Low-Cost Microphone Listens with Light

Traditional microphones capture tiny vibrations on the surfaces of objects caused by sound waves and turn them into audible signals. A microphone developed by researchers at the Beijing Institute of Technology operates differently: Rather than sound, the microphone listens with light. This light-enabled microphone is able to pick up sounds in situations where traditional microphones are ineffective, such as through a glass window.

According to the researchers, the technique can work on everyday objects such as leaves and paper.

“The new technology could potentially change the way we record and monitor sound, bringing new opportunities to many fields, such as environmental monitoring, security, and industrial diagnostics,” said research team leader Xu-Ri Yao. “For example, it could make it possible to talk to someone stuck in a closed-off space like a room or a vehicle.”

While this technology has seen previous exploration, its earlier iteration has required expensive optical equipment. The current project sought to simplify the process by using single-pixel imaging, which would make this technology more accessible. Single-pixel imaging captures images using just one light detector instead of a traditional camera sensor with millions of pixels.

Rather than recording an image all at once, the scene's light is modulated using time-varying structured patterns by a spatial light modulator (SLM), and the single-pixel detector measures the amount of modulated light for each pattern. A computer then uses these measurements to reconstruct information about the object.

The team used a high-speed SLM to encode light reflected from the vibrating surface. The sound-induced motion causes subtle changes in light intensity that were captured by the single-pixel detector and then decoded into audible sound. Team members then used Fourier-based localization methods to track object vibrations, which enabled efficient and precise measurement of minute variations. Single-pixel detectors record the information in a relatively small amount of data, which means the data can be transferred between devices quickly.

The researchers tested the microphone, using it to reconstruct Chinese and English pronunciations of numbers as well as a segment from Beethoven’s Für Elise. They used a paper card and a leaf as vibration targets, placing them 0.5 m away from the objects while a nearby speaker played the audio. The system was able to reconstruct clear and intelligible audio, with the paper card producing better results than the leaf. Low-frequency sounds (<1 kHz) were accurately recovered, while high-frequency sounds (>1 kHz) showed slight distortion that improved when a signal processing filter was applied. Tests of the system's data rate showed it produced 4?MB/s, a rate sufficiently low to minimize storage demands and allow for long-term recording.

Future plans with this technology are human pulse and heart rate detection, as well as a wider range for long-distance sound detection.

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Hybrid Material Achieves Fast, Stable Phosphorescent Emission for OLEDs

A hybrid material made from organic chromophores and transition metal dichalcogenides (TMDs) can produce stable, fast, phosphorescent light emission for OLED displays. The new hybrid, developed by a team co-led by the University of Michigan (U-M), could replace the heavy metal components currently used to improve efficiency, brightness, and color range in OLED devices.

Organic materials with room-temperature phosphorescence are an appealing alternative to heavy metals because of their tunable luminescent properties, large design window, environmentally-friendly components, and economical production cost.

Phosphorescence is 3 times more energy-efficient than fluorescence, but happens more slowly. To keep pace with modern displays, which operate at 120 frames per second, phosphorescence must occur in microseconds. The metals used in OLEDs, like iridium and platinum, enable phosphorescence to take place in microseconds instead of milliseconds. The large atomic nucleus of the heavy metal generates a magnetic field that causes the excited electrons to emit light faster as they go from the excited to the ground state.

The researchers developed an alternative strategy for developing emitters for phosphorescence by creating heterostructures of organic chromophores and TMDs.

The heterostructures were made of diethyl 2,5-dihydroxy terephthalate (DDT), an organic fluorophore, using various TMDs. A 2D layer of molybdenum (MoS2) and sulfur is positioned near a similarly thin layer of the organic light-emitting material, achieving physical proximity without any chemical bonding. Light emission occurs entirely within the organic material, without the need for weak, metal-organic ligand bonding.

The team observed the TMD-induced photophysical variations in the DDT and found that the DDT on the TMDs emitted microsecond phosphorescence at room temperature. It further found that spin-orbit couplings of the DDT were enhanced by the through-space, spin-orbit proximity effect of the TMDs in the heterostructures.

