear producer Groenendijk Bedrijfskleding, Modint, the trade association for the Dutch clothing and textile industry, and CuRe, developer of advanced technology for chemical recycling of polyester.

Scalability and cost-effectiveness

The Nature Communications paper describes how Leenders performed experiments using Avantium’s pilot plant for its proprietary Dawn Technology which was originally developed to convert non-food plant-based feedstock (e.g wood) into glucose and lignin. Its key feature is using highly concentrated hydrochloric acid (43% by weight) at room temperature. Leenders tested batches of actual post-consumer polycotton waste textiles in Avantium’s Dawn pilot plant. It turned out the cotton cellulose could be fully hydrolyzed into glucose under industrially relevant conditions. The polyester part of the fabric remained intact and could be easily separated. The trials demonstrated high glucose yields, indicating scalability and cost-effectiveness.

The cotton-derived glucose from the process can be used in a wide range of industrial applications, including polymers, resins and solvents. It can for example be used by Avantium to produce its lead product 2,5-furandicarboxylic acid (FDCA), a crucial component in the production of the biobased PEF polyester (polyethylene furanoate) that offers a renewable alternative to PET bottles.

The process also enables the complete recycling of polyester from polycotton. It can be chemically recycled to form new virgin polyester, as was established by tests performed by CuRe.

Favorable techno-economic analysis

According to Gruter, the research lays the foundation for actual industrial-scale recycling of polycotton textiles and the first commercial availability of non-food glucose. “Many parties are trying to get either of these things done but no one has succeeded yet. Our techno-economic analysis looks rather favourable and Avantium has already invested substantially in this development. Our ambition is to advance this technology to the next phase of commercialization, together with partners. So we might very well be the first to market non-food glucose obtained through a bio-refinery approach.”

See also:

• Research group Industrial Sustainable Chemistry
• MIWATEX project: Circularity for Mixed cotton/polyester WAste TEXtiles by upcycling to virgin monomer building blocks.
• Avantium
• Avantium: Dawn technology (PDF)

Samples of polycotton waste textile before (left) and after (right) processing. The sample at the right is transparent since all cotton has been removed; only the polyester remains. Image: HIMS / Avantium.

Residue after processing of pure cotton shirts. Only the polyester labels and seams remain. Image: HIMS / Avantium

Credit

Image: HIMS / Avantium

Journal

Nature Communications

DOI

10.1038/s41467-025-55935-6 

Method of Research

Experimental study

Article Title

Polycotton waste textile recycling by sequential hydrolysis and glycolysis

Article Publication Date

29-Jan-2025

 

Even quantum physics obeys the law of entropy

Is there a contradiction between quantum theory and thermodynamics? On the surface, yes - but at TU Wien, researchers have now shown how the two fit together perfectly

Vienna University of Technology

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Can quantum systems become more disordered, as thermodynamics would predict? Yes, they can - if a proper definition of "entropy" is used.

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Credit: TU Wien

It is one of the most important laws of nature that we know: The famous second law of thermodynamics says that the world gets more and more disordered, when random chance is at play. Or, to put it more precisely: That entropy must increase in every closed system. Ordered structures lose their order, regular ice crystals turn into water, porcelain vases are broken up into shards. At first glance, however, quantum physics does not really seem to adhere to this rule: Mathematically speaking, entropy in quantum systems always remains the same.

A research team at TU Wien has now taken a closer look at this apparent contradiction and has been able to show: It depends on what kind of entropy you look at. If you define the concept of entropy in a way that it compatible with the basic ideas of quantum physics, then there is no longer any contradiction between quantum physics and thermodynamics. Entropy also increases in initially ordered quantum systems until it reaches a final state of disorder.

Entropy and the direction of time

Equating ‘entropy’ with ‘disorder’ is not entirely correct. After all, what you understand by ‘disorder’ may be subjective, but entropy can be clearly defined with mathematical equations.

“Entropy is a measure of whether a system is in a special, very particular state, in which case the system has low entropy, or whether it is in one of many states that look more or less the same, in which case it has high entropy,” explains Prof Marcus Huber from the Institute for Atomic and Subatomic Physics at TU Wien. If you start with a very specific state, for example a box full of balls that are sorted exactly by colour, then if you shake the box a little, a higher entropy mixed state will develop over time. This is simply due to the fact that only a few ordered states exist, but many that are similarly disordered.

