neanderthal tar production gibraltar cave

Unveiling Neanderthal Innovation: Adhesive Production Found in Gibraltar Cave

Discovery of an Ancient Neanderthal Manufacturing Center

Carved into the cliffs of Gibralter, a cave entrance opens onto a site dating back 65,000 years—a Neanderthal tar distillation oven, one of the earliest manufacturing centers on Earth.

Sophisticated Health Structure in Vanguard Cave

Researchers from the University of Murcia have uncovered a sophisticated Neanderthal hearth structure in Vanguard Cave. Middle Paleolithic stone tools and plant residues indicate that Neanderthals utilized rockrose (Cistaceae) to produce tar, highlighting their advanced fire managements skills and technological expertise.

The Role of Fire in Neanderthal Technology

Neanderthals employed fire for various purposes, including warmth, light, cooking, landscape management, and extracting adhesive tar from selected plants and trees. The composition of tar residues on tools provides evidence of their fire-based tar extraction methods.

Neanderthal Adhesive Use in Tool-Making

The use of tar as an adhesive for hafting stone tools to wooden handles signifies a pivotal advancement in tool-making, occurring over 100,000 years before its application by modern humans.

Theories and Challenges in Tar Extraction Techniques

Previous reconstructions of Neanderthal tar extraction techniques point to the possible use of underground fire pits, yet no direct evidence of these structures has been uncovered.

New Study on Neanderthal Tar Production in Vanguard Cave

The study, titled 'A Neanderthal's specialized burning structure compatible with obtention,' published in Quaternary Science Reviews, examines a hearth pit in Vanguard Cave using geochemical, mineralogical, palynological, and micromorphological analyses.

Discovery of a Specialized Hearth Pit

The researchers uncovered a central fire pit flanked by two trenches, featuring crust of thermally altered rocks and sediment, consistent with prolonged fire use. This configuration supports theoretical models for specialized low-oxygen heating systems used in tar production.

Geochemical and Micromorphological Findings

Identification of Key Biomarkers in the Hearth Structure

Organic geochemical studies identified levoglucosan and notable levels of retene int he structure's matrix, both linked to the burning of resinous plants. Lipid analysis further revealed odd-carbon-numbered n-alkanes and even-carbon-numbered n-alkanols, biomarkers characteristic of fresh leaf waxes from plants such as rockrose.

Charcoal Analysis and Pollen Findings

Charcoal analysis revealed partially vitrified fragments from plants in the Cistaceae (rockrose) family, indicating incomplete combustion under controlled conditions. Conifer wood accounted for less than 10% of the charcoal.

Palynological analysis revealed a high concentration of pollen grains within the structure, while the surrounding sediments showed no pollen, indicating that Neanderthals deliberately introduced plant matter into the hearth.

Micromorphological Insights into Controlled Heating

Micromorphological analysis revealed no signs of clay heating beyond 500°C, suggesting that the structure was employed for controlled, low-temperature processes suitable for tar production.

Strategic Construction for Efficient Tar Distillation

The carbonate rocks within the structure appear to have been strategically placed, likely to form a seal made of guano and sand, which would create the low-oxygen environment necessary for efficient tar distillation.

Experimental Validation of Neanderthal Tar Extraction Methods

To validate their hypothesis, the research team carried out an experimental archaeology project, constructing a similar structure and heating rockrose leaves under low-oxygen conditions. The experiment successfully generated enough tar to haft stone spearheads, using only tools and materials available to Neanderthals in the region.

Implications for Neanderthal Cognitive Sophistication

Results indicate that Neanderthals systematically organized fire-based activities by creating specialized heaths for tar extraction, supporting the notion of their cognitive sophistication and cultural development, as evidenced by their use of manufactured tools.

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Breakthrough Discovery: Laser Light Proven to Cast Shadows

Can Light Cast a Shadow?

Challenging Traditional Beliefs about Shadows

Is it possible for light to cast a shadow? While it might sound paradoxical, researchers have shown that, in specific conditions, a laser beam can behave like a solid object, creating a shadow. This breakthrough redefines our understanding of shadows and introduces intriguing potential for technologies where one laser could regulate another.

