A perspective of advances in optical methods for biological sample characterization

Message from Editors-in-Chief

Tandem solar cells are the next step in the photovoltaic (PV) evolution due to their higher power conversion efficiency (PCE) potential than currently dominating, but inherently limited, single‐junction solar cells. With the emergence of metal halide perovskite absorber materials, the fabrication of highly efficient tandem solar cells, at a reasonable cost, can significantly impact the future PV landscape. The perovskite‐based tandem solar cells have already shown that they can convert light more efficiently than their standalone sub‐cells. However, to reach PCEs over 30%, several challenges have to be overcome and the understanding of this fascinating technology has to be broadened. In this review, the main scientific and engineering challenges in the field are presented, alongside a discussion of the current status of three main perovskite tandem technologies: perovskite/silicon, perovskite/CIGS, and perovskite/perovskite tandem solar cells. A summary of the advanced structural, electrical, optical, radiative, and electronic characterization methods as well as simulations being utilized for perovskite‐based tandem solar cells is presented. The main findings are summarized and the strength of the techniques to overcome the challenges and gain deeper knowledge for further performance improvement is assessed. Finally, the PCE potential in different experimental and theoretical limits is compared with an aim to shed light on the path towards overcoming the 30% efficiency threshold for all of the three herein reviewed tandem technologies.

Both human brain and computer (electronic brain) could process data and do some cognition and computation tasks. Is the cognition of human brain equal to the computation of computer? It is obviously not. In this talk, the relationships of brain cognition models and intelligence computation models are summarized into four different types, that is, human brain cognition inspired intelligence computation (BCIIC), intelligence computation without human brain cognition (ICOBC), intelligence computation assisted human brain cognition (ICABC), and the integration of human brain cognition and intelligence computation (BC&IC). There are three paradigms in traditional artificial intelligence (AI) studies, that is, symbolism AI, connectionism AI, and behaviorism AI. The physical symbol system hypothesis is used in the symbolism AI. Human brain cognition is taken as a kind of symbolic processing, and the processes of human thinking are computed by symbol in the symbolism AI [1,2]. The connectionism AI relies on the bionics to simulate human brain. In the connectionism AI, neuron is taken as the basic unite of human thinking, and the intelligence is taken as the result of interconnected neurons competition and collaboration [3,4]. In the behaviorism AI, intelligence depends on the perception and behavior, “Perception-action” model is used, and intelligence may not require knowledge, knowledge representation and knowledge reasoning [5]. The symbolism AI and connectionism AI are two different types of human brain cognition inspired intelligence computation, while the behaviorism AI is a representative case of intelligence computation without human brain cognition. Usually, AI researchers get some inspiration from human brain cognition in their studies. On the other way, intelligence computation could also assist human brain cognition studies. The bidirectional cognitive computing model (BCC) is such a case. It studies the bidirectional transformations between the intension and extension of a concept. It is used to simulate some human brain cognition tasks such as learning and recognition [6,7]. Cognitive computing is one of the core fields of artificial intelligence [8,9]. Data-driven granular cognitive computing (DGCC) is an example of the integration of human brain cognition and intelligence computation [10,11]. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of ‘‘global precedence’’ which is a cognition process of ‘‘from coarser to finer’’ and the information processing mechanism of machine learning systems which is ‘‘from finer to coarser’’, in a multiple granularity space. It is data-driven cognitive computing model. The integration of human brain cognition and intelligence computation would be an important research topic of artificial intelligence. Some scientific research issues of the integration of human brain cognition and intelligence computation are discussed based on DGCC.

Light activated shape memory polymers and composites: A review

Advanced optical pulse characterization methods are essential for high-speed optical communications. Recently non-iterative characterization of general optical pulses using sinusoidal optical phase modulation has been reported [1–2]. However, the usable phase modulation amplitude with this method has a relatively small upper limit (0.88 radians [1]). In this paper we propose a novel non-iterative optical pulse characterization technique using sinusoidal optical phase modulation, which can work with modulation amplitude up to 1.9 radians. The larger usable modulation amplitude would make the proposed method more tolerable to noise present in the recorded pulse spectrum after undergoing phase modulation.

The present doctoral thesis established advanced optical read-out and characterisation methods for the in-depth analysis of chemical reactions, such as the quantification of reaction events or kinetic analysis, via state-of-the-art chemiluminescence systems. Critically, an optical read-out for the quantification of para-fluoro – thiol reaction events was established employing the chemiluminescence of Schaap’s dioxetane on the one hand, and peroxyoxalate chemiluminescence was employed for the qualitative assessment of single-chain nanoparticle unfolding on the other hand. Both chemiluminescence systems present promising tools for the in-depth characterisation of macromolecular architectures and their transformations.

