Directional emission of white light via selective amplification of photon recycling and Bayesian optimization of multi-layer thin films

Over the last decades, light-emitting diodes (LED) have replaced common light bulbs in almost every application, from flashlights in smartphones to automotive headlights. Illuminating nightly streets requires LEDs to emit a light spectrum that is perceived as pure white by the human eye. The power associated with such a white light spectrum is not only distributed over the contributing wavelengths but also over the angles of vision. For many applications, the usable light rays are required to exit the LED in forward direction, namely under small angles to the perpendicular. In this work, we demonstrate that a specifically designed multi-layer thin film on top of a white LED increases the power of pure white light emitted in forward direction. Therefore, the deduced multi-objective optimization problem is reformulated via a real-valued physics-guided objective function that represents the hierarchical structure of our engineering problem. Variants of Bayesian optimization are employed to maximize this non-deterministic objective function based on ray tracing simulations. Eventually, the investigation of optical properties of suitable multi-layer thin films allowed to identify the mechanism behind the increased directionality of white light: angle and wavelength selective filtering causes the multi-layer thin film to play ping pong with rays of light

Recommendations for effective documentation in regional anesthesia: an expert panel Delphi consensus project

Background and objectives: Documentation is important for quality improvement, education, and research. There is currently a lack of recommendations regarding key aspects of documentation in regional anesthesia. The aim of this study was to establish recommendations for documentation in regional anesthesia. Methods: Following the formation of the executive committee and a directed literature review, a long list of potential documentation components was created. A modified Delphi process was then employed to achieve consensus amongst a group of international experts in regional anesthesia. This consisted of 2 rounds of anonymous electronic voting and a final virtual round table discussion with live polling on items not yet excluded or accepted from previous rounds. Progression or exclusion of potential components through the rounds was based on the achievement of strong consensus. Strong consensus was defined as ≥75% agreement and weak consensus as 50%-74% agreement. Results: Seventy-seven collaborators participated in both rounds 1 and 2, while 50 collaborators took part in round 3. In total, experts voted on 83 items and achieved a strong consensus on 51 items, weak consensus on 3 and rejected 29. Conclusion: By means of a modified Delphi process, we have established expert consensus on documentation in regional anesthesia

Nano-structured LEDs – Light extraction mechanisms and applications

Nano-structuring is a promising way to improve the efficiency of light emitting diodes (LEDs): two-dimensional photonic crystals can help to extract light from LEDs with the option of shaping the emission pattern, but can also increase the internal quantum efficiency in combination with surface plasmon polaritons. Both concepts are investigated theoretically in order to quantify for the first time their benefit in comparison to standard state-of-the-art LEDs. The impact of the different PhC parameters is investigated in depth along with the importance of the LED’s layer stack. Additionally, the value of PhC LEDs for the application in étendue-limited systems is determined

Theoretical Investigation of the Radiation Pattern From LEDs Incorporating Shallow Photonic Crystals

A theoretical approach based on coupled-mode theory is presented in order to determine the radiation pattern of LEDs incorporating a shallow photonic crystal. From this, a fundamental limit for the directionality of the diffraction of a single guided mode is given. Additionally, the Fabry-Perot resonances are shown to have significant impact on the directionality of diffracted light. For a realistic green-emitting InGaN LED in thin-film configuration the optimum reciprocal lattice vector is derived in terms of absolute diffracted intensity and directionality within a limited acceptance angle. The latter can be as high as 1.8 times the directionality of a Lambertian emitter. Furthermore, the spontaneous emission distribution between guided modes heavily influences the diffracted intensity

Strong High Order Diffraction of Guided Modes in Micro-Cavity Light-Emitting Diodes With Hexagonal Photonic Crystals

Photonic crystals (PhCs) have now been firmly established as an efficient means for light extraction from light emitting diodes (LEDs). We analyze the diffraction properties from thin GaN micro-cavity LEDs with hexagonal lattices that feature three guided TE modes only. In contrast to common design rules, we find that high order diffraction contributes significantly to the light extraction and increases the directionality of the emitted light. The implementation of the PhC leads to an enhancement in light extraction by a factor of up to 1.8 and the directionality of the light is greatly improved with a radiant intensity enhancement factor of 4.3, which can only be explained by the higher order diffraction that has been hitherto neglected. Furthermore, we show that higher order diffraction contributes significantly to the high azimuthal extraction uniformity we observe, suggesting that the use of quasi-crystal lattices is not necessary. We use a model including mode absorption where each in-plane angle of the guided modes is treated separately in order to explain the experimental results.</p

Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology

Abstract Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI‐based decision‐making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision‐making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision‐making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision‐making tools are data security, data representation, and explainability of AI‐based outcome predictions, in particular for decision‐making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts