On-chip laser frequency combs &mdash; lasers that emit multiple frequencies or colors of light simultaneously separated like the tooth on a comb &mdash; are a promising technology for a range of applications including environmental monitoring, optical computing, astronomy, and metrology. However, on-chip frequency combs are still limited by one serious problem &mdash; they are not always efficient. There are several ways to mitigate the efficiency problem, but they all suffer from trade-offs. For example, combs can either have high efficiency or broad bandwidth but not both. The inability to design an on-chip laser frequency comb that is both efficient and broad has stymied researchers for years and hindered the widespread commercialization of these devices.Now, a team from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) has developed an electro-optic frequency comb that is 100-times more efficient and has more than twice the bandwidth of previous state-of-the-art versions.&ldquo;Our device paves the way for practical optical frequency comb generators and opens the door for new applications, said&nbsp;<a href="https://www.seas.harvard.edu/directory/loncar" target="_blank">Marko Lončar,&nbsp;</a>the Tiantsai Lin Professor of Electrical Engineering at SEAS and senior author of the study.&nbsp;&ldquo;It also provides a platform to investigate new areas of optical physics.&rdquo;The research is published in&nbsp;<a href="https://www.nature.com/articles/s41566-022-01059-y.epdf?sharing_token=uPhgWeR0D2vxcIv-OMeB29RgN0jAjWel9jnR3ZoTv0PiGg0GmEmfD59hi91ED_DVhRWTQcdD4yhPmAKxTwZmcM0leN2KHawGB2NdcfjZx1vgPpBRCSVWBdx4maRTpHS1ZOBWNyOgmU7cDu7LISupGD_qInrHezLCcPDmymixq_0%3D" target="_blank">Nature Photonics</a>.&nbsp;This advancement builds upon previous research from Lončar and his team.In 2019, Lončar and his lab&nbsp;<a href="https://www.seas.harvard.edu/news/2019/03/chip-electronically-tunable-frequency-comb" target="_blank">demonstrated the first stable, on-chip frequency comb</a> that could be controlled with microwaves. This so-called electro-optical frequency comb, built on the lithium niobate platform pioneered by Lončar&rsquo;s lab, spanned the entire telecommunications bandwidth but was limited in its efficiency. &nbsp;In 2021, the team&nbsp;<a href="https://www.seas.harvard.edu/news/2021/11/shifting-colors-chip-photonics" target="_blank">developed a coupled resonators</a> device to control the flow of light, and used them to demonstrate&nbsp;&nbsp;on-chip frequency shifters &ndash; a device that can change the color of light with nearly 100% efficiency.&nbsp;The latest research applies the two concepts to address the challenge in resonator based electro-optic frequency combs &ndash; efficiency-bandwidth tradeoff.&ldquo;We demonstrated that by combining these two approaches &mdash; the coupled resonator with the electro-optical frequency comb &mdash; we could improve the efficiency a lot without sacrificing bandwidth. In fact, we actually improved bandwidth,&rdquo; said Yaowen Hu, a research assistant at SEAS and first author of the paper.&ldquo;We found that when you improve the performance of the comb source to this level, the device starts operating in an entirely new regime that combines the process of electro-optic frequency comb generation with the more traditional approach of a Kerr frequency comb,&rdquo; said Mengjie Yu, a former postdoctoral fellow at SEAS and co-first author of the paper.&nbsp;Yu is currently an Assistant Professor at the University of Southern California.&nbsp;This new comb can generate ultrafast femtosecond pulses at high power. Together with the high-efficiency and broadband, this device can be useful for applications in astronomy, optical computing, ranging and optical metrology.&nbsp;The research was co-authored by Brandon Buscaino, Neil Sinclair, Di Zhu, Rebecca Cheng, Amirhassan Shams-Ansari, Linbo Shao, Mian Zhang and Joseph M. Kahn.

