Research focused on improving the sensitivity of SPR analytical technology has led to the investigation of different electric field enhancement elements for incorporation with classic sensor structures. Over the past ten years, considerable attention has been given to ceramic, metal oxide, and 2D materials that not only improve the magnitude of the field but are also used as affinity layers for improved adsorption of molecules. However, focusing on the improvements these materials deliver can lead designers to explore only some of themultiple dependencies associated with sensitivity. When using the angular interrogation mode, the impacts of working with different wavelengths are generally disregarded, as a fixed value of λ=633 nm is commonly adopted. Choosing 633 nm is often justified for achieving near-zero reflectivity and good sensitivity. However, newer SPR designs lack the determination of optimal wavelength-dependent sensitivity points. This article numerically investigates sensitivity as a function of wavelength for SPR sensors. We systematically study the effects of operating outside λ=633 nm and show that the signature produced by bare metal sensors does not always resemble the signatures produced by field-enhanced designs. Examinations are based on Graphene and BaTiO ₃ . Furthermore, we demonstrate how imposing thresholds on optimization targets can be leveraged to sustain high-sensitivity results, and we elaborate on the pertinent tradeoffs. Our approach shows further performance improvements in designs for which highly efficient benchmarks have already been demonstrated.

A numerical investigation of a sensor for acetone monitoring using surface plasmon resonance (SPR) is proposed. We evaluate the effects of polyaniline (PANI), graphene, or chitosan acting as a chemisorption binding layer for the selective sensing of acetone. Our findings suggest that these materials present suitable responses when applied as an overlayer to the sensor. Their thicknesses, however, should be maintained close to a monolayer level to preserve performance.

This work investigates the intrinsic sensitivity limits of surface plasmon resonance (SPR) sensors for detecting acetone concentrations relevant to breath-based diagnostics. Fresnel multilayer simulations are combined with experiments using a Kretschmann–Raether configuration with a PMMA prism and a 50-nm gold film. Sensor performance is evaluated through resonance shift, full width at half maximum (FWHM), sensitivity, and figure of merit (FOM). Simulations predict clear reflectivity variations and resonance redshift with increasing acetone concentration. Experiments with aqueous acetone solutions from 1.8 to 90 ppm confirm the simulated trends. Measurable SPR variations are observed down to approximately 9 ppm. Baseline noise analysis indicates a theoretical limit of detection near 1 ppm, while practical detection is limited by instrumental resolution. These results establish a benchmark for bulk refractive-index SPR sensing.

Modular arithmetic units for friendly moduli are crucial for residue number system (RNS) applications. Choosing a moduli set of the $$$ {2}^n-1 $$$ form is expected to provide maximum benefits for modular arithmetic performance. In this respect, this paper proposes a hardware architecture for the moduli set $$$ \left\{{2}^{n+1}-1,{2}^n-1,{2}^{n-1}-1\right\} $$$ (with $$$ n $$$ even), in particular an efficient architecture for the reverse conversion, which is often the bottleneck of RNS processors. The main idea is to remove the CRT multiplicative inverse from the reverse conversion stage, achieving scalable hardware with reduced complexity. We show that the modular multiplication of the multiplicative inverse can be transferred to the arithmetic channels with minimal overhead while reaching outstanding gains in the scalable reverse conversion stage. Furthermore, the presented method enables the utilization of recent hardware reutilization strategies to attain further improvements. Results show that the proposed reverse converter outperforms the state of the art by 17.48% in delay and 53.47% in area for $$$ n=20 $$$. Advantages become even more substantial for larger values of $$$ n $$$.

Ensuring security and processing speed are crucial attributes in today's computing applications. One of the most commonly used encryption systems is the Rivest–Shamir–Adleman (RSA), whose strength lies in the difficulty of breaking its keys. However, to continue guaranteeing adequate levels of protection, the size of these keys tends to grow over time, resulting in more complex encryption/decryption processes and longer operating times. This paper describes an RSA cryptographic accelerator soft‐core as an alternative to state‐of‐the‐art implementations solely based on efficient Montgomery modular executions. The proposed solution utilizes compression‐based modular multipliers to handle the complexity of the modular‐exponentiation operations. Furthermore, we further enhance performance by introducing a pseudomodulo strategy for processing the information in more efficient hardware (i.e., ) and then correcting the results back to the original modulo . The system was implemented using the MAX 10 10M50DAF484C7G FPGA and was integrated into a NEORV32 RISC‐V core. Results show that the new compression‐based multipliers, particularly with the use of pseudomoduli, provide significantly higher gains in terms of delay, delay logic elements, and execution time than approaches based on direct modulo multiplication and with the state of the art based on Montgomery multiplication.