“From a physical point of view, this is what defines the direction of time,” says Max Lock (TU Wien). “In the past, entropy was lower; the future is where entropy is higher.” However, quantum physics encounters a problem here: the mathematician and physicist John von Neumann was able to show: according to the laws of quantum physics, the entropy in a quantum system cannot change at all. If you have the full information about a quantum system, the so-called ‘von Neumann entropy’ always stays the same; it is impossible to say whether time is running forwards or backwards, each point in time is physically as good as any other.

We only ever know part of the information

“But this view leaves out something important,” says Tom Rivlin (TU Vienna). "In quantum physics you can never actually have full information about a system. We can choose a property of the system that we want to measure – a so-called observable. This can be, for example, the location of a particle or its speed. Quantum theory then tells us the probabilities to obtain different possible measurement results. But according to quantum theory, we can never have full information about the system."

Even if we know the probabilities, the actual result of a specific measurement remains a surprise. This element of surprise must be included in the definition of entropy. Instead of calculating the von Neumann entropy for the complete quantum state of the entire system, you could calculate an entropy for a specific observable. The former would not change with time, but the latter might.

This type of entropy is called ‘Shannon entropy’. It depends on the probabilities with which different possible values are measured. ‘You could say that Shannon entropy is a measure of how much information you gain from the measurement,’ says Florian Meier (TU Wien). "If there is only one possible measurement result that occurs with 100% certainty, then the Shannon entropy is zero. You won't be surprised by the result, you won't learn anything from it. If there are many possible values with similarly large probabilities, then the Shannon entropy is large."

Quantum disorder increases after all

The research team has now been able to show that if you start with a state of low Shannon entropy, then this kind of entropy increases in a closed quantum system until it levels off around a maximum value – exactly as is known from thermodynamics in classical systems. The more time passes, the more unclear the measurement results become and the greater the surprise that can be experienced when observing. This has now been proven mathematically and also confirmed by computer simulations that describe the behaviour of several interacting particles.

"This shows us that the second law of thermodynamics is also true in a quantum system that is completely isolated from its environment. You just have to ask the right questions and use a suitable definition of entropy," says Marcus Huber.

If you are investigating quantum systems that consist of very few particles (for example, a hydrogen atom with only a few electrons), then such considerations are irrelevant. But today, especially with regard to modern technical applications of quantum physics, we are often faced with the challenge of describing quantum systems that consist of many particles. “To describe such many-particle systems, it is essential to reconcile quantum theory with thermodynamics,” says Marcus Huber. “That's why we also want to use our basic research to lay the foundation for new quantum technologies.”

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Journal

PRX Quantum

DOI

10.1103/PRXQuantum.6.010309 

Method of Research

Data/statistical analysis

Subject of Research

Not applicable

Article Title

Emergence of a Second Law of Thermodynamics in Isolated Quantum Systems

 

SEOULTECH researchers develop autonomous geological assessment tool

Machine learning-based method enhances the accuracy of measuring dip angles and directions in rock facets

Seoul National University of Science & Technology

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Researchers developed a methodology that autonomously filters unnecessary data from 3D point clouds of rock faces to accurately determine dip angles and directions-critical parameters in geological and structural engineering.

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Credit: Hyungjoon Seo from the Seoul National University of Science and Technology

Machine learning (ML) algorithms are constantly finding new applications in all scientific fields, and geological engineering is no exception. Over the last decade, researchers have developed various ML-based techniques to determine geological features more effortlessly in rocks, such as the dip angle (the angle at which a planar feature is inclined to the horizontal plane) and direction of rock facets in tunnels. Understanding these characteristics is essential for large construction projects as they help ensure structural stability and safety, preventing potential failures or collapses.

Although powerful, most ML models still struggle to differentiate between joint bands and joint embedment points in rock. To clarify, joint bands are broader, less distinct areas within the rock that may include multiple parallel fractures, while joint embedment points are more localized features representing the actual intersections of rock layers. As direct indicators of surface orientation, joint embedment points enable a more accurate measurement of dip angle and direction than joint bands. Thus, methods that can eliminate joint bands from input data can increase the accuracy of ML-based techniques, leading to more precise geological assessments.

To fulfill this challenge, a research team led by Professor Hyungjoon Seo of Seoul National University of Science and Technology (SEOULTECH) developed the Roughness-CANUPO-Dip-Facet (R-C-D-F) method. This ML-powered, multistep approach combines many filtration techniques to remove joint bands while preserving most joint embedment points in the data, leading to excellent accuracy when measuring dip angle and direction. Their paper was made available online on September 11, 2024, and was published in Volume 154 of the journal Tunnelling and Underground Space Technology on December 1, 2024.