A new on-chip frequency comb combines a coupled resonator with an electro-optical frequency comb to improve the efficiency of frequency combs and improve the bandwidth. (Credit: Yiqing Pei/Harvard SEAS)

Device opens the door to applications in optical communications, sensing, and the search for exoplanets

New on-chip frequency comb is 100x more efficient

Researchers from ITMO&rsquo;s Faculty of Nanoelectronics together with their colleagues from the Lebedev Physical Institute, D. Mendeleev University of Chemical Technology of Russia, and the National Research Nuclear University MEPhI have conducted a study that showed that nanolattices possess high spectral selectivity. Their layers can be used as color filters for specific wavelength ranges, and one glass plate can contain records of up to six layers. Using this technology, the scientists were able to implement dispersive birefringent filters, which have multiple applications. For instance, they can be used to create AR/VR color displays or novel biochemical sensors for bacterial analysis.<h2 dir="ltr" id="isPasted">All in one</h2>For two years, the <a href="https://news.itmo.ru/en/science/photonics/news/9811/" target="_blank">researchers</a> have been modeling, designing, and testing a wide range of integral optical elements to build a waveguide holographic periscope, an essential part of next-gen AR/VR glasses. Current headsets tend to limit a user&rsquo;s field of view, while smart glasses can complement the virtual space and inform users about surrounding objects and environment.In order to create such a device, scientists have to address a number of issues associated with its creation: for instance, to figure out how to transmit a color image via glasses. For that to happen, the researchers have <a href="https://news.itmo.ru/en/science/photonics/news/10123/" target="_blank">improved</a> a laser-initiated microplasma (LIMP) glass-processing method that allows them to create laser beam converters for the application of submicron periodic structures. After that, the team applied laser writing to obtain bulk periodic structures inside a glass with modified refractive index. Their next step was to create submicron diffraction gratings on a single glass plate, which will input and output AR/VR images via diffracted orders.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1652319436706-ezgif-1-0d53f13b63.gif" style="width: 766px;" class="fr-fic fr-dib">Writing of integrated elements inside a glass. Video courtesy of the researchers&nbsp;<blockquote id="isPasted">&ldquo;We aim to develop the basics of using laser writing of integrated elements in photonic materials. Current sets of optical components (a mirror, a light source, filters, converters, etc,) are so massive that they occupy the entire optical table. We, in turn, want to create compact optical integrated circuits that can fit in the palm of a user, also called chip-scale devices. For instance, we record light-transmitting and selectively reflective elements on a glass matrix at several levels in 3D. Such a technology is compact and protected from external threats&rdquo; comments Roman Zakoldaev, a researcher at ITMO&rsquo;s Faculty of Nanoelectronics and an author of the paper.</blockquote><h2 dir="ltr">Research process</h2>The research involved several teams. The first team was in charge of direct laser writing in optical materials, the second one measured the morphological and physical properties of the produced micro- and nanostructures, and the third worked on the theoretical modeling of physical and optical processes.The first stage of the study included an investigation of direct laser writing of wide-ranging optical materials, such as quartz, multicomponent and nanoporous glasses, and calcium fluoride. The researchers made use of both materials already used in production and the promising ones. Among the latter are nanoporous matrices, which are developed and used by <a href="https://news.itmo.ru/en/science/new_materials/news/9883/" target="_blank">ITMO scientists</a>. These media are used in laser writing because they help increase the efficiency of phase incursion within a periodic bulk structure with submicron periods.