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YouTube video by Vera Danilina

Participe do abaixo-assinado pela aprovação do PL 4687/2025, de autoria da deputada federal Professora Luciene Cavalcante!

Tesla is attempting to conceal the details of three separate accidents involving its Robotaxi service in Austin, Texas, despite having...

It has recently been shown that configuration state functions (CSFs) with local orbitals can provide a compact reference state for low-spin open-shell electronic structures, such as antiferromagnetic ...

YouTube video by MSNBC

Collision-induced dissociation mass spectrometry (CID-MS) is an important tool in analytical chemistry for the structural elucidation of unknown compounds. The theoretical prediction of the CID spectra plays a critical role in supporting and accelerating this process. To this end, we adapt the recently developed QCxMS2 program originally designed for the calculation of electron ionization (EI) spectra to enable the computation of CID-MS. To account for the fragmentation conditions characteristic of CID within the automated reaction network discovery approach of QCxMS2 we adapted the internal energy distribution to match the experimental conditions. This distribution can be adjusted via a single parameter to approximate various activation settings, thereby eliminating the need for explicit simulations of the collisional process. We evaluate our approach on a test set of 13 organic molecules with diverse functional groups, compiled specifically for this study. All reference spectra were recorded consistently under the same measurement conditions, including both CID and higher-energy collisional dissociation (HCD) modes. Overall, QCxMS2 achieves a good average entropy similarity score (ESS) of 0.687 for the HCD spectra and 0.773 for the CID spectra. The direct comparison to experimental data demonstrates that the QCxMS2 approach, even without explicit modeling of collisions, is generally capable of computing both CID and HCD spectra with reasonable accuracy and robustness. This highlights its potential as a valuable tool for integration into structure elucidation workflows in analytical mass spectrometry.

YouTube video by Cher

We introduce MindlessGen, a Python-based generator for creating chemically diverse, “mindless” molecules through random atomic placement and subsequent geometry optimization. Using this framework, we ...

Europe's First Digital Identity Protection Law Tackles the Deepfake Crisis Denmark is poised to become the first European nation to grant citizens copyright control over their own faces, voices, and ...