A Groundbreaking Study in Optics

Key Findings of the Research

"It was long believed that laser light could not cast a shadow, as light typically traverses other light without interference," explained Raphael A. Abrahao, the research team leader at Brookhaven National Laboratory and formerly of the University of Ottawa. "Our demonstration of this counter-intuitive optical phenomenon challenges traditional assumptions about shadows."

How the Experiment Worked

A study published in Optica outline how researchers employed a ruby crystal and carefully chosen laser wavelengths to illustrate that a laser beam can indeed block light and produce a visible shadow through a nonlinear optical process. This effect emerges from light's intensity-dependent interaction with materials, enabling one optical field to affect another.

Practical Applications of Laser-Cast Shadows

Implications for Optical Technology

"Our comprehension of shadows has evolved alongside advancements in light and optics," remarked Abrahao. "This discovery may hold value for applications like optical switching, where one light source controls another, or technologies demanding precise light management, such as high-power laser systems."

This study contributes to a wider inquiry into the ways in which a light beam can influence another when subjected to unique conditions and nonlinear optical interactions.

Inspiration Behind the Experiment

From Concept to Reality

The concept arose during a lunch conversation when someone noted that certain experimental diagrams created with 3D visualization software show a laser beam's shadow by representing it as a cylinder, ignoring the physical properties of a laser. This sparked curiosity among scientists: Could this effect be replicated in the lab?

"When began as a lighthearted lunch conversation evolved into a deeper discussion on laser physics and the nonlinear optical response of materials," remarked Abrahao. "This eventually inspired us to conduct an experiment to reveal a laser beam's shadow."

The Experiment in Detail

Setting Up the Laser Shadow Experiment

The researchers directed a high-power green laser through a cube of standard ruby crystal, illuminating it from the side with a blue laser. Inside the ruby, the green laser locally modifies the material's reaction to the blue wavelength, behaving as a physical object while the blue laser serves as the source of illumination.

Results of the Laser Interaction

The two lasers' interaction produced a visible shadow on the screen, appearing as a dark zone where the green laser obstructed the blue light. This shadow met all typical criteria: it was clearly seen, conformed to the contours of the receiving surface, and mimicked the shape and position of the green laser as if it were a solid object.

The laser shadow effect arises from nonlinear optical absorption within the ruby crystal. This effect occurs as the green laser elevates the optical absorption of the blue illuminating beam, creating a corresponding region with reduced intensity. This produces a darker area resembling a shadow cast by the green laser beam.

Shadow Analysis and Future Prospects

Analyzing Shadow Contrast

"This discovery broadens our comprehension of light-matter interactions and unlocks new avenues for utilizing light in previously unimagined ways," said Abrahao.

Through experimentation, the researchers measured how the shadow's contrast varied with the laser beam's power, noting a peak contrast of about 22%—comparable to a tree's shadow on a sunny day. They also devised a theoretical model that reliably predicted the shadow contrast.

Future Research Directions

According to the researchers, this demonstrated effect reveals that a transmitted laser beam's intensity can be regulated by introducing a secondary laser. Their next step is to explore additional materials and laser wavelengths capable of producing similar effects.

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Mesoporous Mos₂ Strategy Marks New Milestone in Perovskite Solar Cell Efficiency

Introduction to Photovoltaic Technology

Photovoltaic (PV) technology has witnessed substantial improvements in efficiency and performance, contributing to greater solar technology adoption. To continue enhancing solar cell capabilities, researchers around the world are innovating alternative designs and experimenting with various materials and cell architectures.

The Promise of Perovskite Solar Cells

Organic-inorganic hybrid perovskite-based solar cells, known for their favorable properties, have shown promising results, with efficiencies exceeding 25%. However, their instability and sensitivity to external factors like UV light and oxygen remain obstacles to widespread commercial use.