Controlling composition, structure, and thus properties of nanomaterials continues to be of importance to numerous fields. For example, applications of quantum dots range from displays and bioimaging, to metal nanoparticles for (electro-) catalytic conversions. The vastness of synthesis parameter space, in combination with still limited understanding of the nucleation and growth mechanisms for many quantum dots systems still hampers progress in identifying nanomaterials with desired properties for existing and newly envisioned purposes. Furthermore, the identification of optimal synthesis recipes for these nanostructures remains a major hurdle.This presentation will report on the development and application a number of enabling capabilities for the synthesis and characterization of quantum dots, achieved through reactor engineering. We demonstrate how the use of automated flow reactors not only helps in run-to-run reproducibility but also in uncovering mechanistic information. Examples include reactors with dedicated nucleation, growth, and shell formation zones, and investigation that revealed mechanistic insight of how water concentration affects QD synthesis outcome. We also employed an autonomous flow reactor system to map synthesis parameter space of specific QD systems, a project that relied on fast, fully automated in-situ characterization using UV-vis or photo-luminescence in combination with multi-step machine learning workflows The utility of flow reactors, however, is limited when considering chemistries with longer reaction times. Hence, more recently we developed a fully automated batch reactor for parameter space mapping of QD synthesis via hot injection, the most frequently used method in both QD research and production at scale. This batch platform is being augmented with purification capabilities, to address some of the challenges of in-line, in-situ characterization of raw reaction mixtures, and to enable using advanced optical and structural characterization methods to provide insight into the actual structure of the QD materials.

The expansive research field of computational intelligence combines various nature-inspired computational methodologies and draws on rigorous quantitative approaches across computer science, mathematics, physics, and life sciences. Some of its research topics, such as artificial neural networks, fuzzy logic, evolutionary computation, and swarm intelligence, are traditional to computational intelligence. Other areas have established their relevance to the field fairly recently: embodied intelligence (Pfeifer and Bongard, 2006; Der, 2014), information theory of cognitive systems (Lungarella and Sporns, 2006; Polani et al., 2007; Ay et al., 2008), guided self-organization (Prokopenko, 2009; Der and Martius, 2012), and evolutionary game theory (Vincent and Brown, 2005). The intelligence phenomenon continues to fascinate scientists and engineers, remaining an elusive moving target. Following numerous past observations (e.g., Hofstadter, 1985, p. 585), it can be pointed out that several attempts to construct “artificial intelligence” have turned to designing programs with discriminative power. These programs would allow computers to discern between meaningful and meaningless in similar ways to how humans perform this task. Interestingly, as noted by de Looze (2006) among others, such discrimination is based on etymology of “intellect” derived from Latin “intellego” (inter-lego): to choose between, or to perceive/read (a core message) between (alternatives). In terms of computational intelligence, the ability to read between the lines, extracting some new essence, corresponds to mechanisms capable of generating computational novelty and choice, coupled with active perception, learning, prediction, and post-diction. When a robot demonstrates a stable control in presence of a priori unknown environmental perturbations, it exhibits intelligence. When a software agent generates and learns new behaviors in a self-organizing rather than a predefined way, it seems to be curiosity-driven. When an algorithm rapidly solves a hard computational problem, by efficiently exploring its search-space, it appears intelligent. In short, innovation and creativity shown within a rich space shaped by diverse, “entropic” forces, appeal to us as cognitive traits (Wissner-Gross and Freer, 2013). Can this intuition be formalized within rigorous and generic computational frameworks? What are the crucial obstacles on such a path? Intuitively, intelligent behavior is expected to be predictable and stable, but sensitive to change. Attempts to formalize this duality date back at least to cybernetics. For example, Ashby’s well-known Law of Requisite Variety states that an active controller requires as much variety (number of states) as that of the controlled system to be stable (Ashby, 1956). In order to explain the generation of behavior and learning in machines and living systems, Ashby also linked the concepts of ultrastability and homeostatic adaptation (Di Paolo, 2000; Fernandez et al., 2014). The balance between robustness and adaptivity is often attained near “the edge of chaos” (Langton, 1990), and the corresponding phase transitions are typically detected via high sensitivities to underlying control parameters (thermodynamic variables) (Prokopenko et al., 2011). Stability in self-organizing systems can be generally related to negentropy, the entropy that the system exports (dissipates) to keep its own entropy low (Schrodinger, 1944). Despite significant advances in this direction, the fundamental question whether stability, within processes developing far from an equilibrium, necessitates specific entropy dynamics is still unanswered. Clarifying the connections between entropy dynamics and stable but adaptive behavior is one of the grand challenges for computational intelligence. Put simply, we need to know whether learning and self-organization necessitate phase transitions in certain spaces, in terms of some order parameters. Is it possible to characterize the richness of self-generated choice, intrinsic to intelligent behavior, with respect to generic thermodynamic principles? The notion of generating and actively exploiting new behaviors, which adequately match the environment highlights that to be intelligent is to be complex in creating innovations. And so a mechanism producing computational novelty needs to exceed some threshold of complexity. To be truly impressive in generating endogenous innovation, it needs to be capable of universal computation, or to approach this capability in finite implementations (Casti, 1994; Markose, 2004). In other words, computational novelty may be fundamentally related to undecidability. Again, serious advances have been made in this foundational area of computer science. For example, Casti (1991) analyzed deeper interconnections between dynamical systems, Turing Machines, and formal logic systems: in particular, the complex, class IV, cellular automata were related to formal systems with undecidable statements (Godel’s incompleteness theorem) and the Halting Problem. Nevertheless, the question whether universal computation is the ultimate innovation-generator is still unresolved, offering another grand challenge: how computational intelligence, including mechanisms producing richness of choice and novelty, is related to

