Researchers from the University of Rochester and University of California, Santa Barbara, have made a groundbreaking discovery that could change the game for various industries. By engineering a laser device smaller than a penny, they’ve created a tool that can power LiDAR systems in self-driving vehicles to gravitational wave detection – one of the most delicate experiments in existence.

The new chip-scale laser is a marvel of miniaturization, capable of conducting extremely fast and accurate measurements by precisely changing its color across a broad spectrum of light at rates of about 10 quintillion times per second. Unlike traditional silicon photonics, this laser is made with synthetic material lithium niobate, leveraging the Pockels effect to change the refractive index of a material when an electric field is present.

This tiny powerhouse has numerous applications that can already benefit from its designs. For instance, it can drive a LiDAR system on a spinning disc and identify objects at highway speeds and distances. The researchers demonstrated this capability by using their laser to spot toy building blocks forming the letters U and R.

Another significant application is the Pound-Drever-Hall (PDH) laser frequency locking technique, essential for optical clocks that can measure time with extreme precision. A typical setup would require instruments the size of a desktop computer, but the chip-scale laser can integrate all these components into a single tiny chip that can be tuned electrically.

The research was supported in part by the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation, showcasing the potential of this miniature marvel to revolutionize metrology and beyond.

The portable sensor, called the E-Tongue, has been developed to empower people to detect lead contamination in their own homes. This device was tested through a citizen science project across four Massachusetts towns and has shown promise as a rapid and reliable tool for at-home detection of lead in drinking water.

Ingesting even tiny amounts of lead can harm the human brain and nervous system, especially in young children. Traditional water tests are costly and time-consuming, requiring specialized scientific equipment and long processing times. The E-Tongue device addresses this issue by allowing users to analyze water samples and receive a color-coded reading on their smartphone app.

The researchers behind the E-Tongue worked with 317 residents from four local towns to test its usability and performance. The process was simple: combine a sample of tap water with a premade buffer solution, follow three steps on the smartphone app, and wait for the results.

If lead is detected above the EPA’s maximum allowed level of 10 parts per billion, the researchers verified the results through a certified laboratory using traditional detection methods to ensure accuracy. The E-Tongue device was found to be reliable in detecting lead contamination, empowering communities to take action and protect their health.

The authors acknowledge funding from the National Science Foundation and hope that this tool will soon be a practical option for detecting and mitigating heavy metal contaminants in municipal water sources. By putting knowledge and power directly into people’s hands, the E-Tongue device has the potential to make a significant impact on public health and community safety.

The world of industrial laser processes is on the cusp of a revolution. Machine learning has taken center stage, enabling researchers at Empa’s Advanced Materials Processing laboratory in Thun to simplify complex laser-based techniques. The goal? To make these processes more affordable, efficient, and accessible for industries such as automotive and aviation, where precision is paramount.

Additive manufacturing (3D printing) using lasers is one such process that has been optimized using machine learning. Researchers Giulio Masinelli and Chang Rajani focused on the powder bed fusion (PBF) method, which involves melting metal powder in exactly the right spots to create a final component. Before production begins, however, a series of preliminary tests is typically required to determine the optimal settings for parameters such as scanning speed and laser power.

The two researchers used machine learning to reduce these experiments by around two-thirds while maintaining product quality. They “taught” their algorithm to recognize when the laser was in conduction or keyhole mode (where metal is melted or vaporized, respectively) using optical data from sensors incorporated in the laser machines. Based on this information, the algorithm determined the settings for the next test run.

This breakthrough has far-reaching implications. “We hope that our algorithm will enable non-experts to use PBF devices,” says Masinelli. Integration into the firmware of laser welding machines by device manufacturers would be all it takes to make machine learning-driven 3D printing accessible to a wider audience.

The researchers have also explored real-time optimization of laser welding processes using special computer chips called field-programmable gate arrays (FPGAs). These FPGAs enable the evaluation and decision-making process to occur in near real-time, even for complex tasks such as observing and controlling laser parameters.

Empa’s Masinelli and Rajani are confident that machine learning and artificial intelligence can contribute significantly more to the field of laser processing of metals. They will continue to develop their algorithms and models, expanding their area of application through collaboration with research and industry partners.

The future looks bright for industrial laser processes, thanks to the power of machine learning.