TECHNOLOGY

Recently, the team of Professor Li Zhen and Associate Researcher Xie Yujun of the Institute of Molecular Aggregation Science of Tianjin University successfully developed a new fingerprint developer, which can realize high-quality fingerprint image development and bring new ideas for identity authentication and case investigation. The relevant results have been supported by the National Natural Science Foundation of China and Tianjin Municipality, and have been published in the authoritative journal “Advanced Materials” in the field.

At present, fingerprint recognition technology has been widely used in criminal investigation, identity recognition and other fields. “Fingerprint tertiary features” are microscopic details further extracted from fingerprint patterns, such as the width and shape of the fingerprint ridge, the distribution and spacing of sweat glands on the fingerprint ridge. In the actual criminal investigation process, many crime scenes often leave only a small number of fingerprints, and the existing fingerprint recognition technology is difficult to identify on this basis, but according to the three-level characteristics of fingerprints, it can be identified. High-quality fingerprint images are inseparable from the reliable detection of three-level fingerprint features, so the development of high-performance fingerprint visualization technology is of great significance for criminal investigation and other fields.

The working principle of the developer and the fingerprint development time Courtesy of Tianjin University

The team of Li Zhen and Xie Yujun developed a new amphiphilic fingerprint developer. The developer grinds in air to change its luminous color and can also recover on its own; The developer uses water as a solvent, which not only avoids the damage to the fine structure of the fingerprint, but also has a good development effect in various matrices. In particular, it is worth mentioning that the developer not only has a low working concentration and short development time, but also has an extremely high resolution to obtain fingerprint development images, especially for the third-level fingerprint details.

Developer develops effect on various materials, tertiary fingerprint details on tin foil and plastic Photo courtesy of Tianjin University

“This fingerprint developer can achieve the almost best effect of the current three-level fingerprint feature development,” according to the researchers, “In the future, this technology is of great significance for identity identification and fingerprint evidence collection in case scenarios.” (Source: China Science News, Jiao Defang, Wang Mingchen, Chen Bin)

Related paper information:https://doi.org/10.1002/adma.202211917

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Recently, the silicon-based functional materials group of the Environmental Materials and Ecological Chemistry R&D Center of the Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, cooperated with Shandong Xinna Ultra-loose New Materials Co., Ltd. to develop a 5G radome and radome superhydrophobic rainproof decay coating with excellent pressure resistance, mechanical stability and weather resistance, which can effectively solve the “rain decay effect” of 5G signals in rainfall. The paper was published in Nature Communications.

5G technology is China’s major strategic layout, at present, China has built the world’s largest 5G network, which requires a large number of 5G base station support. 5G radome is an important part of 5G base station, used to protect the antenna system from the interference of the external complex environment, improve the accuracy and service life of the antenna. 5G signals currently operate in the < 6 GHz band, and 30-300 GHz millimeter waves will be used in the future. Therefore, 5G signals are susceptible to external interference, especially the “rain decay effect”.

Zhang Junping, the leader of the research group, told China Science News that the superhydrophobic coating has the characteristics of high droplet contact angle and low rolling angle, and the droplets are easy to roll off the surface, which has broad application prospects in the fields of self-cleaning surface, anti-liquid adhesion, and anti-liquid spreading, and is very promising to be used on the surface of 5G radomes to solve its “rain decay effect”.

The researchers solved the problem of high production costs by using common, low-cost raw materials to prepare coatings. Through the systematic design of the coating, a large-area superhydrophobic coating with excellent comprehensive performance was successfully prepared by a simple one-step spraying method. In addition, the long-term outdoor stability of the superhydrophobic coating has been successfully overcome.

After 3 years of research and development, industrialization and large-scale application, the coating performance has been greatly improved. In the future, the team will explore more potential application fields of superhydrophobic coatings, and realize the engineering applications of superhydrophobic coatings in high-voltage transmission lines, bridges, tunnel anti-icing, 5G radomes, radomes against rain and decay, anti-hazardous liquid adhesion, waterproof and oilproof films of electronic products, and self-cleaning municipal engineering. In addition, the research and application of the coating is of great significance to environmental protection and sustainable development, which will further reduce project maintenance, cleaning times, energy consumption and labor costs. (Source: Ye Manshan, China Science News)

Related paper information:https://doi.org/10.1038/s41467-023-38678-0

Rain decay resistance and engineering applications of superhydrophobic coatings. Photo courtesy of Lanzhou Chemical Institute

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The reporter learned from the Institute of Oceanography of the Chinese Academy of Sciences on May 29 that the research group of Li Xiaofeng, a researcher at the institute, has made important progress in inverting the global phytoplankton pigment concentration based on deep learning algorithms, and the research results were published in the top journal in the field of remote sensing “Environmental Remote Sensing” (IF=13.85). 

Phytoplankton is not only the primary producer of the ocean, but also a vital carrier in the process of marine biogeochemical cycling, and its community structure is related to the change of marine ecological environment, which is an important indicator factor for understanding the ecological evolution driven by marine dynamic processes. The pigment concentration of phytoplankton is an important basis for their classification and analysis of their community structure. The optical absorption information related to phytoplankton pigment concentration can be obtained by using marine optical remote sensing, but due to the variable optical characteristics of seawater and the “packing effect” in the optical absorption process of phytoplankton, it is difficult to invert the concentration of phytoplankton at the same time on a global scale.