Innovative Strategy for Enhanced Performance

Scientists from the Ulsan National Institute of Science and Technology, Korea University, and partnering institutions recently developed an innovative strategy to enhance the performance of perovskite solar cells. Reported in Nature Nanotechnology, their approach involves implementing mesoporous molybdenum disulfide (MoS₂) as an electron transport layer (ETL) to boost efficiency and stability.

Advantages of Mesoporous ETLs

According to Donghwan Koo, Yunseong Choi, and their research team, mesoporous structured ETLs in perovskite solar cells (PSCs) improve surface contact with the perovskite layer, facilitating efficient charge separation and extraction, which leads to high-performance devices.

Limitations of Conventional ETL Materials

"The commonly used ETL material in PSCs, TiO₂, requires high-temperature sintering above 500°C and is prone to photocatalytic reactions under light exposure, which restricts its stability. Consequently, recent research has sought alternative ETL materials, like SnO₂."

The Role of Mesoporous MoS₂

Following previous research advancements, Koo, Choi, and their pursued performance improvements in perovskite solar cells by employing mesoporous ETLs. These layers incorporate tiny pores, measuring between 2 and 50 nanometers.

Characteristics of MoS₂

The team employed mesoporous MoSP₂, a multifunctional material known for its optoelectronic properties and prior applications in batteries, photodetectors, LEDs, and other technologies. They observed that integrating a mesoporous MoS₂ ETL produced solar cells with efficiencies exceeding 25% and demonstrated robust stability.

Enhanced Charge Transfer Dynamics

Koo, Choi, and their team highlighted that the MoS₂ interlayer enlarges the surface contact with the perovskite layer, thereby improving charge transfer between the two layers.

Lattice Compatibility and Performance

"Additionally, the lattice compatibility between MoS₂ and the perovskite layer support the growth of perovskite crystals with reduced residual strain in comparison to TiO₂. This mesoporous MoS₂ ETL structure yielded perovskite solar cells with efficiencies of 25.7% (0.08 cm², certified 25.4%) and 22.4% (1.00 cm²)."

Results and Future Implications

Initial testing revealed that the solar cells developed by the team achieved highly encouraging results, especially in comparison to those utilizing a TiO₂ ETL. Notably, the perovskite solar cells featureing a mesoporous MoS₂ interlayer exhibited stability, retaining 90% of their original power conversion efficiency (PCE) following more than 2,000 hours of continuous exposure to light.

Potential for Broader Adoption

These promising results may guide future initiatives focused on enhancing the efficiency and stability of organic-inorganic perovskite solar cells through the incorporation of a mesoporous MoS₂ layer. Such advancements could enable perovskite solar cells to compete with silicon-based photovoltaics, facilitating their broader adoption in the market.

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hamiltonian-parameters-quantum-simulators

Unlocking Quantum Simulations: Scientists Develop Techniques for Estimating Hamiltonian Parameters in Superconducting Quantum Simulators

Introduction

Scientists from Freie University Berlin, University of Maryland, NIST, Google AI, and Abu Dhabi aimed to estimate the free Hamiltonian parameters of bosonic excitations in superconducting quantum simulators. Their protocols, shared in an arXiv preprint, could enable highly precise quantum simulations that surpass classical computing.

The Call from Google AI

Jens Eisert, the paper's lead author, told "I was attending a conference in Brazil when i got a call from colleagues at the Google AI team."

Challenges in Calibrating Quantum Chips

"While working to calibrate their Sycamore superconducting quantum chip with Hamiltonian learning methods, they encountered substantial difficulties and called for help. With my background in analog quantum simulation and systems identification, I found their request particularly compelling."

Understanding Hamiltonian Learning

Initial Assumptions and Realizations

At first, Eisert assumed that the issue raised by his friends would be simple to address. However, he quickly discovered that it was more complex than expected, as the system's Hamiltonian operator frequencies were not precise enough to determine the unknown Hamiltonian from the available data.

"I invited two brilliant Ph.D. students, Ingo Roth and Dominik Hangleiter, and together, we promptly devised a solution using superresolution techniques--in theory, at least, until the data arrived," said Eisert.

