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Present-day liquid-state lasers are based on organic dyes. Here we demonstrate an alternative class of liquid lasers that use solutions of colloidal quantum dots (QDs). Previous efforts to realize such devices have been hampered by the fast non-radiative Auger recombination of multicarrier states required for optical gain. Here we overcome this challenge by using type-(I + II) QDs, which feature a trion-like optical gain state with strongly suppressed Auger recombination. When combined with a Littrow optical cavity, static (non-circulated) solutions of these QDs exhibit stable lasing tunable from 634 nm to 575 nm. These results indicate the feasibility of technologically viable dye-like QD lasers that exhibit broad spectral tunability and, importantly, provide stable operation without the need for a circulation system—a standard attribute of traditional dye lasers. The latter opens the door to less complex and more compact devices that can be readily integrated with various optical and electro-optical systems. An additional advantage of these lasers is the wide range of potentially available wavelengths that can be selected by controlling the composition, size and structure of the QDs.


Liquid lasers based on solutions of colloidal quantum dots exhibit a trion-like optical gain state with suppressed Auger recombination, which combined with a Littrow optical cavity enables stable and tunable liquid-state lasing.

Researchers at Lawrence Livermore National Laboratory (LLNL) have developed a new approach that combines generative artificial intelligence (AI) and first-principles simulations to predict three-dimensional (3D) atomic structures of highly complex materials.

This research highlights LLNL’s efforts in advancing machine learning for materials science research and supporting the Lab’s mission to develop innovative technological solutions for energy and sustainability.

The study, recently published in Machine Learning: Science and Technology, represents a potential leap forward in the application of AI for materials characterization and inverse design.

Researchers have set a new record for quantum entanglement — bringing reliable quantum computers a step closer to reality. The scientists successfully entangled 24 “logical qubits” — low-error quantum bits of information created by combining multiple physical qubits. This is the highest number ever achieved to date.

They also demonstrated that logical qubits can maintain error correction as the number of qubits increases, a crucial step toward larger, more fault-tolerant quantum systems. The researchers detailed their work in a study published Nov. 18 on the preprint database arXiv.

The Geminid meteor shower is one of the most prolific annual meteor showers, impressing skywatchers year after year.

It is possible to see up to 120 meteors per hour under dark conditions when the Geminids peak each year.

Initially a variant of LSTM known as AWD LSTM was pre trained (unsupervised pre training) for language modelling task using wikipedia articles. In the next step the output layer was turned into a classifier and was fine tuned using various datasets from IMDB, yelp etc. When the model was tested on unseen data, sate of the art results were obtained. The paper further went on to claim that if a model was built using 10,000 rows from scratch then fine tuning the above model (transfer learning) would give much better results with 100 rows only. The only thing to keep in mind is they did not used a transformer in their architecture. This was because both these concepts were researched parallely (transformers and transfer learning) so researchers on both the sides had no idea of what work the other was doing. Transformers paper came in 2017 and ULMFit paper (transfer learning) came in early 2018.

Now architecture wise we had state of the art architecture i.e. Transformers and training wise we have a very beautiful and elegant concept of Transfer Learning. LLMs were the outcome of the combination of these 2 ideas.

Summary: The dural sinuses and skull bone marrow serve as key communication hubs between the brain’s central immune system and the body’s peripheral immune system. These regions may act as “traffic lights,” allowing immune signals to flow between the brain and body, challenging the traditional view of the blood-brain barrier as an absolute divide.

Researchers found inflammatory activity in these areas correlates with inflammation in both the brain and body, offering new insights into conditions like depression. This discovery could pave the way for innovative treatments targeting these hubs to address immune-related conditions more precisely.

Scientists have developed the first electrically pumped continuous-wave semiconductor laser composed exclusively of elements from the fourth group of the periodic table—the “silicon group.”

Built from stacked ultrathin layers of germanium-tin and germanium-tin, this new laser is the first of its kind directly grown on a silicon wafer, opening up new possibilities for on-chip integrated photonics. The findings have been published in Nature Communications. The team includes researchers from Forschungszentrum Jülich, FZJ, the University of Stuttgart, and the Leibniz Institute for High Performance Microelectronics (IHP), together with their French partner CEA-Leti.

The rapid growth of artificial intelligence and the Internet of Things are driving the demand for increasingly powerful, energy-efficient hardware. Optical data transmission, with its ability to transfer vast amounts of data while minimizing , is already the preferred method for distances above 1 meter and is proving advantageous even for shorter distances. This development points towards future microchips featuring low-cost photonic integrated circuits (PICs), offering significant cost savings and improved performance.

Forest ecosystems of the future will have to cope with very different conditions to those of today. For this reason, researchers at the Technical University of Munich (TUM) state that a strategic approach to forest management is crucial. To this end, the research team has developed iLand: a simulation model that can compute long-term developments of large forest landscapes, right down to the individual tree—including disturbances from bark beetles to wildfires.

Charred tree trunks and blackened soil are typical of the desolation that a leaves behind. Inevitably, the question arises whether it will be possible to restore a green natural landscape. According to Rupert Seidl, Professor of Ecosystem Dynamics and Forest Management, this is possible, but the “how” decides how much the new forest will benefit the climate, nature and people.

“Today’s forest ecosystems are not particularly well adapted to future climate conditions,” says Seidl. “Over the next decades they will presumably come under increasing pressure from water shortage and insect pests, and may even die off. This is why it makes sense to use measures such as the reforestation of disturbed areas to strategically select tree species and take future developments into consideration.”

Therapeutic mRNAs offer great potential as a versatile and precise tool against cancer and other diseases. However, the therapeutic effectiveness is limited by the poor translation uptake of naked mRNA. To circumvent this challenge, researchers from VIB, VUB, Ghent University, and eTheRNA Immunotherapies developed an immunotherapeutic platform based on lipid-based nanoparticles (LNPs).

In different cancer models, applying a novel mixture of immunotherapeutic mRNA encapsulated in LNPs led to a clearly improved therapeutic efficacy with limited side effects. This proves the added value of the platform to the development of effective mRNA immunotherapies. The work is published in the journal Nature Communications.

The COVID-19 pandemic and recent Nobel Prize recognition have spotlighted mRNA therapies as a promising approach for diseases like cancer. With precision, scalability, and controlled , mRNA-based immunotherapy can encode proteins that stimulate the immune system to target and destroy cancer cells. Yet, naked mRNA is unstable, prone to degradation, and poorly absorbed by cells, limiting its effectiveness. This makes the development of reliable delivery methods essential for the future success of mRNA immunotherapies.