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1652319489668-1256302.jpg" style="width: 766px;" class="fr-fic fr-dib">Alexey Rupasov studies the morphology of nanolattices with a period of 390 nm for further modeling. Photo courtesy of the researchers&nbsp;Then, the researchers tested different laser writing modes in quartz glass. By experimenting with the duration of femtosecond laser pulses, fluence, and the wavelength of laser radiation, they learned that such characteristics as phase incursion and light scattering affect a spectral range of the resulting filters. In other words, the scientists discovered laser writing modes, which allowed them to obtain structures with the highest phase incursion and the lowest light scattering.<h2 dir="ltr">For both physics and biochemistry</h2>As a result, the researchers were able to identify a relationship between phase incursion and the modes of laser writing in quartz glass. Phase incursion is a change of the refractive index in quartz glass due to the formation of periodic structures with various refractive indices (glass-air). These zones display a relatively high phase incursion (up to 166 degrees) per layer, which makes it close to a half-wave plate. The discovered modes make it possible to implement any phase optical element inside a glass. Due to the high phase incursion, these structures get colorized with birefringence colors when placed between polarizers.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1652319521967-1256301.jpg" style="width: 766px;" class="fr-fic fr-dib">Birefringence regions inside quartz glass in crossed and parallel polarizers. Photo courtesy of the researchers&nbsp;<blockquote id="isPasted">&ldquo;Multilayer writing makes it possible to create dispersive birefringent filters in a given spectral range, which paves the way for future compact sensors that will be based on the principles of integrated optics and will include the spectral analysis function. While the modern market does have dispersive birefringent filters, they are still separate optical devices. And we are working our way to offer a single glass plate that will integrate all these filters in one plate. Since our study is fundamental, we prepared a model, which showed us that our technology can be used to record multilayer structures in the visible spectral range,&rdquo; explains Alexey Rupasov, a researcher at ITMO&rsquo;s Faculty of Nanoelectronics and an author of the research.&nbsp;</blockquote>The results obtained provide a vital framework for radiation processing in AR devices, in which images are generated using three RGB coordinates. Diffraction gratings will help ensure input and output of radiation at various wavelengths. In the future, there might be a single glass plate with three types of periodic structures, which will adjust the radiation to RGB wavelengths and colorize the images.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1652319546675-1256300.jpg" style="width: 766px;" class="fr-fic fr-dib">The image of dispersive filters, the cell transmission spectrum, and a layout of a diagnostic microfluidic chip. Illustrations from the article published in Laser Physics Letters&nbsp;The team also proposed a concept and a design of an integrated biochemical sensor for bacterial analysis. The suggested idea can replace the existing dispersive birefringent filters, which can only analyze wavelengths separately, by offering an opportunity to conduct all studies using a single lab-on-a-chip &ndash; a glass surface exposed to polarized light. The technology will include an internal or an external microfluidic chip, within which there&rsquo;ll be liquids with a detectable substance, such as bacteria. Each of these substances respond to a specific spectral range and a wavelength. On top of that, they can absorb light or fluoresce. The reactions show what is happening inside the chip.<hr><a href="https://iopscience.iop.org/article/10.1088/1612-202X/ac6806/meta" target="_blank">Reference</a>: Sergey Kudryashov, Pavel Danilov, Alexey Rupasov, Mikhail Smayev, Nikita Smirnov, Vladimir Kesaev, Andrey Putilin, Michael Kovalev, Roman Zakoldaev and Sergey Gonchukov. Direct Laser Writing Regimes for Bulk Inscription of Polarization-Based Spectral Microfilters and Fabrication of Microfluidic Bio/Chemosensor in Bulk Fused Silica (Laser Physics Letters, 2022).Translated by <a href="https://news.itmo.ru/en/profile/61/" rel="noopener noreferrer" target="_blank">Marina Belyaeva</a> and originally published by <a data-saferedirecturl="https://www.google.com/url?q=https://news.itmo.ru/en/&source=gmail&ust=1652405976196000&usg=AOvVaw0iY5hMnJ3kgyjjWceggIw1" href="https://news.itmo.ru/en/" target="_blank">ITMO.NEWS</a>