The first step of the filtration process consists of a roughness analysis on an input 3D point cloud, taken directly from a rock surface. This step removes minor surface irregularities and noise from the data, preserving continuous lines on the surface but removing joint lines. The second filtration step uses the CANUPO algorithm, which classifies points based on their geometric characteristics and isolates key features, removing even more joint lines. The third filtration step eliminates connecting rock segments based on dip angles, isolating distinct rock formations. Finally, the measurement stage consists of facet segmentation to obtain the dip angle and direction of each section of the rock sample.

The researchers tested the R-C-D-F method on various real tunnel face images, achieving remarkable accuracy rates ranging from 97% to 99.4%. Notably, 100% of joint bands were successfully removed while still preserving 81% of joint embedment points. But the most attractive aspect of this technique was its fully autonomous nature, requiring no human intervention. “By automating the process of filtering and segmenting rock features, it reduces human error and computational inefficiencies, making it ideal for modern infrastructure projects that demand high accuracy and reliability,” highlights Prof. Seo.

Overall, the proposed approach could find promising applications across many disciplines of structural and geological engineering. “The R-C-D-F method’s integration of ML and deep learning ensures reliable and accurate geological data processing, which can directly improve the safety of large-scale engineering projects like tunnels and underground structures,” notes Prof. Seo. “It could also enable the development of smarter and faster geological analysis tools, reducing costs and improving efficiency in industries reliant on subsurface exploration and infrastructure development.”

The innovative approach thus holds great promise for paving the way for safer and more efficient geological engineering solutions.

 

***

 

Reference      
DOI: 10.1016/j.tust.2024.106071

 

                           

About the institute Seoul National University of Science and Technology (SEOULTECH)
Seoul National University of Science and Technology, commonly known as 'SEOULTECH,' is a national university located in Nowon-gu, Seoul, South Korea. Founded in April 1910, around the time of the establishment of the Republic of Korea, SEOULTECH has grown into a large and comprehensive university with a campus size of 504,922 m2. It comprises 10 undergraduate schools, 35 departments, 6 graduate schools, and has an enrollment of approximately 14,595 students.

Website: https://en.seoultech.ac.kr/

 

About the authors

Hyungjoon Seo, Assistant Professor at Seoul National University of Science and Technology, focuses on integrating machine learning into civil engineering to optimize infrastructure analysis.

Jiayao Chen is an Associate Professor at Beijing Jiaotong University in China, who specializes in urban underground engineering and advanced structural geology techniques.

Hongwei Huang, a Professor at Tongji University, China, leads the research on geotechnical engineering and rock mechanics, contributing to innovative methods for geological and structural analysis.

Bara Alseid, a PhD student at Concordia university, specializes in structural rehabilitation and structural health monitoring.

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Journal

Tunnelling and Underground Space Technology

DOI

10.1016/j.tust.2024.106071 

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

R-C-D-F machine learning method to measure for geological structures in 3D point cloud of rock tunnel face

 

Scientists discover a genetic lifeline for the endangered shortfin mako shark

The shortfin mako shark is being fished to extinction, but genetics show that diversity in Atlantic populations remains high. A new study underscores the urgency to halt overfishing and help the fastest shark in the sea survive as our climate changes

Save Our Seas Foundation

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NOAA Fisheries implemented regulations consistent with new ICCAT requirements adopted in 2021, based on the 2017 stock assessment. In the U.S, fishermen may not land or retain Atlantic shortfin mako sharks.

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Credit: Photo by Justin Gilligan | © Save Our Seas Foundation

Shortfin makos are the fastest sharks in the sea, but they’re failing to outpace the scale of overfishing that is driving them to extinction. Global demand for their meat and lucrative fins has placed this predator on the International Union for Conservation of Nature’s (IUCN) endangered list and on Appendix II of the Convention on Trade in Endangered Species of Wild Fauna and Flora (CITES).

The situation for shortfin mako sharks in the Atlantic Ocean is particularly dire. Populations are currently managed as two assumed separate populations (or stocks), with fishery-based assessments indicating that Northern Atlantic mako sharks are overfished. Independent scientific surveys, using data from satellite tags deployed on shortfin makos, suggest that fishing mortality may be 10 times higher than estimates from previous fisheries models. With extreme pressure on mako populations from international fisheries, the questions are: has the shortfin makos’ genetic health and potential to adapt been compromised; and is the current fisheries management strategy based on two populations backed by scientific evidence?

