Enhancing Open-World Bacterial Raman Spectra Identification by Feature Regularization for Improved Resilience against Unknown Classes.

The combination of deep learning techniques and Raman spectroscopy shows great potential offering precise and prompt identification of pathogenic bacteria in clinical settings. However, the traditional closed-set classification approaches assume that all test samples belong to one of the known pathogens, and their applicability is limited since the clinical environment is inherently unpredictable and dynamic, unknown, or emerging pathogens may not be included in the available catalogs. We demonstrate that the current state-of-the-art neural networks identifying pathogens through Raman spectra are vulnerable to unknown inputs, resulting in an uncontrollable false positive rate.

Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach.

Raman spectroscopy, combined with machine learning techniques, holds great promise for many applications as a rapid, sensitive, and label-free identification method. Such approaches perform well when classifying spectra of chemical species that were encountered during the training phase. That is, species that are known to the neural network.

Optimization of laser deposited silver nanoparticle substrates for surface-enhanced raman spectroscopy.

Creating sensitive and reproducible substrates for surface-enhanced Raman spectroscopy (SERS) has been a challenge in recent years. While SERS offers significant benefits over traditional Raman spectroscopy, certain hindrances have limited their commercial use, especially in settings where low limits of detection are necessary. We studied a variety of laser-deposited silver microstructured SERS substrates with different morphology as a means to optimize analyte detection.

Absorption cross section of gold nanoparticles based on NIR laser heating and thermodynamic calculations.

We present a method for measuring the optical absorption cross section ([Formula: see text]) of gold nanoparticles (GNPs) based on optically heating the solution of GNPs with an 808 nm near-infrared (NIR) laser and measuring the temperature increase of the solution. We rely on the theoretical calculations based on the heat diffusion equations and experimental measurements based on the energy balance equations to measure the [Formula: see text] and the temperature distribution of single GNPs. Several morphologies, including gold nanospheres (GNSs), spherical gold nanoparticle conjugate (AuNPC), which are 20 nm GNSs surface-functionalized with an IR 808 dye, gold nanorods (GNRs), and gold nanourchins (GNUs), were studied.

Understanding cellular internalization pathways of silicon nanowires.

Background: Understanding how cells interact with nanomaterials is important for rational design of nanomaterials for nanomedicine and transforming them for clinical applications. Particularly, the mechanism for one-dimensional (1D) nanomaterials with high aspect ratios still remains unclear.

Results: In this work, we present amine-functionalized silicon nanowires (SiNW-NH) entering CHO-β cells via a physical membrane wrapping mechanism.

Parallel Nanoshaping of Brittle Semiconductor Nanowires for Strained Electronics.

Semiconductor nanowires (SCNWs) provide a unique tunability of electro-optical property than their bulk counterparts (e.g., polycrystalline thin films) due to size effects.

Use of graphene as protection film in biological environments.

Corrosion of metal in biomedical devices could cause serious health problems to patients. Currently ceramics coating materials used in metal implants can reduce corrosion to some extent with limitations. Here we proposed graphene as a biocompatible protective film for metal potentially for biomedical application.