Global distribution of the ratio of phytoplankton pigment concentration to total chlorophyll concentration Photo courtesy of the research group

It is understood that based on the long-term collection of on-site HPLC data and MODIS satellite remote sensing data, the study constructs a global phytoplankton pigment concentration matching dataset, which for the first time realizes the inversion of the concentration of 17 phytoplankton pigments in the global ocean, and obtains the distribution of different phytoplankton taxa in the global ocean. 

In the process of constructing the deep learning model, the research team fully considered the influence of other substances in seawater on the inversion of phytoplankton pigment concentration, and used the residual network and multi-scale pyramid structure to realize the acquisition of complex nonlinear relationships and multi-scale feature learning when multiple pigment concentrations were inverted at the same time. The global phytoplankton pigment concentration inversion model can be used to study the change process of long-term marine phytoplankton taxa, and reveal the influence of large-scale marine dynamic processes on the structure of marine phytoplankton communities. 

The results show that the deep learning algorithm can effectively invert the phytoplankton pigment concentration at large spatiotemporal scales, so as to analyze the phytoplankton community dynamics in the global ocean. During the 2015/2016 El Niño event, prokaryotic predominant seas extended from 180°E to 150°W to the east. From 2003 to 2021, prokaryotic abundance was positively correlated with El Niño intensity, but negatively correlated with the abundance of phytoplankton as a whole. 

The first author of the paper is Li Xiaolong, a senior engineer of the institute, and the co-authors include master’s student Yang Yi and Nagoya University professor Joji Ishizaka, and the corresponding author is Li Xiaofeng. (Source: China Science News, Liao Yang, Wang Min)

Related paper information:https://doi.org/10.1016/j.rse.2023.113628

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Statistics show that various sports and life accidents represented by skiing, boxing and rugby cause about 42 million people worldwide with minor concussions every year. However, minor concussions usually do not result in organic lesions, so computed tomography (CT) and magnetic resonance imaging (MRI) play a limited role in diagnosis, and the patient’s self-symptom description is the main source of information for the diagnosis of minor concussion. The lack of objective evaluation criteria and portable monitoring technology for the diagnosis of minor concussion has become the main problem in clinical diagnosis and treatment.

Based on this, the team of Academician Wang Zhonglin and Chen Baodong of the Beijing Institute of Nanoenergy and Systems, Chinese Academy of Sciences proposed a strategy to achieve real-time monitoring of head impact through a flexible curved sensing array composed of 3D printed multi-angle nanogenerators (TENG). This shows good prospects in the fields of personalized medicine, smart sports and aerospace. Recently, related papers were published in Science Advances.

Illustration of a self-driving sensing array application. Photo courtesy of interviewee

Nanogenerators show the advantages of sensing

Minor concussions occur frequently and may be accompanied by long-term cognitive, emotional, and physical sequelae. And the energy transfer of different types of impacts to the head will cause the brain to be cut, compressed, rotated and torn in the skull, resulting in different concussions. Therefore, it is necessary to objectively and accurately assess the degree and type of concussion.

Nanogenerators work using the coupling of friction, the electrical effects of pressure, and electrostatic induction. Therefore, the change of impact (friction) force can be judged from the change in the electrical efficiency of the circuit. Nanogenerators feature self-driven sensing, high sensitivity, and material diversity, making them ideal for providing active monitoring of static and dynamic pressure.

“Based on previous research, the team designed a wearable sensing array for position tracking and level evaluation of head impacts.” Chen Baodong told China Science News, “The array consists of 32 frictional nanogenerator units. Compared to other bulky and complex cabling solutions, the device is lighter, more flexible, and more portable. ”

This sensing array structure design not only helps the sensor to show the best performance, but also enables the friction nanogenerator to have the ability to move at multiple angles. In the experiment, the researchers used friction nanogenerators to collect mechanical energy from different directions and identify shear, rotational and compressive forces. After 30,000 duty cycles, it was found that the overall sensitivity decreased by only 2%.

“In addition, the sensor has ultra-high sensitivity and an ultra-wide pressure bandwidth.” Lulu, the first author of the paper and a doctoral student at the Beijing Institute of Nanoenergy and Systems, Chinese Academy of Sciences, explained, “Ultra-high sensitivity and ultra-wide pressure bandwidth are important performance indicators to measure the sensor’s perception ability and test range, our flexible sensing array can work normally in the range of external pressure (impact force) of 0~200 kilopascals (Kpa), if the voltage is used as the sensing signal, it can accurately distinguish pressure changes with an average sensitivity of 0.214 V/Kpa.” ”

Using 6 sensing metrics (stability, uniformity, linearity, repeatability, sensitivity, and hysteresis) to evaluate the sensor’s overall performance, the researchers found that friction nanogenerators can convert forces (compression, rotation, or shear) from multiple directions into electrical signals without an external power source. With a response time of 30 milliseconds and a minimum resolution of 1.415 kPa, it exhibits excellent sensing capabilities over a wide range of 0 to 200 kPa. In addition, the array can be visually mapped of head impacts through a wireless Bluetooth warning system. With the help of machine learning algorithms, it also shows an assessment of damage levels (with 98% accuracy).

“These data outperform other triboelectric and piezoelectric pressure sensors reported in the current literature.” “By collecting standardized data, we hope to build a big data platform to delve into the direct and indirect effects between head impacts and minor concussions in the future,” Chen said. ”

3D printing expands the application space

Although nanogenerators have obvious advantages in sensing, as an emerging sensor, there is still a lack of standard production processes, and manual products in the laboratory have become a major challenge for the large-scale application of this sensor. Advances in 3D printing technology not only make it possible to standardize the production of sensors produced by various materials (such as conductors, semiconductors and biomaterials), but also adapt to irregular body structures. Combined with 3D printing technology, it not only simplifies the processing of nanogenerators and reduces costs, but also allows integration into various application scenarios.