A team of scientists led by Dr Andrea Bernard and Professor Mahmood Shivji from the Save Our Seas Foundation Shark Research Center (SOSF-SRC) and Guy Harvey Institute at Nova Eastern University, USA, has published its answers in a paper ‘Connections across open water: A bi-organelle, genomics-scale assessment of Atlantic-wide population dynamics in a pelagic, endangered apex predator shark (Isurus oxyrinchus)’ in the journal Environmental Applications.

The scientists have for the first time sequenced entire genomes for mitochondrial DNA and conducted high-resolution scans across the nuclear genomes of shortfin makos from nearly the entire distribution of this species in the Atlantic Ocean.

These genomic assessments have discovered a potential lifeline that should add urgency to curbing overfishing. ‘Despite decades of fishing pressure, shortfin mako sharks in the Atlantic Ocean still show a (relatively) high level of genetic diversity,’ explains Professor Shivji. ‘Genetic diversity in a population is what allows species to adapt to environmental change, or to survive catastrophes.’ While overfishing is the single greatest threat to sharks worldwide, many species remain vulnerable to complex and compounding additional threats like habitat loss, deep-sea mining, pollution and our changing climate.

‘We were rather surprised, but also pleased, to see that the genetic health of shortfin makos does not appear to have been severely compromised – yet – by the population reductions caused by overfishing,’ says Professor Shivji. ‘That means that if we can prevent further erosion of this genetic diversity in shortfin mako sharks by urgently curbing overfishing, we have more hope for this species to retain the resilience needed for its populations to adapt to our fast-changing climate and survive.’ He goes on to caution, ‘Typically, in most of the exploited shark species we study we see pretty low diversity.’ Such is the case for the critically endangered great hammerhead shark, another species being fished to the edge of existence, but whose vulnerability to being tipped into extinction is higher because it lacks the diversity to adapt to our rapidly changing climate.

The scientists also hypothesised that nomadic sharks like makos, which have been tracked making extraordinary journeys across oceans, would mix freely, hampered by few genetic barriers. And that is exactly what the research team found from the high-resolution scans made of shortfin mako nuclear DNA.

Nuclear DNA is inherited from both parents, and it suggests that male shortfin mako sharks are indeed ranging across the Atlantic and spreading their genes widely. ‘Female mako sharks, which get even larger than males, are quite capable of also making these large-scale journeys,’ says Professor Shivji. ‘But when we look at the mitochondrial DNA – the genetic material inherited only from mothers – we see a contrasting picture.’

The mitochondrial genome sequences show matrilineal genetic structure for northern and southern hemisphere populations. That’s scientific-speak for the populations in each hemisphere being genetically distinct from each other. In fact, the results suggest that although female shortfin makos may well be as wide-ranging as their male counterparts, they return to key sites in one hemisphere to pup. And if we’re to protect this important genetic diversity, the management of two distinct Atlantic populations – the northern Atlantic and southern Atlantic shortfin mako sharks – is now backed by this high-resolution genetic information.

----------------------------------------------------END----------------------------------------------------

About the Save Our Seas Foundation

Founded in Geneva, Switzerland, in 2003, the Save Our Seas Foundation (SOSF) is a philanthropic organisation whose ultimate goal is to create a legacy of securing the health and sustainability of our oceans, and the communities that depend on them, for generations to come. Its support for research, conservation and education projects worldwide focuses on endangered sharks, rays and skates. Three permanent SOSF research and education centres reinforce its actions in Seychelles, South Africa and the USA.

Dr Mahmood Shivji, director of the Guy Harvey Research Institute and Save Our Seas Shark Research Center (Nova Southeastern University). A major focus of his research is the application of modern molecular genetic techniques to investigate trade-related issues in elasmobranchs.

Credit

Photo by Justin Gilligan | © Save Our Seas Foundation

The fastest fish in the sea, the shortfin mako shark is listed as Endangered on the IUCN Red List of Threatened Species.

Shortfin mako sharks are built for speed. Their streamlined, torpedo-shaped body, powerful muscular tail and specially adapted skin allows them to reach speeds of up to 70km/hr. They are a highly valuable shark on the international market, and have declined rapidly due to overfishing

Credit

Photo © Sebastian Staines

Journal

Evolutionary Applications

DOI

10.1111/eva.70071 

Method of Research

Data/statistical analysis

Subject of Research

Animal tissue samples

Article Title

Connections Across Open Water: A Bi-Organelle, Genomics-Scale Assessment of Atlantic-Wide Population Dynamics in a Pelagic, Endangered Apex Predator Shark (Isurus oxyrinchus)

Article Publication Date

29-Jan-2025