Overcoming Complexities

"It took several more years before we fully understood how to make Hamiltonian learning robust enough for application in large-scale experiments."

"During that time, another Ph.D. student, Jonas Fuksa, joined the team, while the other two had already graduated. Pedram Roushan, the experimental lead of Google AI, remained steadfast and provided exceptional data, which ultimately helped us find a solution to the problem posed in the Zoom call years ago."

Techniques and Innovations in Hamiltonian Learning

Applying Superresolution

To uncover the Hamiltonian dynamics of a superconducting quantum simulator, Eisert and his team utilized several techniques. Initially, they applied superresolution to improve the precision of eigenvalue estimation and accurately determine Hamiltonian frequencies.

Manifold Optimization

They subsequently employed a technique called manifold optimization to retrieve the eigenspaces of the Hamiltonian operator, effectively reconstructing the Hamiltonian. Manifold optimization involves specialized algorithms designed to address complex problems where variables reside on a manifold (a smooth and curved space) instead of in conventional Euclidean space.

"To achieve reliable estimates, we integrated several concepts," Eisert explained.

The TensorEsprit Approach

"Understanding the processes of switching on and off was crucial, as these processes are neither perfect nor instantaneous (and not even unitary). Attempting to fit a Hamiltonian evolution that is partially non-Hamiltonian leads to significant complications. Ultimately, we developed new signal processing methods, termed TensorEsprit, which enabled robust recovery even for large system sizes."

Results and Future Implications

Precision and scalability of the Techniques

The researchers introduce a new method for implementing super-resolution in their paper, which they have termed TensorEsprit. By combining this method with a manifold optimization strategy, they effectively identified the Hamiltonian parameters for as many as 14 coupled superconducting qubits across two Sycamore processors.

Future Studies and Applications

"During the initial phase, grasping the overall importance of Hamiltonian learning methods was crucial," stated Eisert.

"One can meaningfully recover eigenspaces only when the eigenvalues are known with exceptional accuracy. During the later phases of the project, we learned firsthand why there are so few publications presenting data from Hamiltonian learning: it is inherently challenging to apply this approach to practical data."

The preliminary tests conducted by the researchers indicate that their proposed techniques may be scalable and effectively applicable to large quantum processors. Their findings could pave the way for similar methods aimed at characterizing the Hamiltonian parameters of quantum processors.

In their upcoming studies, Eisert and his colleagues intend to apply their methods to interacting quantum systems. They are also exploring the use of similar concepts derived from tensor networks in quantum systems made up of cold atoms, a concept originally introduced by physicist immanuel Bloch.

Broader Impact on Quantum Mechanics

The Importance of Knowing the Hamiltonian

"In my view, this field will be increasingly important in the future," stated Eisert. "A long-standing yet often undervalued question pertains to the nature of a system's Hamiltonian. This question is introduced in introductory quantum mechanics courses. Although it describes the system, it is typically assumed to be known, an assumption that is often erroneous."

"In the final analysis, experiments produce only data, meaning that in quantum mechanics, predictive capability exists only when the Hamiltonian is accurately defined. This leads to the inquiry of how one can extract it from the data."

Potential for High-Precision Quantum Simulations

In addition to enriching the conceptual framework surrounding Hamiltonian operators, the researchers' forthcoming studies may guide the evolution of quantum technologies. By facilitating the characterization of analog quantum simulators, they could unlock new pathways for achieving high-precision quantum simulations.

Conclusion

Quantum Systems and Their Future

"Analog quantum simulation enables the investigation of intricate quantum systems and materials by replicating them under highly controlled laboratory conditions," explained Eisert.

"This idea is meaningful--and linked to precise predictions--only when the Hamiltonian that characterizes the system is accurately known."

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Diffraction Casting: Revolutionizing Optical Computing for Next-Gen AI Applications

Introduction to Optical Computing

• The Need for Powerful Computing Solutions: The growing complexity of applications like artificial intelligence demands increasingly powerful, energy-intensive computers. Optical computing offers a potential solution for boosting speed and efficiency, but challenges remain in its practical implementation.