The hybrid construction increased light emission by 1000 times, achieving speeds fast enough for modern displays. “We found a way to make a phosphorescent organic molecule that can emit light on the microsecond scale, without including heavy metals in the molecular framework,” professor Jinsang Kim said.

Phosphorescent OLEDs that rely on heavy metals also use the metals to help produce color. The weak chemical bonds between the metal and the organic material can break apart when two excited electrons come into contact, dimming the pixel.

Pixel burnout in high-energy blue light has yet to be resolved, but the researchers hope their new design approach will contribute to stable, blue phosphorescent pixels. Currently, OLEDs use phosphorescent red and green pixels and fluorescent blue pixels, avoiding blue pixel burnout at the expense of lowering energy efficiency.

When the researchers analyzed the molecular hybrid system, they made an unusual discovery — the system appeared to break a rule of quantum mechanics.

Paired electrons sharing an orbital seemed to have a combined spin under dark conditions, suggesting a “forbidden” triplet state, when instead their spins should have cancelled one another out. According to a principle of quantum mechanics, the Pauli Exclusion Principle, an electron and its partner in the ground state must spin in opposite directions.

“We don’t yet fully understand what causes this triplet character in the ground state because this violates the Pauli Exclusion Principle,” Kim said. “That’s why we have a lot of questions about what really makes that happen.”

The researchers plan to explore how the hybrid material achieves triplet character ground states, while also pursuing potential spintronics device applications. The team has applied for patent protection with the assistance of U-M Innovation Partnerships and is seeking partners to create devices using the hybrid material.

In addition to the team from U-M, researchers from Inha University; Sungkyunkwan University; the University of California, Berkeley; and Dongguk University contributed to the study.


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Imaging Technique Provides Superresolution and High SNR for Thick Samples

The creation of sharp, detailed images of thick biological samples, such as those of human tissue, are now possible using an optical microscopy method developed by researchers at the Italian Institute of Technology (IIT). The team aimed to achieve the full resolution and signal to noise (SNR) benefits of image scanning microscopy (ISM). At the same time, it wanted to improve optical sectioning for complex, high-density samples.

The resulting imaging technique, which the researchers named superresolution sectioning image scanning microscopy (s2ISM), could help scientists gain insight into the aging process and the origin of certain diseases by studying the biomolecular processes inside living cells.

The technique reconstructs an image with digital and optical superresolution, high SNR, and enhanced optical sectioning from single-plane acquisition. It provides superresolution and optical sectioning simultaneously.

An instrument that acts like a light scalpel penetrates the sample deeply and observes the sample without damaging it. A small array of sensors captures the light at the point where it hits, and captures the various ways the light spreads in the sample. Once this information is recorded, a reconstruction algorithm processes the information, identifying the path of the light through the sample and producing sharper, better-sectioned images, without losing signal quality.

“The optical microscope used is equipped with an array of [single-photon avalanche diode] detectors, capable of detecting the arrival of individual photons with very high spatial and temporal precision,” researcher Alessandro Zunino said. “This characteristic not only improves the resolution and optical sectioning, but also enables advanced techniques such as fluorescence lifetime, which are fundamental to explore molecular dynamics in living tissues and to provide functional as well as structural information.”

Previous approaches to optical microscopy made it difficult to observe thick samples in detail, because the contrast in the image was hindered by the high density of the samples’ structures.

“What we did was rethink the way microscopes measure the light that hits the samples under observation, improving both the spatial resolution and the contrast when studying thick tissues, where background light would normally overpower their structure, creating noise in the images,” researcher Giuseppe Vicidomini, who coordinated the study, said.

The researchers formulated a comprehensive theoretical framework for their approach and validated the approach with images of biological samples captured using a custom setup equipped with a SPAD array detector. They demonstrated the s2ISM technique by exciting fluorescence emission in both linear and nonlinear regimes. Also, they generalized the reconstruction algorithm for fluorescence lifetime imaging.

The s2 microscopy method requires no changes in the optical system and can be extended to any laser scanning microscopy technique.

