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Category: nanotechnology – Page 71

The human mind is by far one of the most amazing natural phenomena known to man. It embodies our perception of reality, and is in that respect the ultimate observer. The past century produced monumental discoveries regarding the nature of nerve cells, the anatomical connections between nerve cells, the electrophysiological properties of nerve cells, and the molecular biology of nervous tissue. What remains to be uncovered is that essential something – the fundamental dynamic mechanism by which all these well understood biophysical elements combine to form a mental state. In this chapter, we further develop the concept of an intraneuronal matrix as the basis for autonomous, self–organized neural computing, bearing in mind that at this stage such models are speculative. The intraneuronal matrix – composed of microtubules, actin filaments, and cross–linking, adaptor, and scaffolding proteins – is envisioned to be an intraneuronal computational network, which operates in conjunction with traditional neural membrane computational mechanisms to provide vastly enhanced computational power to individual neurons as well as to larger neural networks. Both classical and quantum mechanical physical principles may contribute to the ability of these matrices of cytoskeletal proteins to perform computations that regulate synaptic efficacy and neural response. A scientifically plausible route for controlling synaptic efficacy is through the regulation of neural transport of synaptic proteins and of mRNA. Operations within the matrix of cytoskeletal proteins that have applications to learning, memory, perception, and consciousness, and conceptual models implementing classical and quantum mechanical physics are discussed. Nanoneuroscience methods are emerging that are capable of testing aspects of these conceptual models, both theoretically and experimentally. Incorporating intra–neuronal biophysical operations into existing theoretical frameworks of single neuron and neural network function stands to enhance existing models of neurocognition.

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Pancreatic cancer is one of the deadliest types of cancers in humans. It is the fourth leading cause of cancer-related deaths in the western world. The early stages of the disease often progress without symptoms, so diagnosis is usually very late.

Another problem: Advanced tumors – and their metastases – can no longer be completely removed. Chemotherapies, in turn, attack not only the tumor cells but also healthy cells throughout the body. Innovative nanoparticles could be a new approach to treat cancer more precisely.

The approach was developed by a research team from the Max Planck Institute (MPI) for Multidisciplinary Sciences, the University Medical Center Göttingen (UMG), and the Karlsruhe Institute of Technology (KIT). The therapy is now to be optimized for clinical application as quickly as possible.

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A color wheel (CW) is one of the most essential devices for contemporary projection displays because it provides the color initialization definition and determines the color performance of the whole system. However, conventional color wheels remain limited in terms of color performance and efficiency because of the light-absorbing material and time sequential color generation. Quantum dots, found in 1981 and known as a kind of quasi-zero-dimensional nanomaterial, exhibit excellent features for displays due to their quantum confinement effect, which won the 2023 Nobel Prize in Chemistry. Inspired by this, the paper systematically demonstrates a quantum-dot color wheel (QD-CW) device through theoretical derivation, simulation analysis, and experimental verification. The theoretical model to define the duty circle ratio is presented for the QD-CW and verified by Monte Carlo ray-tracing simulation. In terms of experimental verification, the QD-CW device is realized by multiple rounds of a photolithography process, and then assembled into a blue laser pumped projection prototype for full-color display. The chromaticity coordinates of white-balanced output are finally located at (0.317,0.338), which matches well with a standard D65 source. The color gamut area of the QD-CW device reaches 116.6% NTSC, and the average light conversion efficiency (LCE) of the prepared QD-CW is 57.0%. The proposed QD-CW device has ∼40% higher color gamut area and 1.2× higher LCE than a conventional CW device. These exciting findings show a groundbreaking approach to color generation in projection displays, which are expected to shed light on other high-quality display applications.

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Summary: Researchers developed an experimental computing system, resembling a biological brain, that successfully identified handwritten numbers with a 93.4% accuracy rate.

This breakthrough was achieved using a novel training algorithm providing continuous real-time feedback, outperforming traditional batch data processing methods which yielded 91.4% accuracy.

The system’s design features a self-organizing network of nanowires on electrodes, with memory and processing capabilities interwoven, unlike conventional computers with separate modules.

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An experimental computing system physically modeled after the biological brain has “learned” to identify handwritten numbers with an overall accuracy of 93.4%. The key innovation in the experiment was a new training algorithm that gave the system continuous information about its success at the task in real time while it learned. The study was published in Nature Communications.

The algorithm outperformed a conventional machine-learning approach in which training was performed after a batch of data had been processed, producing 91.4% accuracy. The researchers also showed that memory of past inputs stored in the system itself enhanced learning. In contrast, other computing approaches store memory within software or hardware separate from a device’s processor.

For 15 years, researchers at the California NanoSystems Institute at UCLA, or CNSI, have been developing a new platform technology for computation. The technology is a brain-inspired system composed of a tangled-up network of wires containing silver, laid on a bed of electrodes. The system receives input and produces output via pulses of electricity. The individual wires are so small that their diameter is measured on the nanoscale, in billionths of a meter.

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As information and communication technologies (ICT) process data, they convert electricity into heat. Already today, the global ICT ecosystem’s CO2 footprint rivals that of aviation. It turns out, however, that a big part of the energy consumed by computer processors doesn’t go into performing calculations. Instead, the bulk of the energy used to process data is spent shuttling bytes between the memory to the processor.

In a paper published in the journal Nature Electronics, researchers from EPFL’s School of Engineering in the Laboratory of Nanoscale Electronics and Structures (LANES) present a new processor that tackles this inefficiency by integrating data processing and storage onto a single device, a so-called in-memory processor.

They broke new ground by creating the first in-memory processor based on a two-dimensional to comprise more than 1,000 transistors, a key milestone on the path to industrial production.

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Connectomics, the ambitious field of study that seeks to map the intricate network of animal brains, is undergoing a growth spurt. Within the span of a decade, it has journeyed from its nascent stages to a discipline that is poised to (hopefully) unlock the enigmas of cognition and the physical underpinning of neuropathologies such as in Alzheimer’s disease.

At its forefront is the use of powerful electron microscopes, which researchers from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Samuel and Lichtman Labs of Harvard University bestowed with the analytical prowess of machine learning. Unlike traditional electron microscopy, the integrated AI serves as a “brain” that learns a specimen while acquiring the images, and intelligently focuses on the relevant pixels at nanoscale resolution similar to how animals inspect their worlds.

“SmartEM” assists connectomics in quickly examining and reconstructing the brain’s complex network of synapses and neurons with nanometer precision. Unlike traditional electron microscopy, its integrated AI opens new doors to understand the brain’s intricate architecture. “SmartEM: machine-learning guided electron microscopy” has been published on the pre-print server bioRxiv.

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More sophisticated manipulation of complicated materials and their spin states at short time scales will be needed to create the next generation of spintronic devices. But, a thorough understanding of the fundamental physics underpinning nanoscale spin manipulation is necessary to fully utilize these powers for more energy-efficient nanotechnologies.

The JILA team and colleagues from institutions in Sweden, Greece, and Germany investigated the spin dynamics within a unique substance known as a Heusler compound—a combination of metals that exhibits properties similar to those of a single magnetic material. In their investigation, the scientists used a cobalt, manganese, and gallium combination that acted as an insulator for electrons with downwardly oriented spins and a conductor for those with upwardly aligned spins.

Scientists used extreme ultraviolet high-harmonic generation (EUV HHG) light as a probe to track the re-orientations of the spins inside the compound after exciting it with a femtosecond laser. Tuning the color of the EUV HHG probe light is the key to accurately interpreting the spin re-orientations.

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