Understanding Diffraction Casting

• What is Diffraction Casting?: Introducing diffraction casting--a new design framework that tackles the current drawbacks and introduces groundbreaking concepts to optical computing, enhancing its potential for next-generation devices.

Challenges of Traditional Electronic Computing

• Limitations of Current Technology: Whether it's your smartphone or laptop, today's computing devices are all built on electronic technology. Yet, this approach has its drawbacks-chief among them, substantial heat production as performance rises and the looming limits of current fabrication techniques.

As a result, scientists are pursuing alternative computational techniques that aim to overcome these limitations and, ideally, provide innovative features and advantages.

The Promise of Optical Computing

• Harnessing the Speed of Light: One potential solution lies in optical computing, a concept that has been around for decades but has yet to achieve commercial success.

Optical computing fundamentally harnesses the speed of light waves and their complex interactions with various optical materials, all without generating heat. Additionally, light waves can pass through materials simultaneously without interference, theoretically enabling a highly parallel, power-efficient, and high-speed computing system.

Historical Context: Shadow Casting in Optical Computing

• The Early Attempts: "During the 1980s, researchers in Japan examined a method of optical computing called shadow casting, which could carry out simple logical operations. Nonetheless, their approach utilized bulky geometric optical designs akin to the vacuum tubes used in early digital computing. Although these methods were theoretically sound, they lacked the necessary flexibility and integration ease for practical utility," explained Associate Professor Ryoichi Horisaki of the Information Photonics Lab at the University of Tokyo.

Advancements through Diffraction Casting

• Enhancing Optical Elements: We present an optical computing approach known as diffraction casting, which enhances the concept of shadow casting. While shadow casting relies on light rays interacting with various geometries, diffraction casting leverages the inherent properties of light waves. This results in more spatially efficient and functionally adaptable optical elements that can be extended as needed for universal computing applications.

Numerical Simulations and Results

• Testing the Framework: "We executed numerical simulations that demonstrated very favorable results, using small black-and-white images measuring 16 by 16 pixels, which are even smaller than the icons displayed on a smartphone."

An All-Optical System for Data Processing

• From Optical to Digital: Horisaki and his team suggest an all-optical system, meaning that is only converts the final output into an electronic and digital format; all preceding stages of the system operate entirely optically. Their research has been published in Advanced Photonics.

Application and Representation of Data

• Utilizing Images as Data Sources: Their concept involves utilizing an images as a data source, which naturally indicates that this system could be applied to image processing. However, it can also represent other types of data, particularly that utilized in machine learning systems, in graphical form, combining the source image with a series of additional images that depict stages in logic operations.

Layers in Optical Processing

• A Visual Analogy: Imagine it as layers in an image editing software like Adobe Photoshop; you begin with an input layer, which is the source image, and then additional layers can be added on top. These layers can obscure, manipulate, or transmit information from the layer below. The final output--the top layer--results from the processing of this combination of layers.

The Process of Diffraction Casting

• Creating Digital Images: In this context, light will pass through these layers, crating an image--hence the term 'casing' in diffraction casting---on a sensor. This image will subsequently be converted into digital data for storage or display to the user.

Future Potential and Commercial Viability

• An Auxiliary Component in Computing: "Diffraction casting represents merely one component in a theoretical computer founded on this principle. It may be more appropriate to view it as an auxiliary element rather than a complete substitute for existing systems, similar to how graphical processing units serve as specialized components for graphics, gaming and machine learning tasks," stated lead author Ryosuke Mashiko.

Estimating Time for Commercial Readiness

"I estimate that it will take approximately 10 years before this technology becomes commercially viable, as significant effort is required for physical implementation, which despite being based on solid research, has not yet been developed."

Conclusion: The Road Ahead for Diffraction Casting

• Expanding into Quantum Computing: "As this stage, we are able to showcase the applicability of diffraction casting in carrying out the 16 essential logic operations foundational to much information processing. Moreover, our system has the potential to evolve into the burgeoning area of quantum computing, which transcends conventional methods. The future will determine the results."