The new microscopy technique has many potential applications. For example, it could be used to study brain tissue, tumors, organoids, and other biological systems and observe the processes of living cells to better understand disease progression. In the pharmaceutical field, the technique could be used to visualize in real time how drugs interact with living biological tissues, speeding the discovery of new treatments.

The s2 technique is open-access and the code is provided as an open-source Python package; any laboratory can adopt, modify, and apply this technique at no cost and without the need for complex equipment. To simplify the application of s2ISM, the researchers have proposed a rigorous strategy to automatically extract the relevant parameters needed to run the algorithm.

The team hopes that making the software and data available for broad, rapid dissemination will encourage further innovation within the scientific community, especially in the fields of optical microscopy and life sciences.


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‘Self-Driving’ Microscope Predicts Neurodegenerative Disease

The accumulation of misfolded proteins in the brain is central to the progression of neurodegenerative diseases such as Huntington’s, Alzheimer’s, and Parkinson’s. But to the human eye, proteins that are destined to form harmful aggregates are indistinguishable from normal proteins. The formation of such aggregates also tends to happen randomly and relatively rapidly — on the scale of minutes. The ability to identify and characterize protein aggregates is essential for understanding and fighting neurodegenerative diseases.

Now, using deep learning, École Polytechnique Fédérale de Lausanne (EPFL) researchers have developed a “self-driving” imaging system that leverages multiple microscopy methods to track and analyze protein aggregation in real time — and even anticipate it before it begins. In addition to maximizing imaging efficiency, the approach minimizes the use of fluorescent labels, which can alter the biophysical properties of cell samples and impede accurate analysis.

“This is the first time we have been able to accurately foresee the formation of these protein aggregates,” said recent EPFL Ph.D. graduate Khalid Ibrahim. “Because their biomechanical properties are linked to diseases and the disruption of cellular function, understanding how these properties evolve throughout the aggregation process will lead to fundamental understanding essential for developing solutions.”

The project is the result of a longstanding collaboration between the labs of Aleksandra Radenovic, head of the Laboratory of Nanoscale Biology and Hilal Lashuel of the school of sciences. The work unites complementary expertise in neurodegeneration and advanced live-cell imaging technologies.

“This project was born out of a motivation to build methods that reveal new biophysical insights, and it is exciting to see how this vision has now borne fruit,” Radenovic said.

In their first collaborative effort, led by Ibrahim, the team developed a deep learning algorithm that was able to detect mature protein aggregates when presented with unlabeled images of living cells. The new study builds on that work with an image classification version of the algorithm that analyzes such images in real time: when this algorithm detects a mature protein aggregate, it triggers a Brillouin microscope, which analyzes scattered light to characterize the aggregates’ biomechanical properties like elasticity.

Normally, the slow imaging speed of a Brillouin microscope would make it a poor choice for studying rapidly evolving protein aggregates. But thanks to the EPFL team’s AI-driven approach, the Brillouin microscope is only switched on when a protein aggregate is detected, speeding up the entire process while opening new avenues in smart microscopy.

“This is the first publication that shows the impressive potential for self-driving systems to incorporate label-free microscopy methods, which should allow more biologists to adopt rapidly evolving smart microscopy techniques,” Ibrahim said.

Because the image classification algorithm only targets mature protein aggregates, the researchers still needed to go further if they wanted to catch aggregate formation in the act. For this, they developed a second deep learning algorithm and trained it on fluorescently labelled images of proteins in living cells. This “aggregation-onset” detection algorithm can differentiate between near-identical images to correctly identify when aggregation will occur with 91% accuracy. Once this onset is spotted, the self-driving system again switches on Brillouin imaging to provide a never-before-seen window into protein aggregation. For the first time, the biomechanics of this process can be captured dynamically as it occurs.

Lashuel emphasized that in addition to advancing smart microscopy, this work has important implications for drug discovery and precision medicine. “Label-free imaging approaches create entirely new ways to study and target small protein aggregates called toxic oligomers, which are thought to play central causative roles in neurodegeneration,” he said. “We are excited to build on these achievements and pave the way for drug development platforms that will accelerate more effective therapies for neurodegenerative diseases.”

