Category: Blog

Scientists are refining techniques to boost the sensitivity of molecular detection using light, focusing on methods called Surface-Enhanced Raman Scattering (SERS) and Surface-Enhanced Infrared Absorption (SEIRA). These techniques are powerful tools for identifying molecules, but detecting very small amounts, especially on surfaces, has been a persistent challenge.

Researchers are delving into the electromagnetic theories that underpin SERS and SEIRA to understand how to amplify their signals. The key to this enhancement lies in manipulating light at the nanoscale using specially designed structures. The intensity of both SERS and SEIRA signals relies heavily on the enhancement of the electromagnetic field at the surface where molecules are being analyzed.

Nanomaterials, particularly metallic nanostructures like gold and silver, play a crucial role in this field enhancement. When light interacts with these nanostructures, it can excite collective oscillations of electrons, known as plasmons. These plasmons create a concentrated and intensified electromagnetic field around the nanostructures. For SERS, this enhancement occurs in a two-step process: first, intensifying the incident light to excite molecules, and second, boosting the Raman scattering signal emitted by the molecules. SEIRA primarily benefits from the intensified incident light, which enhances the absorption of infrared light by the molecules.

Various nanostructure designs are being explored to maximize this signal boost. These include nanoparticles, closely spaced pairs of nanoparticles (nanodimers), and structures with extremely small gaps (nanogaps). More complex coupled structures, combining different types of plasmonic effects, are also showing promise for even greater sensitivity. For example, structures coupling localized surface plasmons (LSP) found in nanoparticles with surface plasmon polaritons (SPP) from metal films, or lightning rod effects from sharp nanotips are leading to significant improvements.

Recent advancements demonstrate the power of these approaches. Shell-Isolated Nanoparticle-Enhanced Raman Spectroscopy (SHINERS) uses nanoparticles coated with a thin inert shell, providing stability and preventing interference, while also allowing precise control over the crucial gap between the nanoparticle and the sample surface. Tip-Enhanced Raman Spectroscopy (TERS), utilizing a sharp metallic tip to scan surfaces, can achieve nanoscale and even sub-nanometer spatial resolution, pushing the boundaries of single-molecule detection.

While these techniques are reaching remarkable sensitivity, challenges remain. Quantum effects like electron tunneling at extremely small nanogaps can limit further enhancement. Researchers are also working to expand the applicability of SERS and SEIRA beyond specific molecules and noble metals to make them broadly useful for analyzing diverse materials. The ongoing refinement of nano-optical designs and a deeper understanding of the interplay between electromagnetic enhancement and quantum effects are paving the way for even more sensitive and versatile spectroscopic tools in the future.

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TUCSON, Ariz., March 12, 2025 — Arizona Optical Metrology LLC has launched Morpheus™, a new software designed to simplify measurement data reduction for complex optical surfaces. Morpheus™ is a computer-generated hologram measurement data reduction software that offers a one-click solution for substrate correction, mapping correction, and misalignment correction. The software is compatible with most interferometer systems that support standard file formats, aiming to streamline metrology workflows in the optics industry.

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Optical Surfaces могут Revolutionize Light Control and Information Processing

Scientists have developed innovative methods to control light using specially designed surfaces, opening new possibilities for advanced optical technologies. This research explores the creation of "diffractive surfaces" – thin optical elements patterned to manipulate light waves in precise ways. These surfaces can perform complex transformations on light, essentially acting as customizable optical components.

The study introduces two distinct approaches to design these light-shaping surfaces. The first, a "data-free" method, employs mathematical calculations known as matrix pseudoinversion. This technique directly computes the surface patterns needed to achieve a desired optical transformation without needing examples of inputs and outputs.

The second method leverages "deep learning," a type of artificial intelligence. This "data-driven" approach trains computer algorithms with numerous examples of input and output light fields, allowing the system to learn the optimal surface patterns for a given transformation through an iterative process of adjustment.

Researchers tested both design methods for various optical tasks, including complex mathematical transformations, image processing operations like the Fourier transform, rearranging light patterns (permutation), and filtering specific light frequencies (high-pass filtering).

The findings indicate that both design methods can successfully create diffractive surfaces capable of performing these transformations with high accuracy. However, the deep learning approach shows significant advantages, particularly when using multiple diffractive surfaces stacked together in layers. These multi-layered, deep learning-designed systems demonstrated greater efficiency in utilizing light and achieved better performance with a limited number of surface features. Notably, deep learning designs appear to benefit from distributing the complexity of the transformation across multiple layers, rather than concentrating it on a single surface.

These advances could pave the way for smaller, more efficient, and versatile optical systems. The ability to precisely shape and transform light using these diffractive surfaces has potential applications in advanced imaging, optical computing, and sophisticated sensors. Because these surfaces operate based on wave diffraction rather than traditional lens-based refraction, they offer the potential to create very compact optical systems free from common lens aberrations, particularly advantageous for wide fields of view. This research suggests a promising path towards engineering light with unprecedented control, driven by both mathematical precision and the power of artificial intelligence.