The researchers made the electrode wires into flexible printed circuit boards and provided upper and lower copper shields to reduce or minimize the effects of crosstalk. After applying pressure, an array of preloaded contact points can be imaged as letters “L”, “X” and “N” by rainbow color maps. By successfully demonstrating the pressure distribution of planar arrays, we are one step closer to the goal of curved sensing.

“The complex curvature of the head dictates that the sensing device must have precise surface geometry and stability. Therefore, we used reverse engineering, combined with 3D scanning to create a head point cloud, and 3D printed an ergonomic soft array that approximates the contour of a human head. Chen Baodong added, “3D printing provides a viable pathway for the commercialization of nanogenerators and shows good prospects in personalized medicine, intelligent sports and aerospace.” ”

Smart sports and wearable healthcare opportunities

To more accurately assess the degree of head impact, the team used deep convolutional neural networks (DCNNs) to analyze and identify multidimensional and massive amounts of data.

“DCNN can predict one or more response variables, with significant success in model prediction. The trained 6-fold cross-validation confusion matrix and prediction result confusion matrix show that the trained model has 100% classification accuracy for injury levels and 98% prediction accuracy for prediction sets. Lulu said.

Based on this, the team proposed a head-impact remote sensing system (HIRS) consisting of a friction nanogenerator sensing array, a data processing module, and a mobile terminal. In addition, a topologically optimized support structure was added to work with friction nanogenerator sensing arrays to create smart helmets better suited to reduce the effects of minor concussions. The results show that the head impact remote sensing system can quickly identify the injury area and provide accurate and intuitive recommendations before the clinical diagnosis of minor concussion, which helps to avoid delays in diagnosis and treatment.

“Using self-driving sensing arrays and machine learning methods to build DCNNs as decision-making models, and then creating a head-impact remote sensing system to provide predictive diagnostic reference in practical applications.” For example, in the case of an athlete’s injury, coaches and medical staff can judge whether a match needs to be terminated by the conclusions displayed on their smartphones. Chen Baodong said, “In addition, the breakthrough of fully 3D printed sensor technology makes the system suitable for pressure sensing equipment with various structures and different scenarios.” Therefore, this friction nanogenerator sensing array is expected to be widely used in the field of smart sports and wearable medicine. (Source: Zhang Shuanghu, China Science News)

Related paper information:https://doi.org/10.1126/sciadv.adg5152

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Light excitation is one of the most important manifestations of light-matter interaction in nature, such as photosynthesis in plants, vision in living things, photography and laser processing of materials. It is generally believed that the more light a substance absorbs, the stronger the material modification produced. However, this is not the case in the processing of glass by femtosecond lasers.

Femtosecond (1 fs=10-15 s) laser pulse has the characteristics of ultra-short pulse time and ultra-high peak power, so it can quickly and accurately deposit energy in transparent materials and obtain ultra-high three-dimensional spatial resolution, which is widely used in ophthalmic surgery, three-dimensional integrated optics, quantum optics, microfluidic devices, optical device preparation and optical storage. However, many of the observed physical phenomena are not fully explained. On the other hand, the relatively low processing speed and high processing cost limit the practical application of femtosecond laser processing technology.

Birefringence, that is, the incident of light waves into an anisotropic medium and decomposes into two polarized lights with vibration directions perpendicular to each other and different refractive indices, is a common phenomenon in optics. Commonly used optical components, such as half-wave plates and polarizers, are prepared based on the birefringence properties of crystals.

Glass itself does not have birefringence properties. Researchers have demonstrated that focused femtosecond laser pulses can induce periodic nanograting structures with birefringence properties in quartz glass.

This flexible and controllable birefringence has been applied to (1) high-density multi-dimensional light storage with unlimited life; (2) Preparation of geometric phase devices and vector light converters with high damage thresholds. The physical mechanism of nanostructure formation and how to improve its preparation efficiency has always been a hot topic in research.

Innovative research

Recently, a research group led by Professor Peter G. Kazansky of the University of Southampton in the United Kingdom demonstrated that elliptically polarized femtosecond laser pulses can produce stronger birefringence modification in quartz glass (Figure 1). Although the absorption rate of elliptically polarized light pulses (elliptic polarization of 0.6) is only about 40% of linearly polarized light, it can produce 1. 5x phase delay. In other words, with elliptically polarized femtosecond laser pulses, we can produce stronger material modifications with less energy. Inside this birefringence modification are randomly distributed anisotropic nanopores with up to 99% optical transmittance, which is much higher than traditional nanograting-based birefringence modifications.

Fig. 1 Birefringence modification of femtosecond laser pulse writing with different elliptic polarization degrees. (a) birefringence voxels written at different ellipsometries and (b) phase delay measured. (c) Absorption rates of different elliptically polarized light pulses.

Compared to linearly polarized pulses, elliptically polarized light pulses can create nanopores with larger duty cycles in quartz glass (Figure 2). The formation of anisotropic nanopores consists of two parts: the generation of nanopores and the stretching of nanopores. The authors propose that the strongest material modification produced by elliptically polarized light pulses is the result of the generation and tensile equilibrium of nanopores.

On the one hand, the stretching of nanopores is based on near-field enhancement effects, which means that linearly polarized light can maximize the stretching of nanopores. Circularly polarized light, on the other hand, can produce more nanopores. Because through tunnel ionization, circularly polarized light can more effectively excite defects with low ionization energy in quartz glass, resulting in nanopores with larger duty cycles.

