Bruker Acquires Scientific Software Provider Arxspan
Bruker Corporation (Billerica, Massachusetts) has acquired Arxspan LLC, a provider of cloud-based scientific software and workflow solutions based in Southborough, Massachusetts.
Bruker Corporation (Billerica, Massachusetts) has acquired Arxspan LLC, a provider of cloud-based scientific software and workflow solutions based in Southborough, Massachusetts. Arxspan, which sells cloud-based products for the management of research data under the Arxlab brand, focuses on serving pharmaceutical and biopharma customers.
With the acquisition, Bruker can now provide a range of software tools for customers in the chemistry, pharmaceutical, biopharmaceutical, and analytical laboratory markets. Together with the Mestrelab strategic partnership and majority investment, the acquisition of Arxspan will allow Bruker to offer new chemistry and biopharmaceutical software tools support drug discovery and development.
“The Arxspan acquisition, coupled with our strategic partnership with Mestrelab, positions Bruker firmly in the field of cloud-based, scientific software for our chemistry and pharma customers,” said Falko Busse, group president for Bruker Biospin. “Our new software solutions enable our biopharma customers to increase their productivity in drug discovery and development.”
Scientists Unveil Better Mixing Rule for Absorption Spectroscopy of Aerosols and Colloids
June 16th 2025Researchers have introduced a simple yet powerful new rule based on Rayleigh scattering theory that accurately links the absorption behavior of composite media, like aerosols or colloids, to the properties of their nanoparticle constituents.
Short Tutorial: Complex-Valued Chemometrics for Composition Analysis
June 16th 2025In this tutorial, Thomas G. Mayerhöfer and Jürgen Popp introduce complex-valued chemometrics as a more physically grounded alternative to traditional intensity-based spectroscopy measurement methods. By incorporating both the real and imaginary parts of the complex refractive index of a sample, this approach preserves phase information and improves linearity with sample analyte concentration. The result is more robust and interpretable multivariate models, especially in systems affected by nonlinear effects or strong solvent and analyte interactions.