Written micro- and nano-periodic structures inside a glass. Photo courtesy of the researchers

Researchers have conducted a study that showed that nanolattices possess high spectral selectivity. Their layers can be used as color filters for specific wavelength ranges, and one glass plate can contain records of up to six layers.

Researchers Explore Direct Laser Writing of Nanolattices

The growing energy demanded by deep-learning systems hampers their potential for widespread deployment against a range of problems, but a team of scientists at Cornell University and NTT Research Inc. believe they have an answer: Creating physical neural networks (PNNs), including an oscillating metal plate which can recognise hand-written digits.“By breaking the traditional software–hardware division,” the team explains, “PNNs provide the possibility to opportunistically construct neural network hardware from virtually any controllable physical system” — and in doing so offer improved scalability, energy efficiency, and performance than traditional deep-learning systems.<h1>Beating Moore’s law</h1>With the computational demand of deep neural network (DNN) models outstripping the growth in computational power available from each successive generation of hardware — as projected by Moore's Law, which sets out a rough doubling of computational capability every 18 months — there’s considerable interest in DNN-specific acceleration hardware. In effect, that’s what Logan G. Wright and colleagues have created — except they’re not microchips but physical systems based on electronics, optics, and even mechanics.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1645731557186-pnn4.jpg" style="width: 300px;" class="fr-fic fr-dib" alt="An illustration showing the operation of a traditional neural network versus a physical neural network in the classification of a handwritten digit input.">Physical neural networks could, researchers claim, offer dramatic improvements in efficiency and performance compared to traditional approaches. The idea: Training the physical transformations available in hardware directly so as to perform computation, turning something as simple as a vibrating metal plate into a device capable of rapidly and accurately classifying handwritten digits. In doing so, the team claims it’s possible to improve the performance and reduce the energy requirements of such computations.Designed as a universal framework for physical neural networks, the team’s paper details the creation of a number of examples — all enabled using a hybrid in-situ in-silico algorithm dubbed Physics Aware Training (PAT), which takes the backpropagation algorithm which has already proven it use in more traditional neural network designs and allows it to be executed on any sequence of physical input/output transformations.<h1>Processing with physics</h1>It’s a concept the team demonstrates with a series of prototypes. The most recognizable as something which could potentially run a neural network workload is the electronic prototype, though it uses a non-linear electronic oscillator rather than a microprocessor. The second switches out the electronics for optics, using an ultra-fast optical second-harmonic generator (SHG). The final prototype seems far removed from anything that could be considered a computer: A vibrating metal plate.The researchers admit that what they have created is “a radical departure from traditional hardware,” but claim that PNNs can be easily integrated into a modern machine learning environment — even to the point of combining them with conventional hardware to create physical-digital hybrid architectures. In doing so, they claim the potential to improve both the efficiency and speed of machine learning “by many orders of magnitude.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1645731579302-pnn3.jpg" style="width: 300px;" class="fr-fic fr-dib" alt="A digtam showing how Physics Aware Training (PAT) trains a physical neural network, along with accuracy improvements as training progresses.">The Physics Aware Training (PAT) system is designed to be universal, applicable to any physical system that can be tuned. Each device is trained using the Physics Aware Training (PAT) approach: training data is input to the physical system alongside trainable parameters; a forward pass sees the physical system apply its transformation, whatever that may be, to produce an output; the output is compared with the target output to calculate error; then a differentiable digital model estimates the gradient loss with respect to controllable parameters; finally, the parameters are updated based on the inferred gradient.By repeating the process multiple times, the error level is reduced — and the PNN trained. To prove that claim, the team used the optical PNN to carry out vowel classification — outputting a blue spectrum which is summed via digital computation into spectral bins, and the predicted vowel picked via the bin with the maximum energy. In testing, the device reached an accuracy level of 93 per cent, well above the 14 per cent possible through random guessing.<h1>Thinking with metal</h1>It’s the metal plate which captures the attention, however. Driven by time-varying forces which encode input data and trainable parameters, the vibrating plate — again trained via PAT, in the same manner as the optical vowel classifier — reached 87 per cent accuracy for the Modified National Institute of Standards and Technology (MNIST) handwritten digit classification task.This, the team points out, proves beyond doubt that it’s possible to use a wide range of physical systems not normally associated with computation for running deep neural network calculations. Further, they say that it offers — “in principle,” at least — the potential to do so “orders of magnitude faster and more energy-efficiently than conventional hardware.”There are, however, some caveats to the team’s approach. The biggest is simply that some physical systems may be better suited to particular computations than others, particularly where the physical system shares the same or similar constraints to the computation class. Another is that the benefits of PNNs are seen only during inferencing, rather than training — something the researchers say may lead to PNN deployment to focus on their use as an addition to, rather than replacement for, conventional hardware.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1645731588769-pnn2.jpg" style="width: 300px;" class="fr-fic fr-dib" alt="Images of three types of physical neural networks - mechanic, electronic, and optic - along with their oepration and performance in handwritten digit classification.">The team built three implementations, a mechanical, an electronic, and an optical PNN, plus an additional hybrid digital-physical implementation. For this the team returned to the optical ultra-fast SHG implementation to create a hybrid PNN, featuring trainable digital input layers followed by trainable ultra-fast SHG transformations. The results prove the team’s point: The accuracy on the MNIST test rose from around 90 per cent using the PNN alone to 97 per cent. “This,” the team claims, “illustrates how a hybrid physical-digital PNN can automatically learn to offload portions of a computation from an expensive digital process to a fast, energy-efficient physical co-processor.”In addition to improving the efficiency and performance of deep neural networks, the team proposes PNNs as having potential for putting computational capabilities directly into sensors — offering the example of a low-power microphone-coupled circuit which could recognize wake-words and only wake up a high-power digital system when detected — as well as into robotics and materials.The team’s work has been published under open-access terms in the journal <cite><a href="https://www.nature.com/articles/s41586-021-04223-6" target="_blank">Nature</a></cite>; an open-source implementation of the Physics Aware Training (PAT) system, released under the Creative Commons Attribution 4.0 International license, is available <a href="https://github.com/mcmahon-lab/Physics-Aware-Training" target="_blank">on GitHub</a> with an open-access data repository <a href="https://zenodo.org/record/4719150" target="_blank">published to Zenodo</a>.<h1>Reference</h1>Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein, Tianyu Wang, Darren T. Schachter, Zoey Hu, and Peter L. McMahon: <cite>Deep physical neural networks trained with backpropagation, Nature Iss. 601</cite>. DOI <a href="https://doi.org/10.1038/s41586-021-04223-6" target="_blank">10.1038/s41586-021-04223-6</a>.