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Biohybrid-swimming-robot-human-cells-muscle-functionality

Innovative Biohybrid Swimming Robot Developed Using Human Cells for Muscle Functionality

Introduction

Scientist from Brigham and Women's Hospital in the U.S. and the iPrint Institute in Switzerland have teamed up to develop a tiny swimming robot that uses human motor neurons and cardiomyocytes to simulate muscle tissue functionality.

Research Overview

Their research has been published in Science Robotics. In the same issue, Nicole Xu, a mechanical engineer at the University of Colorado Boulder, authored a Focus article discussing ongoing efforts to develop bioinspired robots using animal tissue.

The Inspiration Behind Biohybrid Robots

Science fiction writers and filmmakers have long envisioned combining electronics, computing, and animal tissue to create robots with distinctive, often fearsome traits. According to Xu, such work is currently underway in the real world.

Challenges in Robotic Dexterity

The abilities of animals, particularly humans, exceed the capacities of robots by a significant margin. A simple task like doing laundry illustrates this, requiring multiple skills such as sorting garments, setting washer and dryer cycles, and folding or hanging clothes.

The Biohybrid Robot Design

Engineering the Ray-Like Robot

The coordination of dexterity and cognitive abilities is essential for such tasks, leading roboticists to explore Biohybrid robots. In response, the team engineered a Ray-Like swimming robot with a computer-driven brain that manages human muscle cells activated by motor neurons.

Video

Creating the Robot's Muscle Tissue

Researchers used human pluripotent stem cells to culture motor neurons and cardiomyocytes in creating the robot. The cardiomyocytes were then guided to grow into muscle tissue on a scaffold designed to resemble ray fins, enabling interaction with motor neurons.

Innovations in Control Systems

Developing Electrical Synapses

The development of electrical synapses was enabled, and portion of the motor neurons were connected to an electronic processor functioning as the robot's brain. This processor incorporated Wi-Fi technology to transmit commands from human controllers to either fin.

Achieving Swimming Capability

This technique allowed the researchers to command the robot's movements, leading to its ability to swim. As the research progressed, the team discovered they could expertly maneuver the robot, enabling sharp turns and swimming at speeds reaching 0.52 ± 0.22 mm/s.

Conclusion

The development of this Biohybrid swimming robot marks a significant advancement in robotics, merging biological elements with engineering to enhance robotic functionality.

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hydrogel-based brain sensors for enhanced adhesion

Innovative Brain Sensor Enhances Transcranial Focused Ultrasound for Neurological Disorders

Introduction to Transcranial Focused Ultrasound

This non-invasive technique, known as transcranial focused ultrasound, employs high-frequency sound waves to stimulate targeted brain regions, offering a potential breakthrough in treating neurological disorders like drug-resistant epilepsy and recurrent tremors.

Development of the Innovative Sensor

Researchers at Sungkyunkwan University (SKKU), IBS, and the Korea Institute of Science and Technology have designed an innovative sensor for transcranial focused ultrasound. Their study, featured in Nature Electronics, describes a flexible sensor that conforms to cortical surfaces, facilitating neural signal detection and low-intensity ultrasound-based brain stimulation.

Challenges with Previous Brain Sensors

"Previous efforts to develop brain sensors struggled to achieve precise signal measurement because they couldn't fully adapt to the brain's complex folds," remarked Donghee Son, the supervising author of the study, in an interview with Tech Xplore.

"The inability to precisely analyze the entire brain surface limited accurate diagnosis of brain lesions. Despite the innovative ultra-thin brain sensor developed by Professors John A. Rogers and Dae-Hyeong Kim, it encountered difficulties in tightly adhering to areas with severe curvature."

Limitations of Existing Sensors

The brain sensor created by Professors Rogers and Kim demonstrated improved precision in collecting surface-level measurements. However, it exhibited notable limitations, including difficulty adhering to areas with significant curvature and a tendency to shift from its attachment point due to micro-movements and cerebral spinal fluid flow.