It is generally believed that multiphoton ionization dominates the processing of transparent materials by femtosecond lasers. But this study reveals tunneling ionization of laser-induced defects with low excitation energy, which is key to the formation of nanostructures in quartz glass.

Figure 2 Birefringence structure of ultrafast laser writing. (a) Birefringence images. (b) Scanning electron microscopy image of anisotropic nanopores.

Elliptically polarized femtosecond laser writing has been applied to five-dimensional permanent optical storage, and its data readout accuracy is close to 100%. Compared to traditional linear polarized light writing, elliptically polarized light can write data points of the same mass with fewer pulses and less energy, thereby improving data storage speed.

On the other hand, the preparation time of geometric phase devices with ultra-high transmittance and high damage threshold and vector light converters has also been greatly reduced, and it is expected to be used in high-power lasers and ultraviolet lasers.

The results were published in Light Science & Applications under the title “Efficient ultrafast laser writing with elliptical polarization”. The main author of this paper is Lei Yuhao, a doctoral student at the University of Southampton, UK, and the co-corresponding author is Professor Peter G. Kazansky. Key collaborators include Professor Linards Skuja of the University of Latvia and Professor Yuri Svirko of the University of Eastern Finland. Associate Professor Wang Lei and Engineer Yanhao Yu, who are currently working at Jilin University, also made important contributions to this paper. (Source: LightScience Applications WeChat public account)

Related paper information:https://www.nature.com/articles/s41377‍-023-0‍1098-2

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Recently, Professor Chen Qin’s team from the Institute of Nanophotonics of Jinan University demonstrated a high-sensitivity and low-latency hydrogen sensor that integrates photocatalysis, photosensing, and photoelectric detection by regulating the interface transport behavior of surface plasmon thermoelectrons by gas molecules.

The results were published in Light: Science & Applications under the title “On-Chip Ultrasensitive and Rapid Hydrogen Sensing based on Plasmon-Induced Hot Electron-Molecule Interaction.” Professor Wen Long is the first author of the paper.

The monolithic integrated technical architecture uses surface plasmon thermoelectrons as the medium of photocatalysis, photosensing and photoelectric conversion, which provides a new idea for enhancing the interaction between photons-electrons and molecules and portable optical sensing detection, and has high research and application value.

As a clean energy, hydrogen is of great significance in promoting energy conservation and emission reduction, adjusting the structure of the energy industry, and coping with global climate change. However, hydrogen is difficult to detect when leaking, and it is very easy to cause safety accidents after accumulation, such as the 2011 Fukushima nuclear accident in Japan, which was an explosion caused by hydrogen leakage. In order to better develop and utilize hydrogen energy, fast and highly sensitive hydrogen sensing technology is essential.

At present, the main commercial products are resistive hydrogen sensors with semiconductor oxide systems and palladium (palladium alloy) systems. However, even high-performance resistive sensors can only quickly detect hydrogen concentrations above 500 ppm, cannot alarm in the initial stages of leakage, and often operate at temperatures of 150 °C or higher. Tunable semiconductor laser absorption spectroscopy technology commonly used in gas sensing is difficult to meet sensitivity requirements due to the low absorption coefficient of hydrogen, and the system is complex and expensive. New hydrogen sensing technologies and methods need to be developed.

Surface plasmon optical sensors are a hot topic in this field in recent years, and their technical principle is to use the difference in optical resonance characteristics caused by the change or deformation of plasmon metals under the action of hydrogen as a detection signal, and many remarkable breakthroughs have been made. However, most of the existing optical sensing technologies, including surface plasmon hydrogen sensing, adopt a discrete architecture of sensing unit and detection unit, relying on external high-precision spectrometers for optical sensing signal demodulation, which is not as practical as electrical sensors, and surface plasmon hydrogen sensors that have been reported in the existing literature also have shortcomings in sensitivity and response speed.

The core problem in improving the response sensitivity and speed of optical sensors is how to enhance the interaction between light and matter and achieve perfect translation of the sensing signal. Thanks to the study of the generation, recombination and interfacial transport mechanism of non-radiative attenuation thermoelectrons, surface plasmons are expected to expand from single optical applications to broader application scenarios such as photochemistry (photocatalysis, photoelectrochemistry) and optoelectronic devices (optical emission and light detection).

This paper innovatively combines the characteristics of surface plasmon thermoelectrons in the process of photocatalysis and photoelectric conversion, and proposes a photoelectric hydrogen sensor based on photocatalysis-photosensing-photodetection integration: (Figure 1) The use of hydrogen-sensitive metals to generate in-band transition thermoelectrons through surface plasmon coupling, on the one hand, can enhance and accelerate the catalytic bond breaking reaction at the hydrogen molecule/metal interface, on the other hand, it can cross the metal-semiconductor Schottky junction barrier to form a photocurrent signal related to the concentration of hydrogen molecules, and then realize the highly sensitive and monolithic integration Low latency hydrogen sensing.

Figure 1. Catalytic enhancement and in-situ photoelectric sensing were realized on hydrogen-sensitive metal-dielectric-semiconductor (MIS) junctions by using the transport and interfacial transport behavior of high-energy thermoelectrons in the band transition of surface plasmons.

