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ScienceDaily

Clothes last longer and shed fewer microfibers in quicker, cooler washing cycles

Those nice new clothes you got for Christmas or in the new year sales might just last longer, thanks to advice from scientists researching the impact washing machines have on clothes and the environment.

Academics from the University of Leeds and specialists from Procter & Gamble, makers of Ariel, Daz, Bold, Fairy and Lenor, have wrung out new insight into how laundering clothing affects fading, colour runs and microfibre release.

Every load of washing releases hundreds of thousands of microfibres — tiny strands that are flushed down the drain. Many reach beaches and oceans where they can remain for many years and be swallowed by sea creatures.

In what is the first research into wash cycle duration that used both laboratory and real consumer testing, they found that reducing both washing cycle length and water temperature can significantly extend the life of garments and reduce the quantity of dye and microfibres shed into the environment.

Report lead author Lucy Cotton, from the University’s School of Design, said: “We are increasingly familiar with the environmental threat posed by throwaway fast fashion, but we also know that consumers claim their clothes can lose their fit, softness and colour after fewer than five washes — this means it’s more likely they will ditch them long before they are worn out.

“Using shorter, cooler washes is a simple way everyone can make their clothes last longer and keep them out of landfill.”

Dr Cotton worked with Dr Adam Hayward and Dr Neil Lant from P&G’s Newcastle Innovation Centre, as well as Leeds colleague Dr Richard Blackburn. Their findings are published today in the journal Dyes and Pigments.

Mimicking average household loads, they washed 12 dark and eight brightly-coloured t-shirts, together with white fabric squares to test colour-fastness.

Conventional domestic washing machines and Ariel pods of biological detergent were used, comparing 30 minute cycles at 25°C, and 85 minute cycles at 40°C (both with 1,600rpm spins) for 16 cycles each. The research was repeated and validated with authentic loads of dirty laundry provided by UK consumers.

A series of tests were carried out on the garments and fabric squares, and the washing machine waste water analysed. Chemical analysis distinguished individual dyes washed out of the clothing, and microfibres were collected and weighed.

The tests established:

  • There was significantly less colour loss in the t-shirts that were washed using the cooler, quicker cycle;
  • Quicker, cooler washes decreased dye transfer from coloured washing;
  • Significantly less microfibres were released into wastewater during the quicker, cooler wash.

The researchers found washing with a quicker, cooler cycle reduced the amount of microfibre release into the environment by up to 52%, and cut dye release by up to 74%.

Dr Blackburn, who heads the Sustainable Materials Research Group at Leeds, said: “Our findings can help tackle the issue of ‘invisible’ plastics in the environment.

“Synthetic microfibres are released every time textiles are washed and account for more than a third of all plastic reaching the ocean. But microfibres from cotton and other natural sources are found in even greater numbers in the sea, and we’re worried about their impact too.

“Our research shows that consumers can actively reduce the number of microfibres released from their own clothing simply by washing in quicker, cooler cycles.”

What is more, washing clothes at 20°C rather than 40°C saves approximately 66% of the energy used per load — according to the Energy Saving Trust, providing even more reason to use quicker, cooler cycles to reduce energy use and CO2 emissions.

Dr Lant, a Procter & Gamble Research Fellow, added: “Advances in detergent technology, especially in sustainable ingredients such as enzymes, are allowing consumers to get excellent cleaning results in colder and quicker washes.

“It’s well known that these cycles reduce our energy bills and carbon footprint, but our partnership with the University of Leeds is helping us understand how they also slow down the ageing of clothes — keeping us looking smart, saving us money replacing garments and helping the environment. It’s a real win win win.”

Further information:

“Improved garment longevity and reduced microfibre release are important sustainability benefits of laundering in colder and quicker washing machine cycles” is published on 14 January 2019 in Dyes and Pigments.

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ScienceDaily

Machine learning shapes microwaves for a computer’s eyes

Engineers from Duke University and the Institut de Physique de Nice in France have developed a new method to identify objects using microwaves that improves accuracy while reducing the associated computing time and power requirements.

The system could provide a boost to object identification and speed in fields where both are critical, such as autonomous vehicles, security screening and motion sensing.

The new machine-learning approach cuts out the middleman, skipping the step of creating an image for analysis by a human and instead analyzes the pure data directly. It also jointly determines optimal hardware settings that reveal the most important data while simultaneously discovering what the most important data actually is. In a proof-of-principle study, the setup correctly identified a set of 3D numbers using tens of measurements instead of the hundreds or thousands typically required.

The results appear online on December 6 in the journal Advanced Science and are a collaboration between David R. Smith, the James B. Duke Distinguished Professor of Electrical and Computer Engineering at Duke, and Roarke Horstmeyer, assistant professor of biomedical engineering at Duke.

“Object identification schemes typically take measurements and go to all this trouble to make an image for people to look at and appreciate,” said Horstmeyer. “But that’s inefficient because the computer doesn’t need to ‘look’ at an image at all.”

