Skip to main content
Home
  • Home
  • Professor Fernandez
  • Research
  • Publications
  • Supplementary Material
  • People
  • Media
  • Instrumentation
  • Teaching

Ambient Sampling/Ionization & Imaging MS.

Desorption Electrospray Ionization (DESI) ; Direct Analysis in Real Time (DART) Ionization ; Hybrid Techniques (DEMI, LA-MICI, etc.)


Our group is developing new types of ambient plasma, laser, and spray-based ion generation technologies that can be used for a number of applications, including those in the field and in space, or as new detectors for traditional chemical separation approaches, such as LC. We focus a large amount of our efforts on understanding the ionization mechanisms prevailing in ambient ion generation, and the fluid dynamic processes affecting ion transmission. We are also developing new instrumentation for applying ambient ionization approaches in microprobe MS imaging (MSI) modes. Ambient MSI applications in our lab include studies in traumatic brain injuries, imaging of surface chemical defenses on marine algae, and imaging of organics on minerals. All these projects are pursued in collaboration with researchers at GT (BME, Center for Chemical Evolution, Biology) or elsewhere.
More info here

Ion Mobility Spectrometry

New drift tube designs. New ambient ion mobility ion sources. Multiplexing.


Ion Mobility Spectrometry (IMS) is a gas phase structural and chemical separation technique based on differences in ion-neutral collisional cross sections. It has become widely accepted for the detection of chemical warfare agents, explosives and narcotics as well as for pharmaceutical quality control and pesticide screening. It can also be combined with MS (IM-MS) for obtaining conformational information of ions of interest, or as an enhancement in overall method peak capacity. With industrial sponsorship, our group is developing high resolution IM instrumentation based on drift-tube (DTIMS) approaches for pharmaceutical applications, and developing new interfaces for ambient MS ion sources to be coupled to IMS and IM-MS..
More info here

Metabolomics

Ambient MS metabolomics. UPLC-MS and MS/MS. Cancer. Cystic Fibrosis. Plankton. Imaging MS.


Medicine in the 21st century is shifting from a reactive to a proactive discipline to provide personalized, predictive, preventive, and participatory (P4) care, not just treating disease, but also maximizing wellness. P4 medicine is propelled by data-driven systems approaches to understand disease, and emerging analytical technologies and tools. It aims at integrating billions of genomic, transcriptomic, proteomic and metabolomics “data points” for each patient to develop predictive multivariate models that can be used to guide personalized patient treatment. This is especially true for complex conditions such as ovarian cancer (OC) which, under the classical classification scheme, may actually encompass several different disease subtypes (endometrioid, serous papillary, mucinous, clear cell, etc.). Hence the proper sub-classification of the disease could be critical to developing a personalized -and perhaps different- treatment strategy for each individual patient.
More info here

Pharmaceutical Forensics

New technologies for detection of counterfeit drugs. Antimalarials and others.


As with disease pandemics, the globalization of the pharmaceutical trade has the potential to rapidly spread poor-quality medicines worldwide before adequate detection and intervention are possible. There are three main categories of poor quality medicines; degraded, substandard and falsified (counterfeit). Substandard products arise as a result of lack of expertise, poor manufacturing practices, or insufficient infrastructure whilst those falsified are the ‘products’ of criminals. Degraded medicines arise from poor storage conditions. Falsified drugs may not contain the active ingredient, may contain the wrong ingredients or may even contain toxic compounds. Substandard drugs, for example, may contain active pharmaceutical ingredient (API) amounts that are in excess of ±15% of the stated amount. Distinguishing between these three classes requires simultaneously identifying and quantifying the expected (or wrong) APIs.
More info here