More interestingly, the high sensitivity of this technique also benefits from the novel physical effects generated by the synergy of multiple physical mechanisms at the MIS junction interface. In this paper, the researchers found that the photovoltammetry (I-V) characteristic curve of the thermoelectron MIS junction is completely different from that of air and hydrogen: as shown in Figure 2 (upper left), the illumination I-V curve of the MIS junction in air is consistent with that of conventional junction optoelectronic devices; After exposure to the hydrogen-containing atmosphere, the illumination I-V curve showed a significant S-line curve around 0V. As shown in Figure 2 (top right), this effect enables a very high switching ratio of the sensing response at zero bias and room temperature. In this paper, combined with the quantum tunneling model of MIS junction, it is revealed that the S-linear I-V characteristics are derived from the modulation effect of hydrogen-induced interface dipole layer on the collection of thermal electrons in the MIS junction.

Figure 2. The main characteristics and performance of thermoelectronic hydrogen sensors proposed in this paper are displayed. (upper left) S-line illumination I-V curve based on the regulation of thermal electron tunneling characteristics by hydrogen-induced interface dipole layer; (top right) The device obtains a much higher hydrogen response switching ratio under illumination conditions than when operating in dark state; (bottom left) Low hydrogen sensing detection limit; (bottom right) Significantly faster response under lighting.

It is very important that this new sensor has the characteristics of simple preparation technology and low cost, without the need for precision graphical process, the formation of disordered metal nanostructures through rapid thermal annealing, with low-cost white LED light source, and does not rely on tunable lasers and spectrometers for signal processing, so it has the advantages of low-cost and portable applications.

Compared with the existing technology, the new principle of hydrogen sensing integrated photocatalysis-photosensing-photodetection proposed in this paper not only combines the catalytic enhancement effect brought by the thermoelectronic interface transfer behavior, but also uses the interfacial polarization layer induced by hydrogen to act on the photoelectric conversion of thermoelectrons, and obtains the performance indicators such as sensitivity and response time, and the detection limit reaches 1 ppm, and at the same time realizes the key technology of sensor chip with low cost, high integration, zero bias and room temperature operation. (Source: LightScience Applications WeChat public account)

Related paper information:https://doi.org/10.1038/s41377-023-01123-4

Special statement: This article is reproduced only for the need to disseminate information, and does not mean to represent the views of this website or confirm the authenticity of its content; If other media, websites or individuals reprint and use from this website, they must retain the “source” indicated on this website and bear their own legal responsibilities such as copyright; If the author does not wish to be reprinted or contact the reprint fee, please contact us.

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The interaction of machine learning and optics/photonics is changing the way we design novel metasurface structures and develop intelligent metasurface devices. In metasurface work, a key step is to obtain a high-performance metasurface structure or distribution with the desired optical response.

Compared with the traditional electromagnetic numerical calculation methods, the metasurface reverse design based on machine learning greatly improves the design efficiency and shortens the solution time. However, as a data-driven algorithm, data is as important to machine learning as the “fuel” of the engine, and the appropriate algorithm model is even more related to the quality of the engine’s “engine”.

One major hurdle in existing explorations is that both datasets and networks are disposable. For each new state or task, all datasets and networks must be discarded and rebuilt, resulting in huge wasted resources and time consumption. In existing metasurface reverse design, each metasurface task is physically unrelated and data utilization is extremely inefficient. Therefore, if a network corresponding to the physical connection between metasurfaces can be constructed, a large number of metasurface design problems can be robustly handled, opening up a new path for the design of intelligent superdevices.

Recently, the team of Professor Chen Hongsheng, researcher Qian Chao of Zhejiang University, proposed for the first time a “green” knowledge inheritance neural network suitable for metasurface reverse design and assembly. The study was published in Light: Science & Applications under the title “A knowledge-inherited learning for intelligent metasurface design and assembly.”

The research team points out that unlike traditional “brick-to-brick” neural networks, where input-output parameters tend to be fixed and rigid, similar to building containerized houses with high flexibility and free assembly, the “board-to-board” algorithm proposed by the team gives the network recyclability and flexible composability (Figure 1a).

Figure 1: Schematic diagram of a knowledge inheritance neural network applied to metasurface reverse design. (a) Comparing house building to traditional neural networks and knowledge inheritance networks. In masonry construction, all bricks are stacked with mortar and fixed, while containerized buildings are built with removable “panel” components. (b) Knowledge inheritance paradigm. Similar to the “brick by brick” masonry building, traditional neural networks are indivisible, rigid, and single in function once established. In contrast, knowledge inheritance neural networks are suitable for multi-object-oriented and unfixed shape metasurface design tasks. It consists of two functional networks, INN and SNN. For a given “descendant” metasurface, the final network architecture can be synthesized by assembling the INN and dynamically adjusting the SNN.

Based on the special physical properties of metasurfaces, the proposed knowledge inheritance network is associated with the complex spatial information of metasurface structures, so as to realize the knowledge inheritance of the “parent” metasurface, and then construct a new “child” metasurface through free assembly. In other words, the method maps the assembly properties of metasurfaces in physical space to the assembly synthesis of neural networks. This paradigm breaks the stereotype that neural networks have long been suitable for predefined and single-shaped target design objects. Specifically, the knowledge inheritance network consists of two functional network modules, namely the inheritance neural network (full name INN) and the assembly neural network (full name SNN). In it, the INN is responsible for the reverse design of each “panel”/”parent” metasurface, and the SNN assigns subtasks to each INN as the deployer (Figure 1b).

The researchers benchmarked this paradigm using an assembly design of a large-scale non-periodic (Figure 2a) and three periodic “offspring” metasurfaces (Figure 4a), demonstrating its superior generalization ability. The advantages of this knowledge inheritance paradigm are obvious: mining, inheriting and recycling “parent” knowledge not only greatly reduces the design dimension, but also achieves the design task of multi-target scenarios.