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Novel 3D Laser Scanner Helps Harvest with Purpose

Due to a shortage of skilled workers, researchers around the world are working to develop harvesting robots that could provide support to agricultural businesses. Currently, however, according to Andreas Nüchter, from Julius-Maximilians-Universität (JMU) Würzburg, initial prototypes have yet to reach high levels of functionality for the necessary applications.

In response, researchers at the University of Würzburg have developed a 3D laser scanner system that aims to provide a better understanding of the condition of plants — for example, by reliably measuring the water content of fruits. This knowledge is crucial for determining the right time to harvest, according to Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) researcher Manuela Zude-Sasse, who led the development team.

“For the production of horticultural products, knowledge of the stage of ripeness is very important in order to be able to optimally control cultivation, harvest time and storage,” Zude-Sasse said.

“Against the backdrop of increasingly variable growth factors due to global warming, precise data on fruit development is becoming increasingly important — for scientific modelling as well as for the future use of commercial harvesting robots.”

The development team installed the system on a test site in Potsdam, Germany, and initial tests have been successful, team members said. For testing, the 3D laser scanner was mounted on a sensor conveyor station that circles a plantation of 120 apple trees. Harvesting robots, or the imaging and sensing systems embedded into or onto them, must be able to ‘read’ apple trees and other plants correctly, since no two plants look exactly alike.

The plant scanner is further designed to withstand wind and weather, and to operate in temperatures between 0 °C - 40 °C. The sensor system works on the principle of structured light: It projects three wavelengths: 520 nm, 660 nm, and 830 nm onto the plants. The reflected signals provide precise spatial information about the plants. Because the signals are available separately for each wavelength, they open possibilities for recording physiological properties of the plants, such as water content.

The sensor system will be used continuously on the ATB test site until November to monitor the 120 apple trees. The researchers designed the 3D laser scanner exclusively for experimental use, they said, with the goal of improving the data basis for modelling work and the specifications for future harvesting robots.

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Spectrometer Measures from UV to NIR, Small Enough for Smartphone

Researchers at North Carolina State University (NCSU) have successfully demonstrated a spectrometer that is orders of magnitude smaller than current technologies and can accurately measure wavelengths of light from ultraviolet to the near-infrared (NIR). The technology makes it possible to create hand-held spectroscopy devices and holds promise for the development of devices that incorporate an array of the new sensors to serve as next-generation imaging spectrometers, the researchers said.

Spectrometers are used in applications ranging from manufacturing to biomedical diagnostics to discern the chemical and physical properties of various materials based on how light changes as it interacts with those materials. Despite their widespread use, even the smallest spectrometers on the market are fairly cumbersome.

“We’ve created a spectrometer that operates quickly, at low voltage, and that is sensitive to a wide spectrum of light,” said Brendan O’Connor, corresponding author of a paper on the work and a professor of mechanical and aerospace engineering at NCSU. “Our demonstration prototype is only a few square millimeters in size — it could fit on your phone. You could make it as small as a pixel, if you wanted to.”

The technology makes use of a tiny photodetector capable of sensing wavelengths of light after the light interacts with a target material. Applying different voltages to the photodetector manipulates which wavelengths of light it is most sensitive to.

“If you rapidly apply a range of voltages to the photodetector, and measure all of the wavelengths of light being captured at each voltage, you have enough data that a simple computational program can recreate an accurate signature of the light that is passing through or reflecting off of the target material,” O’Connor said. “The range of voltages is less than one volt, and the entire process can take place in less than a millisecond.”

Previous attempts to create miniaturized photodetectors have relied on complex optics, used high voltages, or have not been as sensitive to such a broad range of wavelengths.

In proof-of-concept testing, the researchers found their pixel-sized spectrometer was as accurate as a conventional spectrometer and had sensitivity comparable to commercial photodetection devices.

“In the long term, our goal is to bring spectrometers to the consumer market,” O’Connor said. “The size and energy demand of the technology make it feasible to incorporate into a smartphone, and we think this makes some exciting applications possible. From a research standpoint, this also paves the way for improved access to imaging spectroscopy, microscopic spectroscopy, and other applications that would be useful in the lab.

