Modulating the pH dependent photophysical properties of green fluorescent protein.

The photophysical properties of the β-barrel superfolder green fluorescent protein (sfGFP) arise from the chromophore that forms post-translationally in the interior of the protein. Specifically, the protonation state of the side chain of tyrosine 66 in the chromophore, in addition to the network of hydrogen bonds between the chromophore and surrounding residues, is directly related to the electronic absorbance and emission properties of the protein. The pH dependence of the photophysical properties of this protein were modulated by the genetic, site-specific incorporation of 3-nitro-l-tyrosine (mNOY) at site 66 in sfGFP.

Reduced-order model to approximate response matrices for filter stack spectrometers.

We present a reduced-order model to calculate response matrices rapidly for filter stack spectrometers (FSSs). The reduced-order model allows response matrices to be built modularly from a set of pre-computed photon and electron transport and scattering calculations through various filter and detector materials. While these modular response matrices are not appropriate for high-fidelity analysis of experimental data, they encode sufficient physics to be used as a forward model in design optimization studies of FSSs, particularly for machine learning approaches that require sampling and testing a large number of FSS designs.

Machine learning based unfolding of x-ray spectra from filter stack spectrometer data.

We demonstrate the application of neural networks to perform x-ray spectra unfolding from data collected by filter stack spectrometers. A filter stack spectrometer consists of a series of filter-detector pairs, where the detectors behind each filter measure the energy deposition through each layer as photo-stimulated luminescence (PSL). The network is trained on synthetic data, assuming x-rays of energies <1 MeV and of two different distribution functions (Maxwellian and Gaussian) and the corresponding measured PSL values obtained from five different filter stack spectrometer designs.

A modular, high dynamic range passive neutron dosimeter and imaging diagnostic.

The multi-decade neutron dosimeter and imaging diagnostic (MDND) is a passive diagnostic that utilizes the polyethylene (n, p) nuclear reaction to enhance the diagnostic's sensitivity for time and energy integrated neutron measurements in the range of 2.45-14.1 MeV.

Robust unfolding of MeV x-ray spectra from filter stack spectrometer data.

We present an inversion method capable of robustly unfolding MeV x-ray spectra from filter stack spectrometer (FSS) data without requiring an a priori specification of a spectral shape or arbitrary termination of the algorithm. Our inversion method is based upon the perturbative minimization (PM) algorithm, which has previously been shown to be capable of unfolding x-ray transmission data, albeit for a limited regime in which the x-ray mass attenuation coefficient of the filter material increases monotonically with x-ray energy. Our inversion method improves upon the PM algorithm through regular smoothing of the candidate spectrum and by adding stochasticity to the search.