Understanding how the human body reacts in a range of conditions is a continuing focus of our laboratory programs, from radiation effects to overall health, injury, disease, and mitigation of biological defects. Restoring sight to the blind, crafting on-the-fly medicines in epidemics, and cultivating living organisms on a 3D-printed base are among the fantastic sci-fi advances now in progress.
LLNL pursues electrode material and electrochemical development and characterization.
LLNL excels at combining computing power with predictive modelling to provide insight and understanding of disease progression and prevention.
First-of-their-kind in vitro systems recapitulate the cellular composition & spatial organization human tissues, providing unprecedented insight into the mechanistic workings of the human body.
LLNL is engaged in design, fabrication, characterization, packaging and integration of biological micro- and nanosystems.
Some of our current projects we are working on include:
Electrochemical Roughening for Biological Sensors
LLNL engineers and scientists have improved the performance of Lab-developed flexible thin-film biological sensors by increasing the sensitivity of the implantable arrays to chemicals for biosensing applications, among other performance enhancements.
Machine Learning for Sepsis Diagnosis
Researchers and clinicians may track the progression of sepsis, an extreme reaction to infection, with more precision and confidence using machine-learning models developed at LLNL in conjunction with Kaiser Permanente.
LLNL scientists and engineers have integrated mico-optics on flexible materials using advanced materials and micro-/nano-patterning techniques. The technology can be widely applied to design flexible optoelectronic bio-interfaces such as implantables and wearables.
Brain on a Chip
A new study in which Lawrence Livermore National Laboratory scientists compared drug responses in the brains of rodents to drug responses of brain cells cultured in Lab-developed "brain-on-a-chip" devices may be a critical first step to validating chip-based brain platforms.
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