The limitations observed reduce the sensor's suitability for clinical use, as they hinder its ability to capture brain signals reliably in specific areas over longer duration's.

The New Sensor Design

To overcome these challenges, Son and colleagues developed a new sensor designed for better adhesion to curved brain surfaces, ensuring stable, long-term data collection.

"The sensor we engineered is capable of conforming to even the most curved brain regions, ensuring a firm attachment to brain tissue," said Son. "This strong bond allows for long-term, precise measurement of brain signals from specific areas."

ECoG Sensor Features

The ECoG sensor designed by Son and his team attaches firmly to brain tissue, ensuring no voids are created. This feature markedly decreases noise from external mechanical movements.

"This feature plays a crucial role in improving the efficacy of epilepsy treatment using low-intensity focused ultrasound (LIFU)," noted Son. "Although ultrasound is recognized for its ability to reduce epileptic activity, the variability in patient conditions and individual differences present significant obstacles in customizing treatments."

Personalized Ultrasound Stimulation Therapies

Recently, numerous research teams have been focused on developing personalized ultrasound stimulation therapies for epilepsy and various neurological disorders. To tailor these treatments to the specific needs of each patient, it is essential to measure their brain waves in real-time while simultaneously stimulating targeted brain regions.

"Traditional sensors attached to the brain surface faced challenges in this regard, as the vibrations induced by ultrasound generated considerable noise, hindering real-time monitoring of brain waves," stated Son.

"This limitation significantly hindered the development of personalized treatment strategies. Our sensor substantially minimizes noise, facilitating effective epilepsy treatment through tailored ultrasound stimulation."

Structure of the Shape-Morphing Sensor

Son and his colleagues developed a shape-morphing brain sensor with three primary layers. These consist of a hydrogel-based layer for both physical and chemical bonding with tissue, a self-healing polymer layer that adjusts its form to fit the surface beneath, and a thin, stretchable layer containing gold electrodes and interconnects.

Son noted that when the sensor is positioned on the brain surface, the hydrogel layer activates a gelation process that establishes a strong and instant bond with the brain tissue.

"Subsequently, the self-healing polymer substrate starts to deform, adapting to the curvature of the brain, which enhances the contact area between the sensor and the tissue over time. Once the sensor has completely conformed to the brain's contours, it is primed for operation."

Advantages of the New Sensor

The sensor created by this research team offers multiple advantages compared to other brain sensors developed in recent years. Notably, it can securely attach to brain tissue while adapting its shape to conform tightly to surfaces, regardless of their curvature.

By conforming to the contours of curved surfaces, the sensor effectively reduces vibrations generated by external ultrasound stimulation. This capability enables physicians to accurately measure brain wave activity in patients, both in standard conditions and during ultrasound procedures.

Future Applications

According to Son, we foresee this technology being applicable not only for epilepsy management but also for the diagnosis and treatment of multiple brain disorders. The most crucial aspect of our research is the synergy between tissue-adhesive technology, which enables robust adhesion to brain tissue, and shape-morphing technology, allowing the sensor to conform precisely to the brain's surface without leaving any gaps.

Testing and Future Development

To date, the novel sensor engineered by Son and his team has undergone testing on conscious, living rodents. The results obtained were exceptionally promising, demonstrating the team's ability to accurately measure brain waves and mange seizures in these animals.

The researchers aim to expand the sensor's capabilities by developing a high-density array based on their initial design. Upon successful completion of clinical trials, this enhanced sensor could be utilized to diagnose and treat epilepsy and other neurological disorders, potentially adancing the effectiveness of prosthetic technologies.

With 16 electrode channels currently integrated into our brain sensor, Son highlighted an area ripe for improvement concerning high-resolution mapping of brain signals.

"Taking this into consideration, our strategy involves significantly augmenting the number of electrodes to enable comprehensive and high-resolution brain signal analysis. We also aspire to devise a minimally invasive implantation technique for the brain sensor on the surface of the brain, aiming for its application in clinical research."

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