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Researchers at Columbia Engineering report today that they have developed the first nanomaterial that demonstrates “photon avalanching,” a process that is unrivaled in its combination of extreme nonlinear optical behavior and efficiency. The realization of photon avalanching in nanoparticle form opens up a host of sought-after applications, from real-time super-resolution optical microscopy, precise temperature and environmental sensing, and infrared light detection, to optical analog-to-digital conversion and quantum sensing.

“Nobody has seen avalanching behavior like this in nanomaterials before,” said James Schuck, associate professor of mechanical engineering, who led the study published today by Nature. “We studied these new nanoparticles at the single-nanoparticle level, allowing us to prove that avalanching behavior can occur in nanomaterials. This exquisite sensitivity could be incredibly transformative. For instance, imagine if we could sense changes in our chemical surroundings, like variations in or the actual presence of molecular species. We might even be able to detect coronavirus and other diseases.”

Avalanching processes—where a cascade of events is triggered by series of small perturbations—are found in a wide range of phenomena beyond snow slides, including the popping of champagne bubbles, nuclear explosions, lasing, neuronal networking, and even financial crises. Avalanching is an extreme example of a nonlinear process, in which a change in input or excitation leads to a disproportionate—often disproportionately large—change in output signal. Large volumes of material are usually required for the efficient generation of nonlinear optical signals, and this had also been the case for avalanching, until now.

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Over the past decade or so, deep neural networks have achieved very promising results on a variety of tasks, including image recognition tasks. Despite their advantages, these networks are very complex and sophisticated, which makes interpreting what they learned and determining the processes behind their predictions difficult or sometimes impossible. This lack of interpretability makes deep neural networks somewhat untrustworthy and unreliable.

Researchers from the Prediction Analysis Lab at Duke University, led by Professor Cynthia Rudin, have recently devised a technique that could improve the interpretability of deep neural networks. This approach, called whitening (CW), was first introduced in a paper published in Nature Machine Intelligence.

“Rather than conducting a post hoc analysis to see inside the hidden layers of NNs, we directly alter the NN to disentangle the latent space so that the axes are aligned with known concepts,” Zhi Chen, one of the researchers who carried out the study, told Tech Xplore. “Such disentanglement can provide us with a much clearer understanding of how the network gradually learns concepts over layers. It also focuses all the information about one concept (e.g., “lamp,” “bed,” or “person”) to go through only one neuron; this is what is meant by disentanglement.”

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On 14 November 2014, a bright flash flagged the All Sky Automated Survey for Supernovae, or ASAS-SN — a global network of 20 telescopes managed at Ohio State University in the U.S. The flash originated in galaxy ESO 253–3, located 570 million light-years away.

The sudden burst of energy was examined by astronomers and categorized as a likely supernova and assigned the event designation ASASSN-14ko. Six years later, Anna Payne, a NASA Graduate Fellow at the University of Hawai’i at Mānoa, discovered it was something much different.

While sifting through ASAS-SN data on active galactic nuclei, she discovered a previously unnoticed periodic series of bright flares emanating from galaxy ESO 253–3 — repeating in such a way that led directly back to ASASSN-14ko as the point of first detection.

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“This is perhaps the hardest part of all DNA storage approaches. If you can get the cells to directly talk to a computer, and interface its DNA-based memory system with a silicon-based memory system, then there are lots of possibilities in the future.”

The work builds on a CRISPR-based cellular recorder Wang had previously designed for E. coli bacteria, which detects the presence of certain DNA sequences inside the cell and records this signal into the organism’s genome.

The system includes a DNA-based “sensing module” that produces elevated levels of a “trigger sequence” in response to specific biological signals. These sequences are incorporated into the recorder’s “DNA ticker tape” to document the signal.

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Meet the “Yangtze River Three Gorges 1”, an electric cruise ship, announced in December, that is poised to become the world’s largest of its kind (among EVs).

According to the brief info, it will be launched in July and enter service in November of 2021, on popular tourist routes: the Two Dams and One Gorge, the Yichang Yangtze River Night Cruise, and the Three Gorges Shiplift.

Not only will the size and passenger capacity be the highest, but also the battery capacity — roughly 7.5 MWh (an equivalent of 75–100 long-range electric cars). The LFP-type batteries (over 10000 cells) will be supplied by CATL.

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Working with theorists in the University of Chicago’s Pritzker School of Molecular Engineering, researchers in the U.S. Department of Energy’s (DOE) Argonne National Laboratory have achieved a scientific control that is a first of its kind. They demonstrated a novel approach that allows real-time control of the interactions between microwave photons and magnons, potentially leading to advances in electronic devices and quantum signal processing.

Microwave photons are forming the that we use for wireless communications. On the other hand, magnons are the elementary particles forming what scientists call “spin waves”—wave-like disturbances in an ordered array of microscopic aligned spins that can occur in certain magnetic materials.

Microwave photon-magnon interaction has emerged in recent years as a promising platform for both classical and processing. Yet, this interaction had proved impossible to manipulate in real time, until now.

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A keen sense of smell is a powerful ability shared by many organisms. However, it has proven difficult to replicate by artificial means. Researchers combined biological and engineered elements to create what is known as a biohybrid component. Their volatile organic compound sensor can effectively detect odors in gaseous form. They hope to refine the concept for use in medical diagnosis and the detection of hazardous materials.

Electronic devices such as cameras, microphones and pressure sensors enable machines to sense and quantify their environments optically, acoustically and physically. Our sense of smell however, despite being one of nature’s most primal senses, has proven very difficult to replicate artificially. Evolution has refined this sense over millions of years and researchers are working hard to catch up.

“Odors, airborne chemical signatures, can carry useful information about environments or samples under investigation. However, this information is not harnessed well due to a lack of sensors with sufficient sensitivity and selectivity,” said Professor Shoji Takeuchi from the Biohybrid Systems Laboratory at the University of Tokyo. “On the other hand, biological organisms use information extremely efficiently. So we decided to combine existing biological sensors directly with artificial systems to create highly sensitive volatile organic compound (VOC) sensors. We call these biohybrid sensors.”

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