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Lasers Improve Phototherapy-Drug Delivery Platform

Photoimmunotherapy targets cancer cells with microscopic, nano-engineered cancer drugs that are light-activated at the lesion site. Although the technology is increasingly used to treat metastatic cancer, tools to improve the effectiveness of photoimmunotherapy in gynecologic oncology have been lacking. Responses to treatment vary from person to person, and no method has existed to readily monitor whether the drugs are delivered effectively or have the desired therapeutic effect.

Researchers from the University of Maryland (UMD), in collaboration with medical laser manufacturer Modulight, demonstrated that the efficacy, safety, and consistency of photoimmunotherapy can be improved by integrating targeted, light-based techniques for drug delivery with laser-assisted endoscopy and fluorescence-guided treatment planning.

The researchers designed a drug delivery system using targeted, photo-activable, multi-agent liposomes (TPMAL). TPMAL consists of chemotherapy drugs that are labeled with fluorescent markers. It improves therapeutic delivery and monitors the growth of metastases in difficult-to-treat locations within the body.

A laser-assisted endoscopic camera created by Modulight captures and uploads information from TPMAL to Modulight Cloud, a cloud services platform. Modulight Cloud analyzes the multispectral fluorescence emission from TPMAL in real time to enable fluorescence-guided drug delivery and fluorescence-guided light dosimetry. The real-time connectivity enables simultaneous treatment monitoring, remote protocol development, and rapid transfer and viewing of data.

The data acquired by the camera can be used to customize fluorescence-guided therapy. Doctors can use the data to determine, in real time, how well the drug-carrying liposomes are reaching and delivering treatment to metastatic cancer sites. They can also use the data to calculate the precise amount of laser light needed during photoimmunotherapy to minimize tissue damage.

The optimal accumulation of TPMAL in tumors can occur at different times for different patients. Fluorescence-guided drug delivery identifies the time when the TPMAL fluorescence signal is near peak levels in tumors, thereby informing the initiation of photoimmunotherapy for the best outcome.

The TPMAL levels in tumors can vary among patients. Fluorescence-guided light dosimetry monitors the photobleaching of photosensitizers during photoimmunotherapy, so that the light dose can be adjusted accordingly, in real time, to improve the consistency of treatment effects.

“Patients undergoing cancer treatment exhibit varying tolerances and individualized responses,” said UMD professor Huang Chiao Huang. “Through continuous monitoring of their progress, we can promptly determine if treatment adjustments are needed while they are actively receiving care. There is no need to postpone decisions until the next scheduled session.”

The researchers demonstrated their approach in mouse models with peritoneal carcinomatosis, a type of cancer that can be caused by metastatic ovarian cancer. They observed a fourteenfold improvement in drug delivery to metastatic tumors. The information captured by the endoscopic camera prompted fluorescence-guided light dosimetry in over 50% of the mice. By combining TPMAL, laser-assisted imaging, and fluorescence-guided intervention, the researchers achieved a more homogeneous response to treatment in the mice and improved tumor control without causing side effects.

Although nearly half of the women with advanced ovarian cancer achieve complete remission after surgery and chemotherapy, many patients will relapse due largely to residual submillimeter lesions. These residual micro-metastases are difficult to detect and often develop resistance to standard treatments. New approaches are needed to address these drug-resistant micro-metastases.

The UMD-Modulight study shows that a combination of targeted photomedicine drug delivery, imaging, and monitoring of treatment responses can help prevent the return and spread of cancer while minimizing the side effects of treatment. Together, the three techniques can give doctors the ability to make immediate adjustments in treatment to achieve better outcomes.

“This approach ensures that the treatment remains both effective and uniform,” Huang said. “This process is repeated as necessary for each patient, resulting in a personalized dosage tailored to maximize treatment effectiveness within a single session.”

The team is now working to establish a comprehensive panel of biomarkers that would allow doctors to promptly determine the optimal dosage for each patient during a treatment session.

“The goal is to minimize the number of treatment cycles required while minimizing burden on the patient’s body, in contrast to conventional treatment approaches,” Huang said.

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