Figure 2: Overall architecture and network structure of the knowledge inheritance paradigm. (a) Flowchart of the knowledge inheritance paradigm. To match the inheritance-assembly scheme, two networks (INN and SNN) are established, with INN responsible for the reverse design of each “panel” metasurface, which aims to explore the relationship between the global target electromagnetic response and the local electromagnetic response provided by each “panel” metasurface. In this paradigm, all local “panel” metasurfaces are built into a database containing seven local panels with different tilt angles (tilted in the direction), including 0°, ±10°, ±20°, -30°, and 45°. The irregular aperiodic metasurface consists of 49 local panels provided by panels A, B, F, and G. (b) Knowledge inheritance neural network structure. SNN is a dual-output network consisting of convolutional neural networks, and INN is established as a dual-input dual-output network with two modules. That is, a convolutional neural network module for reverse design and a physically auxiliary module for forward mapping (all numeric subscripts indicate the number of filters), two connected by an intermediate phase distribution.

Compared with transfer learning, knowledge inheritance networks have essential differences in basic mechanism and performance. Transfer learning is a mature algorithm derived from computer science when a pre-trained model is reused for another task. The basic operation process is to transfer a pre-trained neural network in the source task to assist in the training of the target task. However, this transfer method is extremely unstable and does not guarantee the success rate of transfer learning. Even the performance becomes worse compared to no transfer learning. In other words, transfer learning is like a “black box” that relies heavily on brute force attacks with features and lacks physical interpretability (Figure 3A).

Figure 3: Comparison of transfer learning and knowledge inheritance networks. (a) Transfer learning is an algorithm derived from computer science with unstable performance and feature-dependent brute force attacks that lack a reasonable explanation. (b) Knowledge inheritance learning is a unique method that often brings performance gains, with clear physical explanations and associations.

In contrast, knowledge inheritance learning is a unique and proprietary approach that can be viewed as a “white box” with physical connections between internally transferred knowledge (Figure 3b) and cannot be easily replicated in computer science. Due to the unique physical properties of metasurfaces, knowledge inheritance networks are associated with the complex spatial information of structures, which often brings performance gains, and realizes the echo of network synthesis in virtual space and metasurface assembly in physical space.

As the “loudspeaker” of satellites, the spaceborne antenna is an indispensable part of satellite communications. Traditional spaceborne antenna technology has considerable hardware cost, energy consumption, and computational complexity. Even with high accuracy, conventional electromagnetic solvers often rely on complex and lengthy numerical simulations for antenna inverse design.

In this study, the research team used the design of intelligent foldable metasurfaces to propose and experimentally demonstrate an innovative spaceborne antenna that is promising for future satellite communications. Flexible, ultra-thin and low-cost foldable metasurfaces can be mounted on satellite wings, removing the barriers of high hardware cost and computational complexity of traditional spaceborne antennas (Figure 4b). The experimental results clearly show the possibilities of satellite-satellite and satellite-earth communication, and the applied knowledge inheritance neural network also makes it possible to dynamically change the design goals during communication.

Figure 4: Periodic “offspring” metasurfaces and their assumptions for future satellite communications applications. (a) Assembly process of periodic foldable metasurfaces. Three “descendant” metasurfaces with different stretchable angles (±20°, ±10° and 0°) are assembled from panels D/E, B/C and A, abbreviated as subchild 1/2/3. Each “descendant” metasurface contains 16 panels (including 16×64 cells), 8 of which extend along the y-axis and 2 along the x-axis. (b) Schematic diagram of satellite communications based on intelligent foldable metasurfaces. As a flexible, ultra-thin and low-cost free beam steering device, the foldable metasurface can be mounted on the wing of a satellite to achieve free folding and stretching. Combined with the knowledge inheritance paradigm suitable for multi-target scenarios, foldable metasurfaces can realize satellite-to-satellite communication and satellite-earth communication. (Source: LightScience Applications WeChat public account)

Related paper information:https://doi.org/10.1038/s41377-023-01131-4

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On May 22, Zhu Ping’s research group at the Institute of Biophysics, Chinese Academy of Sciences published a paper in the international academic journal Nature Communications. In this paper, the researchers propose a method for direct observation and identification of high signal-to-noise ratio of in-situ structural features and dynamic conformations of target molecules in cryo-electron tomography 3D imaging, and named REST (REstoring the Signal in Tomograms).

Cryo-electron tomography can obtain in situ three-dimensional structures of biological macromolecules with nanoscale resolution in cell and tissue samples, but due to the extremely low signal-to-noise ratio and irreversible information loss in cryo-electron tomography, it is difficult for researchers to obtain the real information of target particles (ground truth) required in the deep learning process, which makes the use of neural networks and deep learning techniques to identify target macromolecular proteins in electron tomography with great challenges.

In order to solve the above technical bottlenecks, the newly published research paper of Zhu Ping’s research group proposes and implements two training strategies. In strategy 1, the researchers select a small number of particles from the original data for subunit averaging, use the average result as the “ground truth” of training, and establish a training pair with the original particles. In the second strategy, the researchers artificially add different degrees of noise and dynamic conformational changes to the high-quality “ground truth” density map to simulate the low signal-to-noise ratio and macromolecular structure heterogeneity in the real data, and establish a mapping and training set for the high-noise, dynamically changing low-mass particle density map obtained by the simulation with the high-quality density map.

After establishing the above training set and deep learning strategy, the researchers used the deep learning network to learn and train the training set, and transferred the trained model and acquired knowledge to the original data to recover the information of the target protein particles.

