Cottonization: Making Hemp And Flax Fibers Into The Better Cotton

June 20, 2023 by Maya Posch 16 Comments

These days it’s hard to imagine that fabrics were ever made out of anything other than cotton or synthetic fibers, yet it wasn’t too long ago that hemp and flax-based fabrics — linen — were the rule rather than the exception. Cotton production has for centuries had the major disadvantages of requiring a lot of water and pesticides, and harvesting the cotton was very labor-intensive, making cotton rather expensive. In order to make separating the cotton fibers from the seed easier, improved versions of the cotton gin (‘cotton engine’) were developed, with the 19th century’s industrial revolution enabling a fully automated version.

What makes cotton attractive is the ease of processing these fibers, which are part of the seed pod. These fibers are 25 mm – 60 mm long, 12 μm – 45 μm fine fibers that can be pulled off the seeds and spun into yarn or whatever else is needed for the final product, much like wool. Hemp and flax fibers, in contrast, are extracted from the plant stem in the form of bast fibers. Rather than being pure cellulose, these fibers are mostly a mixture of cellulose, lignin, hemicellulose and pectin, which provide the plant with rigidity, but also makes these fibers coarse and stiff.

The main purpose of cottonization is to remove as much of these non-cellulosic components as possible, leaving mostly pure cellulose fibers that not only match the handleability of cotton fibers, but are also generally more durable. Yet cottonization used to be a long and tedious process, which made bast fiber-based textiles expensive. Fortunately, the steam explosion cottonization method that we’ll be looking at here may be one of the methods by which the market will be blown open for these green and durable fibers.

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Cosmic Ray Navigation

June 20, 2023 by Al Williams 19 Comments

GPS is a handy modern gadget — until you go inside, underground, or underwater. Japanese researchers want to build a GPS-like system with a twist. It uses cosmic ray muons, which can easily penetrate buildings to create high-precision navigation systems. You can read about it in their recent paper. The technology goes by MUWNS or wireless muometric navigation system — quite a mouthful.

With GPS, satellites with well-known positions beam a signal that allows location determination. However, those signals are relatively weak radio waves. In this new technique, the reference points are also placed in well-understood positions, but instead of sending a signal, they detect cosmic rays and relay information about what it detects to receivers.

The receivers also pick up cosmic rays, and by determining the differences in detection, very precise navigation is possible. Like GPS, you need a well-synchronized clock and a way for the reference receivers to communicate with the receiver.

Muons penetrate deeper than other particles because of their greater mass. Cosmic rays form secondary muons in the atmosphere. About 10,000 muons reach every square meter of our planet at any minute. In reality, the cosmic ray impacts atoms in the atmosphere and creates pions which decay rapidly into muons. The muon lifetime is short, but time dilation means that a short life traveling at 99% of the speed of light seems much longer on Earth and this allows them to reach deep underground before they expire.

Detecting muons might not be as hard as you think. Even a Raspberry Pi can do it.

Creating Lithography-Free Photonic Reprogrammable Circuits

June 20, 2023 by Maya Posch 2 Comments

The field of photonics has seen significant advances during the past decades, to the point where it is now an integral part of high-speed, international communications. For general processing photonics is currently less common, but is the subject of significant research. Unlike most photonic circuits which are formed using patterns etched into semiconductor mask using lithography, purely light-based circuits are a tantalizing possibility. This is the focus of a recent paper (press release, ResearchGate) in Nature Photonics by [Tianwei Wu] and colleagues at the University of Pennsylvania.

What is somewhat puzzling is that despite the lofty claims of this being ‘the first time’ that such an FPGA-like device has been created for photonics, this is far from the case, as evidenced by e.g. a 2017 paper by [Kaichen Dong] and colleagues (full article PDF) in Advanced Materials. Here the researchers used a slab of vanadium dioxide (VO₂) with a laser to heat sections to above 68 °C where the material transitions from an insulating to a metallic phase and remains that way until the temperature is lowered again. The μm-sized features that can be created in this manner allow for a wide range of photonic devices to be created.

A rewritable metacanvas. a) Schematic of laser writing different photonic operator patterns on a metacanvas. b) Temperature-dependentresistance of a VO2 film. c) Optical images from writing and erasing process on the metacanvas. . d) Diagram showing the mathematical matrix (F) is compiled onto a metacanvas in the form of a photonic operator for manipulation of light waveform (I ). e) Schematic of a metacanvas programmed as a beam steerer with a steering angle ϕ. (Credit: Dong et al., 2018) — A rewritable metacanvas. a) Schematic of laser writing different photonic operator patterns on a metacanvas. b) Temperature-dependent resistance of a VO2 film. c) Optical images from writing and erasing process on the metacanvas. . d) Diagram showing the mathematical matrix (F) is compiled onto a metacanvas in the form of a photonic operator for manipulation of light waveform. e) Schematic of a metacanvas programmed as a beam steerer with a steering angle ϕ. (Credit: Dong et al., 2018)

What does appear to be different with the photonic system presented by [Wu] et al. is that it uses a more traditional 2D approach, with a slab of InGaAsP on which the laser pattern is projected. Whether it is more versatile than other approaches remains to be seen, with the use of fully photonic processors in our computers still a long while off, never mind photonics-accelerated machine learning applications.