While it may not look like it, this oscillating metal plate serves as a computer running a digital-classification network.

Designed to address the exploding computational demands of deep neural networks, physical neural networks (PNNs) branch out from electronics into optics and even mechanics to boost performance and efficiency.

Physics-aware training turns physical systems, including an oscillating metal plate, into deep neural networks

The polarisation controller is a highly technical device for controlling the polarisation state of light. Polarisation specifies the geometric orientation of light waves, so controlling this is relevant to a variety of applications such as optical communication, fibre optic sensors, PDL measurement and component testing. The device produced by Thorlabs optimises the alignment of light as it travels through various sensitive optical components by spooling, looping and twisting a fibre using the spools of a polarisation paddle. Using a motor for the polarisation paddle gives better repeatability of the looping and twisting of the fibre when compared to doing it manually.Generally used by laboratories, the specialist equipment is produced in low volumes and needs to meet exacting standards.Thorlabs needed to outsource parts for the housing for the motors and other areas. Before moving to final production, the company needed to test the designs to confirm material selection, motor performance, screw hole sizes and other factors.<blockquote><blockquote><blockquote>“The fact that Protolabs could provide design feedback within a couple of hours after we uploaded our CAD into its design for manufacturability analysis software, rapid prototyping of the parts, and a low volume injection moulding solution that keeps the cost per part down was invaluable.”</blockquote></blockquote></blockquote>Philip Storey, senior engineering buyer at Thorlabs, commented: “The new product is highly specialised equipment that needed a lot of development and testing before production. The fact that Protolabs could provide design feedback within a couple of hours after we uploaded our CAD into its design for manufacturability analysis software, rapid prototyping of the parts, and a low volume injection moulding solution that keeps the cost per part down was invaluable.“This joined up thinking makes a lot of sense for specialist manufacturers who need low to medium volumes of parts like us. &nbsp;It also meant that that the prototype designs and material had the final production process in mind.”Protolabs produced a number of prototypes using stereolithography 3D printing. A process using thermoset resins cured with ultraviolet light, it offers a broad material selection with a large range of tensile strengths, tensile and flexible moduli and elongation at break. &nbsp;By combining a good surface finish and high feature resolution, stereolithography can produce parts that mimic injection moulding for performance and appearance.Thorlabs planned to injection mould the final parts using Black ABS as the material because it is a tough plastic with a natural good surface finish. Black ABS is also a good electrical insulator making it an ideal choice for the application. &nbsp;ABS-like black was used for the prototyping to test their designs, since it not only mimics true ABS’s mechanical properties, but its deep black colour and glossy facing surfaces offer the appearance of a moulded part.Each new prototype design was delivered in less than a week allowing Thorlabs to quickly test and evaluate every new iteration so that they could ensure that it was right before committing to production.Once the final design for production was ready, the company placed an initial order of 75 parts for injection moulding. &nbsp;Conventional steel moulds would take between 12 to 15 weeks to produce and are expensive leading to a costly price per part solution for the sort of volumes that the company needed.&nbsp;Protolabs’ on-demand injection moulding solution offered them an alternative. By using aluminium moulds, they could lower the cost and the price per part and deliver the final parts in a fraction of the time taken by other suppliers. &nbsp;From production of the moulds to delivery of the ABS plastic parts for the first order took less than five weeks. &nbsp;Subsequent orders of 100, 150 and 200 parts were all delivered in less than a week.Protolabs account manager Jamie Philips explained: “Traditionally injection moulding is seen as a solution for mass produced parts produced in tens of thousands or hundreds of thousands. Yet there are many electronic manufacturing companies that want to use injection moulding in lower volumes to produce parts for specialist products.&nbsp;“By using aluminium moulds we can reduce the time and cost for lower volume production so that specialist manufacturers can place small and medium sized orders at a lower price per part. If you need anything from a few hundred up to twenty thousand injection moulded parts, then our on-demand manufacturing service will save you money and time.”Offering rapid prototyping and manufacturing using a range of different production technologies means that <a href="https://www.protolabs.co.uk/?utm_source=wevolver&utm_medium=referral&utm_campaign=uk-case-study-0521&utm_content=thorlabs-case-study-pl-link-0521" rel="noopener noreferrer" target="_blank">Protolabs</a> offers a complete product development solution for outsourced parts that can cut lead times by weeks or even months. &nbsp;