REST methodology, flow, and training strategy

It is found that using the above strategies, REST methods will have a wide range of application values and prospects in various tasks related to cryo-electron tomography, such as restoring the clear signal of the target protein (such as identifying and extracting particles in a noisy background), segmenting target features, identifying the dynamic or flexible structure of the target protein, obtaining a density without missing information as an initial model, and assisting electron tomography with sub-unit average (STA).

Zhang Haonan, a doctoral student in Zhu Ping’s research group at the Institute of Biophysics, Chinese Academy of Sciences, and Li Yan, an associate researcher, are the co-first authors of the paper, and researcher Zhu Ping is the corresponding author of the paper. The research work was supported by the National Natural Science Foundation of China, the key research and development projects of the Ministry of Science and Technology, and the Strategic Leading Science and Technology Project of the Chinese Academy of Sciences (Class B). (Source: Meng Lingxiao, China Science News)

Related paper information:https://doi.org/10.1038/s41467-023-38539-w

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Diexcitons in quantum dots emit luminescence

Quantum dots are nanometer semiconductor crystals with a size of 2-100 nm. Due to the size effect, quantum dots can bind electrons and holes to form excitons, thereby achieving efficient luminescence, which is widely used in display, laser, fluorescence imaging and other fields. Under high-density light excitation, quantum dots can exhibit double exciton emission characteristics, which provides a potential scheme for polarimetric entanglement light sources and lasers. However, under the influence of Auger recombination, how to improve the luminous efficiency of two-excitons of quantum dots is a very challenging topic.

Previous studies have found that the Auger composite lifetime of quantum dots shows a linear scaling law with volume increase, as shown in Figure 1. This means that increasing the volume of quantum dots (diameter 1 nm-10 nm) can reduce Auger recombination (lifetime 1 ps-1000 ps). However, traditional group II-VI quantum dots generally use core-shell structure, and the synthesis of large-scale and efficient luminescent quantum dots is very difficult.

In this work, Professor Zhong Haizheng and his collaborators at Beijing Institute of Technology used the high defect tolerance of halogen perovskite quantum dots (CsPbBr?) to prepare large-size CsPbBr® nanocrystals (diameter > 20 nm) with high-efficiency double exciton emission characteristics, which not only achieved a large-size and high-efficiency breakthrough in quantum dot double exciton luminescence, but more importantly, the authors found that The Auger lifetime of large-size perovskite nanocrystals shows a special nonlinear increase law different from the previous reports after breaking through a certain volume, so that the lifetime of the double exciton can reach 6.3 ns, and the double exciton efficiency is as high as 80%. Further research shows that this special nonlinear increment law is caused by non-local effects in large-size nanocrystals. This work provides a new physical mechanism for the luminescence of quantum dots with double excitons.

Figure 1: The lifetime of a double exciton in a small-sized quantum dot increases linearly with increasing volume. Source: Chemical Reviews 2021, 121, 2325-2372. https://dx.doi.org/10.1021/acs.chemrev.0c00931

Large-size nanocrystals change the linear increase law of Auger recombination

Since 2015, Professor Maksym V. Kovalenko of the Swiss Federal Institute of Technology and Professor Zhong Haizheng of Beijing Institute of Technology respectively reported the high-efficiency luminescent perovskite quantum dots, the research of perovskite quantum dots has developed rapidly, and has received widespread attention in academia and industry, and the application of liquid crystal display has entered the industrialization stage.

Recently, Professor Zhong Haizheng, Associate Professor Zhang Yingyou and researcher Qin Haiyan of Zhejiang University of Beijing Institute of Technology have made new progress in cooperation, they synthesized high-quality large-size CsPbBr nanocrystals (diameter > 20 nm), through picosecond laser excitation of different powers, studied the characteristics of double exciton luminescence in CsPbBr® nanocrystals, and found that the luminous efficiency and lifetime of CsPbBbr nanocrystals increased greatly with size, and the double exciton efficiency was as high as 80%. The double exciton lifetime reaches 6.3 ns. In addition, the researchers also found that the Auger lifetime of large-size perovskite nanocrystals shows a special nonlinear scaling law with volume.

The work was based on “Nonlocal interaction enhanced biexciton emission in large CsPbBr? nanocrystals” was published in the new issue of eLight in the High Start of the Excellence Program. The first author of the paper is Huang Peng, a doctoral student from Beijing Institute of Technology, and Sun Shipei from Beijing Institute of Technology and Dr. Lei Hairui of Zhejiang University participated in the research.

To explain this phenomenon, the research team considered the non-local effect effect of double exciton luminescence, and theoretically simulated the influence of size on the exciton recombination process. As shown on the left side of Figure 2, in a small-size strongly confined quantum dot, because the quantum dot size is much smaller than the exciton wavelength, the exciton wave function is strongly bound, and the interaction between exciton and carrier is local. In contrast, in large-size, weakly confined nanocrystals, the exciton wave function exhibits spatially dependent oscillation behavior (EIK·R) in the nanocrystal, as shown on the right side of Figure 2. At the same time, Auger recombination is due to the interaction between the exciton and the third carrier. Therefore, Auger recombination in large-sized nanocrystals needs to consider the influence of the spatial oscillation behavior of the exciton wave function, that is, non-local interaction. From the perspective of Auger lifetime, non-local interactions in large-sized nanocrystals cause Auger lifetime to increase exponentially with volume. This non-local effect is the main reason for the efficient double exciton luminescence of large-size perovskite nanocrystals.

