Functionalized iron oxide nanoparticles (IONPs) are of great interest due to wide range applications, especially in nanomedicine. However, they face challenges preventing their further applications such as rapid agglomeration, oxidation, etc. Appropriate surface modification of IONPs can conquer these barriers with improved physicochemical properties. This review summarizes recent advances in the surface modification of IONPs with small organic molecules, polymers and inorganic materials. The preparation methods, mechanisms and applications of surface-modified IONPs with different materials are discussed. Finally, the technical barriers of IONPs and their limitations in practical applications are pointed out, and the development trends and prospects are discussed.

To guide the design of plasmonic solar cells, theoretical investigation of core (metal)-shell (dielectric) nanoparticles for light absorption enhancement in thin film Si solar cells is performed. In contrast to the reported simulations and experimental results that rear-located surface plasmon on bare metallic nanoparticles is preferred, the core-shell nanoparticles demonstrate better performance when surface plasmon is located in front of a solar cell. This has been attributed to the enhanced forward scattering with vanishing backward scattering preserved over a wide spectral range in core-shell nanoparticles. This work provides a concept to achieve enhanced forward scattering with weakened backward scattering in plasmonic thin film solar cells.

The field of chiral plasmonics has registered considerable progress with machine-learning (ML)-mediated metamaterial prototyping, drawing from the success of ML frameworks in other applications such as pattern and image recognition. Here, we present an end-to-end functional bidirectional deep-learning (DL) model for three-dimensional chiral metamaterial design and optimization. This ML model utilizes multitask joint learning features to recognize, generalize, and explore in detail the nontrivial relationship between the metamaterials’ geometry and their chiroptical response, eliminating the need for auxiliary networks or equivalent approaches to stabilize the physically relevant output. Our model efficiently realizes both forward and inverse retrieval tasks with great precision, offering a promising tool for iterative computational design tasks in complex physical systems. Finally, we explore the behavior of a sample ML-optimized structure in a practical application, assisting the sensing of biomolecular enantiomers. Other potential applications of our metastructure include photodetectors, polarization-resolved imaging, and circular dichroism (CD) spectroscopy, with our ML framework being applicable to a wider range of physical problems.

Nonradiative decay of surface plasmons (SPs) is usually considered an unwanted process. However, recent studies have proven that hot carriers generated from nonradiative SP decay can be used for photodetection that circumvents the bandgap limitation in semiconductors. The major problem plaguing the plasmonic hot carrier photodetectors stems from the low quantum efficiency. In this review, we discuss recent progress of engineering plasmonic hot carrier dynamics and describe a host of plasmon-enhanced photodetectors, including optical antenna-based photodetectors, planar photodetectors, photodetectors coupled with 2D materials, functionalized photodetectors, photodetectors for integrated nanophotonics, and hot-hole photodetectors. Finally, we herein highlight some new directions in the plasmonic photodetection.