You Can’t Spell ‘Carbon Nanotube Electricity’ Without ‘Yarn’

The Problem

Category: Energy

Harvesting mechanical energy from a person’s everyday ordinary actions – or nature’s actions – is both expensive and inefficient.  Therefore, significant amounts of energy are wasted.

 The Tech

An international team of researchers have developed a technology called Twistron Harvesters, which is essentially carbon nanotube submerged in ion gel that is weaved into yarn.  A small electric current is generated when the weave is stretched out.  The carbon nanotubes can potentially replace external voltages which were previously used for this type of mechanical energy harvesting.

As seen in the photo below, clothing can have this carbon nanotube tech weaved into the fabric.  Electricity can be simply generated as a person breathes in and out, stretching out the weave.  The tech was also tested in ocean water.  Waves can naturally stretch the harvesters, generating power.

The power generated is still not sufficient for home lighting or EV charging, however scientists are optimistic that improving the tech and dropping carbon nanotube pricing will make this energy generation method a possible power charging option for wearables.


Article Title: Carbon nanotube “yarn” generates electricity when stretched
Website: Ars Technica

 The Stakeholders Using The Tech

Clothing Designers

Tech Companies (both wearables and non-wearables)


Utilities (for large-scale generation, i.e. waves)

Aquatic authorities

The First Three Steps

  1. From a wearables standpoint, the scientists should seek an ambitious wearable tech partner willing to conduct experiments using the carbon nanotube tech to charge actual wearable tech.
  2. They should then partner with a clothing manufacturer to see if mass-producing textiles with this carbon nanotube tech is feasible.
  3. If so, they should conduct testing on robots and/or humans to see how effectively everyday human mechanical actions generate power.

UNI: gm2778

Comment on Google maps predicts parking difficulty using machine learning

I imagine this application can be extended to other supply/demand service scenarios such as assessing wait times for entering events (i.e. concerts or baseball games) or restaurant seating availability. Certainly, this will lead to less demand for crowded services, and thus an influx of supply! “Nobody goes there anymore – it’s too crowded.”


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