Figure 2: Exciton-carrier non-local interactions result in Auger lifetime in nonlinear increase with volume change in large-size nanocrystals (dashed arrow) and linear increasing behavior at small size (solid arrow). The left and right circles in the figure represent nanocrystals at small and large sizes, respectively, and the red curve shows the exciton wave function.

summary

This study found the non-local effect in large-size perovskite nanocrystals, which can also effectively inhibit Auger recombination and achieve efficient double exciton luminescence, which provides a new physical model for studying the interaction between large-size nanocrystalline light and matter, and is expected to promote the development of quantum light sources, lasers and displays. (Source: China Optics WeChat public account)

Related paper information:https://doi.org/10.1186/s43593-023-00045-3

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Recently, researchers such as Vladimir Aksyuk of the National Institute of Standards and Technology used flip-chip bonding technology to combine integrated photonics technology and metasurface optics to demonstrate an integrated optical architecture for realizing a compact strontium atomic clock. Through the design of experiments, the researchers demonstrated that the integrated photonic stage can scale to any number of beams, each with different wavelengths, geometries, and polarizations.

Background

Optical systems are an important part of atomic vapor, ion trap and neutral atom technology. To solve the problem of optical conversion in atomic systems, precise control of the wavelength, power, and polarization of coherent free-space light is required. This control is easy to achieve in a lab-scale setting, but becomes more challenging as optical systems shrink and commercialize. Miniaturized optical systems can be built using a combination of compact block or planar optics, while photonic integrated circuits (PICs) can provide a scalable way to fabricate atomic technology.

Photonic ICs enable production-scale integration of optical components, which can range from laser sources and modulators to on-chip detectors. Diffraction gratings can be integrated on the chip to generate a free-space beam from the guidance mode and are used to process out-of-plane atomic systems. Over the years, grating technology has evolved to enable multi-wavelength control of light, polarization control, and the generation of beams with large numerical apertures and large pattern expansion. However, there are challenges in bringing together these disparate capabilities on a single platform to achieve efficient arbitrary beam control.

For example, optical lattice clocks achieve state-of-the-art frequency instability and ultra-high accuracy, but require complex in vitro optical combinations to produce many laser beams and wavelengths for clock-referenced atomic samples.

Vladimir Aksyuk et al. of the National Institute of Standards and Technology designed and fabricated an integrated photonics package for the miniaturization of strontium atomic clocks. Based on a bonded planar stage, the optical metasurface is combined with a grating output to produce a beam with high numerical aperture, arbitrary tilt angle, defined polarization, and parallel propagation. This planar platform provides new ideas for the generation of on-chip beams and represents an important step towards the realization of atomic technology for fabricable photonic integrated circuits. The researchers demonstrated the device’s ability to simultaneously produce beams of various sizes, polarizations, and wavelengths, opening up a whole new avenue for reducing the size of atomic technology.

Innovative research

The researchers demonstrated a compact photonic chip system that produced 12 circularly polarized beams with a diameter of 10 millimeters, which were designed to form magneto-optical traps in a small volume in a vacuum chamber containing strontium vapor. In addition, the researchers demonstrated the collision combination of two separate waveguide beams to produce an optical lattice aligned with the clock-converted probe beam. The combined lattice and clock beams are pointed vertically within a range of 0.1°. Figure 1a is a schematic diagram of the magneto-optical trap installation, using an unconventional beam arrangement to obtain three-dimensional cooling and capture in a compact planar geometry. Figure 1b shows all 12 beams produced to form a magneto-optical trap device superimposed on a photonic IC chip picture, each with an optical fiber and waveguide, so that the power of each beam can be tuned independently. Figure 1c shows the use of a compact emitter combining measured lattice (green) and clock (red) beams that are generated separately on the surface of the photonic integrated circuit and overlap at the MS location.

Figure 1 Schematic diagram of magneto-optical trap device and the generated beam

Figure 2 shows a summary of the performance of blue and red magnetooptical trap beams. Figure 2a is an image of the blue beam and Figure 2b is an image of the red beam, showing the combined power curve projected horizontally and vertically along with the Gaussian fit. The beam performance characteristics after bonding the MS chip are shown in Figure 2c-2f, which is an image of a red and a blue beam taken at different heights with three visible diffraction orders of red light. Figure 2d is a map of 12 beams measured above MS, each overlaid with the polarization of the light field measured in the direction of beam propagation. Figure 2e depicts the average radial position of the red and blue beams at different heights, and Figure 2f shows the measured cross-section of the red and blue beams.

Figure 2 Summary of beam performance of blue and red magnetic traps

This work employs a scalable approach to the photonics technique for fabricating miniature strontium atomic clocks. This integrated design provides a way to implement increasingly complex optical systems. Future work focuses on combining photonics packaging with vacuum chambers for physics experiments to make photonic IC designs compatible with foundry-scale lithography techniques.

The article, published in the journal Light: Science & Applications, titled “Integrating planar photonics for multi-beam generation and atomic clock packaging on chip,” was first author by Chad Ropp and Vladimir Aksyuk as corresponding author. (Source: LightScience Applications WeChat public account)

Related paper information:https://www.nature.com/articles/s41377‍-023-0‍1081-x

Special statement: This article is reproduced only for the need to disseminate information, and does not mean to represent the views of this website or confirm the authenticity of its content; If other media, websites or individuals reprint and use from this website, they must retain the “source” indicated on this website and bear their own legal responsibilities such as copyright; If the author does not wish to be reprinted or contact the reprint fee, please contact us.

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