“This approach circumvents that step and allows the program to capture details that an image-forming process might miss while ignoring other details of the scene that it doesn’t need,” added Aaron Diebold, a research assistant in Smith’s lab. “We’re basically trying to see the object directly from the eyes of the machine.”

In the study, the researchers use a metamaterial antenna that can sculpt a microwave wave front into many different shapes. In this case, the metamaterial is an 8×8 grid of squares, each of which contains electronic structures that allow it to be dynamically tuned to either block or transmit microwaves.

For each measurement, the intelligent sensor selects a handful of squares to let microwaves pass through. This creates a unique microwave pattern, which bounces off the object to be recognized and returns to another similar metamaterial antenna. The sensing antenna also uses a pattern of active squares to add further options to shape the reflected waves. The computer then analyzes the incoming signal and attempts to identify the object.

By repeating this process thousands of times for different variations, the machine learning algorithm eventually discovers which pieces of information are the most important as well as which settings on both the sending and receiving antennas are the best at gathering them.

“The transmitter and receiver act together and are designed together by the machine learning algorithm,” said Mohammadreza Imani, research assistant in Smith’s lab. “They are jointly designed and optimized to capture the features relevant to the task at hand.”

“If you know your task, and you know what sort of scene to expect, you may not need to capture all the information possible,” said Philipp del Hougne, a postdoctoral fellow at the Institut de Physique de Nice. “This co-design of measurement and processing allows us to make use of all the a priori knowledge that we have about the task, scene and measurement constraints to optimize the entire sensing process.”

After training, the machine learning algorithm landed on a small group of settings that could help it separate the data’s wheat from the chaff, cutting down on the number of measurements, time and computational power it needs. Instead of the hundreds or even thousands of measurements typically required by traditional microwave imaging systems, it could see the object in less than 10 measurements.

Whether or not this level of improvement would scale up to more complicated sensing applications is an open question. But the researchers are already trying to use their new concept to optimize hand-motion and gesture recognition for next-generation computer interfaces. There are plenty of other domains where improvements in microwave sensing are needed, and the small size, low cost and easy manufacturability of these types of metamaterials make them promising candidates for future devices.

“Microwaves are ideal for applications like concealed threat detection, identifying objects on the road for driverless cars or monitoring for emergencies in assisted-living facilities,” said del Hougne. “When you think about all of these applications, you need the sensing to be as quick as possible, so we hope our approach will prove useful in making these ideas reliable realities.”

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IEEE Spectrum

CES 2020: Robot Vacuums That Don’t Repeat Mistakes, and Other Little Fixes to Life’s Annoyances

Maybe we won’t see a breakthrough new technology at CES 2020. But it’s nice to see consumer electronics companies thinking about our pain points.

LG, kicking of CES press day, was all about AI, or, as the company brands it, ThinQ. Its long-term vision was a grandiose, familiar one in which all the objects that use electricity in your house will talk amongst themselves to make your life perfect.

LG’s nearer term applications of AI to household appliances were more interesting. For one, the company promised that this year’s models of its robot vacuum, the R9, will learn from mistakes—when it gets stuck in a tight corner or under a cabinet, say, and you have to retrieve it, it won’t go there again (kind of like my cat).

In another useful application of AI, LG plans to introduce washing machines that will detect the type of fabrics in the pile of clothes you shove in, automatically setting the appropriate wash cycle settings. (I reached out to the company for information on sensors and other details, and will update when I get an answer.)

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Hackster.io

Arduino Watch Is an Impressive Feat of Miniaturization

While many of us now use our phones to keep track of what time of day it is, it’s still nice to have a watch that shows you — at literally the flick of your wrist — this important information. If you’d like to make one that’s all your own, plus ups your geek cred, creator moononournation shows you how with his recent project.

The build starts out with a “core” assembly, accessing the different dev board options available, how to keep it accurate, the triggering method, and of course, the display. He settled on the Pro Micro board, as it uses an ATmega32U4 microcontroller for control and USB communication without a power hungry FTDI chip.

It also comes in a 3.3V version, allowing it to easily be used with a 3.7V LiPo. Display-wise, he chose a 240 x 240 pixel ST7789 LCD, and an RTC module keeps time. Finally, a pair of vibration sensors wakes the prototype up from sleep mode.

Part two outlines how the device was stuffed into a watch housing. It’s an impressive feat of miniaturization, with the Pro board arranged beside a tiny LiPo. The RTC and another small (1 mAh) battery are soldered on toward the bottom of the board. The LCD screen is then placed on top to conceal the components, and everything is crammed into a nice printed case with a wrist strap. Unfortunately, the double vibration sensor setup couldn’t be fit in the case, so hopefully moononournation will be able to come up with an acceptable alternative at some point in the future!

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Author: Jeremy S. Cook