Thorlabs reduced the time to market for its new motorised polarisation controller by several months using Protolabs design analysis, prototyping and on demand manufacturing for its outsourced parts.

Protolabs' combined prototyping and production solution saves months of product development for Thorlabs

This article was discussed in our <a data-saferedirecturl="https://www.google.com/url?q=https://www.wevolver.com/profile/the.next.byte&source=gmail&ust=1640817319915000&usg=AOvVaw2nPRdRwJrXSYYeM9RCFu5x" href="https://www.wevolver.com/profile/the.next.byte" id="isPasted" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/6a1f8d34-3956-4645-9eb4-918c526c5f45?dark=false" class="fr-draggable"></iframe>The full article will continue below.<hr>Scientists from the<a href="https://distap.mit.edu/" target="_blank">&nbsp;</a><a href="https://distap.mit.edu/" target="_blank">Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP)</a> research group at the<a href="https://smart.mit.edu/" target="_blank">&nbsp;</a><a href="https://smart.mit.edu/" target="_blank">Singapore-MIT Alliance for Research and Technology (SMART)</a>, MIT’s research enterprise in Singapore, have engineered a novel type of plant nanobionic optical sensor that can detect and monitor, in real time, levels of the highly toxic heavy metal arsenic in the underground environment. This development provides significant advantages over conventional methods used to measure arsenic in the environment and will be important for both environmental monitoring and agricultural applications to safeguard food safety, as arsenic is a contaminant in many common agricultural products such as rice, vegetables, and tea leaves.This new approach is described in a paper titled <a href="https://onlinelibrary.wiley.com/doi/10.1002/adma.202005683" target="_blank">“Plant Nanobionic Sensors for Arsenic Detection,”</a> published recently in Advanced Materials. The paper was led by Tedrick Thomas Salim Lew, a recent graduate student of MIT, and co-authored by Michael Strano, co-lead principal investigator of DiSTAP and the Carbon P. Dubbs Professor at MIT, as well as Minkyung Park and Jianqiao Cui, both graduate students at MIT.Arsenic and its compounds are a serious threat to humans and ecosystems. Long-term exposure to arsenic in humans can cause a wide range of <a href="https://www.who.int/news-room/fact-sheets/detail/arsenic#:~:text=In%20addition%20to%20skin%20cancer,water%20is%20carcinogenic%20to%20humans." target="_blank">detrimental health effects</a>, including<a href="https://www.who.int/news-room/fact-sheets/detail/arsenic#:~:text=In%20addition%20to%20skin%20cancer,water%20is%20carcinogenic%20to%20humans." target="_blank">&nbsp;</a>cardiovascular disease such as heart attack, diabetes, birth defects, severe skin lesions, and numerous cancers including those of the skin, bladder, and lung. Elevated levels of soil arsenic as a result of anthropogenic activities such as mining and smelting are also harmful to plants, inhibiting growth and resulting in substantial crop losses.Food crops can absorb arsenic from the soil, leading to contamination of food and produce consumed by humans. Arsenic in underground environments can also contaminate groundwater and other underground water sources, the long-term consumption of which can cause severe health issues. As such, developing accurate, effective, and easy-to-deploy arsenic sensors is important to protect both the agriculture industry and wider environmental safety.The novel optical nanosensors exhibit changes in their fluorescence intensity upon detecting arsenic. Embedded in plant tissues, with no detrimental effects on the plant, these sensors provide a nondestructive way to monitor the internal dynamics of arsenic taken up by plants from the soil. This integration of optical nanosensors within living plants enables the conversion of plants into self-powered detectors of arsenic from their natural environment, marking a significant upgrade from the time- and equipment-intensive arsenic sampling methods of current conventional methods.