Best of the Week: Big Pharma Restructuring, Raman Spectroscopy, Artificial Intelligence

This week, Spectroscopy published a variety of articles on the hottest topics in analytical science. Below, we’ve highlighted some of the most popular articles, according to our readers. Happy reading!

What Restructuring at Big Pharma Companies Means for Analytical Scientists

Caroline Hroncich

On April 25, 2024, Bristol Meyers Squibb (BMS) announced that it will cut $1.5 billion in costs by the end of 2025, including laying off more than 2000 employees. This continues the trend of layoffs from Big Pharma compies like Pfizer, Genentech, Sanofi, and GlaxoSmithKline. This could prove worrisome for analytical scientists, who typically look for roles at these companies in lieu of academic laboratories. While analytical scientists are feeling positive about the job market, this sentiment does not come without challenges. Here, we discuss what Big Pharma restructuring could mean for the future of analytical chemistry.

Raman and AI for Pathological Classification

Aaron Acevedo

Using artificial intelligence alongside analytical chemistry has become a hot topic among scientists. Machine learning has been particularly interesting, since it can self-iterate and optimize problem solving for modeling data in an automated way using computational algorithms. These methods can be used in image and speech recognition, natural language processing, and predictive modeling, among other areas. In this study out of Biahang University and Capital Medical University in Beijing, China, scientists used machine learning technology in combination with Raman spectroscopy to streamline the pathological classification process.

Surface-Enhanced Raman Scattering (SERS) Spectroscopy Used to Detect COVID-19 Virus

Aaron Acevedo

The COVID-19 pandemic caused an unprecedented need for rapid, sensitive, and cost-effective point-of-care diagnostic tests to prevent and mitigate the spread of the SARS-CoV-2 virus. Spectroscopy techniques have been used to detect the virus, protecting against the disease, and helping to screening for long COVID. In this study, the scientists demonstrated an advanced lateral flow immunoassay (LFIA) platform with dual-functional (colorimetric and surface-enhanced Raman scattering [SERS]) detection of the spike 1 (S1) protein of SARS-CoV-2. This system was made in hopes to make a new point-of-care platform for the early detection of SARS-CoV-2 and other respiratory virus infections.

Deep Level Transient Spectroscopy Reveals Influence of Defects on 2D Semiconductor Devices

Will Wetzel

Semiconductors, which are mostly used for clean energy products like self-driving vehicle circuits and solar cells, have become a hot topic in spectroscopy circles. Their production is important to society, but it is also important to ensure that the materials used to construct semiconductors are of optimal condition to prevent any defects with their electrical properties. Vibrational spectroscopic techniques have previously been used to characterize, optimize, and analyze semiconductor materials. In this study, researchers from the Institute of Electrical and Microengineering in Lausanne, Switzerland, led by Andras Kis, delved into analyzing 2D semiconductors and their electrical defects. This was done by using deep level transient spectroscopy (DLTS).

Using LIBS to Gauge the Hardness of Steel Rails

Will Wetzel

The steel industry has been essential to commerce and transportation since the Industrial Revolution, with steel being used in various important projects. This includes rail lines that allow locomotives and trains to transport good and people more efficiently. With how used trains are today, it is vital that the tracks they travel on remain in optimal condition. As such, the hardness of the steel rails used on these tracks must be measured. In this study from Zefeng Yang of Southwest Jiaotong University in Chengdu, China, laser-induced breakdown spectroscopy (LIBS) was used along machine learning technique to evaluate the hardness of steel rails for essential infrastructure projects.