Driving the Ammonia Nitrogen Recycling with "Prussian blue"

AIST Researchers and FUSO Corporation (hereinafter referred to as "FUSO") have developed a technology to recover ammonium ions (NH₄⁺) from industrial wastewater, reducing their concentration to a level suitable for discharge into sewage. Additionally, the recovered NH₄ is concentrated to a level suitable for use as a resource.

Ammonia and other nitrogen compounds are valuable substances used in fertilizers and industries, but their discharge into the environment contributes to environmental pollution. Therefore, international efforts are being made to recover and reuse nitrogen compounds within the nitrogen cycle. Ammonia in industrial wastewater exists primarily as NH₄⁺, highlighting the need for technology that can recover and recycle it so that NH₄⁺ is not wasted.

By replacing iron in Prussian blue, a blue pigment, with zinc, we previously developed a material capable of selectively adsorbing NH₄⁺, and desorbing it into a highly concentrated solution. Now, we have developed a continuous adsorption-desorption system and demonstrated its effectiveness in recovering NH₄⁺ in actual zinc plating wastewater. We are preparing to ship samples of the adsorbent soon. The domestic wastewater treatment market is currently valued at approximately 3 trillion yen, and this technology is expected to play a significant role in this market.

Ammonia nitrogen circulation technology: a system that preferentially collects and concentrates ammonia in wastewater

New Evaluation Technique for Nanocellulose Using Machine Learning

AIST researchers have successfully developed a method to predict the specific surface area of nanocellulose from sedimentation measurements using machine learning technology. Since nanocellulose is derived from carbon-neutral biomass, it has great potential as an environmentally friendly material and is expected to contribute to the realization of a circular economy. The shape-derived properties of nanocellulose, such as fiber width, length, and specific surface area, are important parameters that determine its physical properties and quality when used as reinforcement fibers in water dispersions and composite materials. Conventional techniques used microscopic observation or gas adsorption methods for these evaluations, but the pretreatment and measurements required a great deal of time and effort (approximately 10 days). On the other hand, although the behavior of nanocellulose as it gradually settles in a water dispersion contains information on its shape, there was no effective way to extract this information. In this research, we successfully developed a prediction technique that can obtain shape-derived information easily and quickly (in about one day) by relating the sedimentation behavior and specific surface area through machine learning. This technology can be used for quality control, as it allows for easy evaluation of differences in the shape of nanocellulose. Furthermore, based on the correlation between the specific surface area and the physical properties of products using nanocellulose, this technology can be used to predict the physical properties of polypropylene/nanocellulose composite materials. As a result of the above, this technology is expected to broaden industrial application of nanocellulose throughout the industry.
Details of this technology were published online in Carbohydrate Polymer Technologies and Applications on February 4, 2025（Open Access）.

New evaluation technology based on sedimentation behavior of nanocellulose