“Our plant-based nanosensor is notable not only for being the first of its kind, but also for the significant advantages it confers over conventional methods of measuring arsenic levels in the below-ground environment, requiring less time, equipment, and manpower," says Lew. "We envision that this innovation will eventually see wide use in the agriculture industry and beyond. I am grateful to SMART DiSTAP and the Temasek Life Sciences Laboratory (TLL), both of which were instrumental in idea generation and scientific discussion as well as research funding for this work.”Besides detecting arsenic in rice and spinach, the team also used a species of fern, Pteris cretica, which can hyperaccumulate arsenic. This fern species can absorb and tolerate high levels of arsenic with no detrimental effect — engineering an ultrasensitive plant-based arsenic detector, capable of detecting very low concentrations of arsenic, as low as 0.2 parts per billion. In contrast, the regulatory limit for arsenic detectors is 10 parts per billion. Notably, the novel nanosensors can also be integrated into other species of plants. The researchers say this is the first successful demonstration of living plant-based sensors for arsenic and represents a groundbreaking advancement that could prove highly useful in both agricultural research (e.g., to monitor arsenic taken up by edible crops for food safety) and general environmental monitoring.Previously, conventional methods of measuring arsenic levels included regular field sampling, plant tissue digestion, extraction, and analysis using mass spectrometry. These methods are time-consuming, require extensive sample treatment, and often involve the use of bulky and expensive instrumentation. The new approach couples nanoparticle sensors with plants' natural ability to efficiently extract analytes via the roots and transport them. This allows for the detection of arsenic uptake in living plants in real time, with portable, inexpensive electronics such as a portable Raspberry Pi platform equipped with a charge-coupled device camera akin to a smartphone camera.Co-author, DiSTAP co-lead principal investigator, and MIT Professor Michael Strano adds, “This is a hugely exciting development, as, for the first time, we have developed a nanobionic sensor that can detect arsenic — a serious environmental contaminant and potential public health threat. With its myriad advantages over older methods of arsenic detection, this novel sensor could be a game-changer, as it is not only more time-efficient, but also more accurate and easier to deploy than older methods. It will also help plant scientists in organizations such as TLL to further produce crops that resist uptake of toxic elements. Inspired by TLL’s recent efforts to create rice crops which take up less arsenic, this work is a parallel effort to further support SMART DiSTAP's efforts in food security research, constantly innovating and developing new technological capabilities to improve Singapore’s food quality and safety.”The research is carried out by SMART and supported by the National Research Foundation (NRF) Singapore under its Campus for Research Excellence And Technological Enterprise (CREATE) program.Led by MIT's Strano and Singapore co-lead principal investigator Professor Chua Nam Hai, DiSTAP is one of the five Interdisciplinary Research Groups (IRGs) in SMART. The DiSTAP program addresses deep problems in food production in Singapore and the world by developing a suite of impactful and novel analytical genetic and biosynthetic technologies. The goal is to fundamentally change how plant biosynthetic pathways are discovered, monitored, engineered, and ultimately translated to meet the global demand for food and nutrients. Scientists from MIT, TTL, Nanyang Technological University, and National University of Singapore are collaboratively developing new tools for the continuous measurement of important plant metabolites and hormones for novel discovery, deeper understanding and control of plant biosynthetic pathways in ways not yet possible, especially in the context of green leafy vegetables; leveraging these new techniques to engineer plants with highly desirable properties for global food security, including high yield density production, drought and pathogen resistance and biosynthesis of high-value commercial products; developing tools for producing hydrophobic food components in industry-relevant microbes; developing novel microbial and enzymatic technologies to produce volatile organic compounds that can protect and/or promote growth of leafy vegetables; and applying these technologies to improve urban farming.<hr>SMART was founded by MIT in partnership with the NRF in 2007 and is the first entity in CREATE. SMART serves as an intellectual and innovation hub for cutting-edge research between MIT and Singapore, and currently comprises an Innovation Center and five IRGs: Antimicrobial Resistance, Critical Analytics for Manufacturing Personalized-Medicine, DiSTAP, Future Urban Mobility, and Low Energy Electronic Systems.

A novel type of plant nanobionic optical sensor can detect and monitor, in real-time, levels of arsenic in the underground environment. Credits:Image courtesy of Singapore-MIT Alliance for Research and Technology; Christine Daniloff, MIT

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