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Diffraction of light, a fundamental phenomenon, has long been crucial to imaging and sensing technologies. For generations, scientists and engineers have manipulated and utilized diffraction through optical components and sophisticated algorithms, or a combination of both. This approach has led to advancements in various fields such as holography, microscopy, photography, spectroscopy, and diverse sensing instruments. Traditionally, the design of these systems relied on physical understanding to guide the manipulation of light diffraction, aiming to enhance performance characteristics including signal clarity, image sharpness, depth perception, range of view, and speed of measurement.

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Keysight Technologies has announced a definitive agreement to acquire Synopsys’ Optical Solutions Group (OSG) for an undisclosed sum. The deal is dependent on Synopsys successfully acquiring Ansys, receiving regulatory approvals, and meeting other standard closing conditions. Synopsys’ OSG division includes offerings like CODE V for imaging systems, LightTools for illumination design, LucidShape for automotive lighting, and RSoft Photonic Device Tools. OSG recently launched ImSym, described as the industry’s first complete virtual prototyping platform for imaging systems.

Upon completion, Keysight will gain access to OSG’s team of optical engineers and scientists and their advanced equipment. This acquisition is expected to expand Keysight’s design engineering software offerings and reinforce its capabilities in radio frequency and microwave electronic design automation. It is also anticipated to enhance Keysight’s physics-based computer-aided engineering capabilities, particularly given the increasing complexity of electronics design. The acquisition will further enable Keysight to address high-performance system applications beyond traditional electronics, extending into optics and photonics.

Keysight intends to integrate OSG’s expertise to address design challenges requiring multi-physics simulations. The company anticipates that these advancements will strengthen its portfolio and contribute to revenue growth in the coming periods, potentially driving its stock performance. Keysight stock has shown a gain of 18.6% over the last year, outpacing the industry’s growth of 11.2%. Keysight currently holds a Zacks Rank #3 (Hold).

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New research indicates that the design of intraocular lenses (IOLs), artificial lenses implanted after cataract surgery, can impact their stability and thus, potentially affect patients’ vision after the procedure. A study focusing on different haptic designs – the arms that hold the lens in place – found that plate-haptic IOLs demonstrated superior stability compared to C-loop IOLs.

Cataract surgery, a common procedure to treat clouding of the natural lens of the eye, often involves replacing the clouded lens with an artificial IOL. The position and stability of these IOLs are critical for optimal visual outcomes, as any shift or tilt can lead to visual aberrations.

Researchers investigated three types of IOL haptic designs: plate-haptic, C-loop, and closed C-loop. Using a new advanced imaging technology known as CASIA2 anterior segment optical coherence tomography, they automatically measured the decentration (sideways shift) and tilt of the implanted IOLs in a group of patients who had undergone cataract surgery.

The study revealed that plate-haptic IOLs exhibited the least amount of decentration and tilt, suggesting greater stability. In contrast, C-loop IOLs showed the highest levels of both decentration and tilt. Closed C-loop IOLs performed similarly to plate-haptic IOLs in terms of stability. Specifically, the study highlighted a significant difference in stability between plate-haptic IOLs and C-loop IOLs, with plate-haptic lenses demonstrating significantly less decentration and tilt.

These findings suggest that the plate-haptic design may offer improved post-operative visual outcomes due to enhanced IOL stability. Researchers propose that the broader contact area of plate-haptic IOLs with the capsular bag, the natural structure holding the lens, provides better support and resistance against shifts.

While the study provides valuable insights, researchers acknowledge limitations such as a relatively small sample size and the exclusion of hydrophobic C-loop IOLs, another commonly used type. Further research with larger studies and broader IOL types is recommended to confirm these findings and further refine IOL design for optimal patient outcomes after cataract surgery. This research underscores the importance of IOL design in ensuring the best possible vision for patients undergoing cataract surgery, the leading cause of reversible blindness worldwide.

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NVIDIA’s DLSS technology, which utilizes artificial intelligence to enhance image resolution and boost frame rates on GeForce RTX graphics cards, has seen widespread adoption with integration into 216 games and applications. Building on this, NVIDIA has announced DLSS 3, the latest iteration of their neural graphics technology. DLSS 3 combines DLSS Super Resolution, a new DLSS Frame Generation technique, and NVIDIA Reflex, and is designed to operate on the company’s new GeForce RTX 40 Series GPUs. NVIDIA claims DLSS 3 can increase performance by up to four times compared to traditional rendering methods. Currently, over 35 games and applications are slated to incorporate DLSS 3, with the first implementations expected to arrive in October.

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