Marvels of Artificial and Computational Intelligence in Life Sciences is a primer for scholars and students who are interested in the applications of artificial intelligence (AI) and computational intelligence (CI) in life sciences and other industries. The book consists of 16 chapters (9 of which focus on AI and 7 of which showcase the benefits of CI approaches to solve specific problems). Chapters are edited by subject experts who describe the roles and applications of AI and CI in different parts of our lives in a concise and lucid manner. The book covers the following key themes: AI Revolution in Healthcare and Drug Discovery: AI's Impact on Biology and Energy Management AI and CI in Physical Sciences and Predictive Modeling Computational Biology The editors have compiled a good blend of topics in applied science and engineering to give readers a clear understanding of the multidisciplinary nature of the two facets of computing. Each chapter includes references for advanced readers.

Polarized and wide-field light microscopy has been studied for many years to develop accurate and information-rich images within a focused framework on biophysics and biomedicine. Technological advances and conceptual understanding have recently led to significant results in terms of applications. Simultaneously, developments in label-free methods are opening a new window on molecular imaging at a low dose of illumination. The ability to encode and decode polarized light pixel by pixel, coupled with the computational strength provided by artificial intelligence, is the running perspective of label-free optical microscopy. More specifically, the information-rich content Mueller matrix microscopy through its 16 elements offers multimodal imaging, an original data set to be integrated with other advanced optical methods. This dilates the spectrum of possible and potential applications. Here, we explore the recent advances in basic and applied research towards technological applications tailored for specific questions in biophysics and biomedicine.

Advanced optical methods and strategies for extracellular vesicles characterization and dynamic studies.

Abstract

Colloidal model systems allow studying crystallization kinetics under fairly ideal conditions, with rather well-characterized pair interactions and minimized external influences. In complementary approaches experiment, analytic theory and simulation have been employed to study colloidal solidification in great detail. These studies were based on advanced optical methods, careful system characterization and sophisticated numerical methods. Over the last decade, both the effects of the type, strength and range of the pair-interaction between the colloidal particles and those of the colloid-specific polydispersity have been addressed in a quantitative way. Key parameters of crystallization have been derived and compared to those of metal systems. These systematic investigations significantly contributed to an enhanced understanding of the crystallization processes in general. Further, new fundamental questions have arisen and (partially) been solved over the last decade: including, for example, a two-step nucleation mechanism in homogeneous nucleation, choice of the crystallization pathway, or the subtle interplay of boundary conditions in heterogeneous nucleation. On the other hand, via the application of both gradients and external fields the competition between different nucleation and growth modes can be controlled and the resulting microstructure be influenced. The present review attempts to cover the interesting developments that have occurred since the turn of the millennium and to identify important novel trends, with particular focus on experimental aspects.

SummaryThe recent sale of an artificial intelligence (AI)-generated portrait for $432,000 at Christie's art auction has raised questions about how credit and responsibility should be allocated to individuals involved and how the anthropomorphic perception of the AI system contributed to the artwork's success. Here, we identify natural heterogeneity in the extent to which different people perceive AI as anthropomorphic. We find that differences in the perception of AI anthropomorphicity are associated with different allocations of responsibility to the AI system and credit to different stakeholders involved in art production. We then show that perceptions of AI anthropomorphicity can be manipulated by changing the language used to talk about AI—as a tool versus agent—with consequences for artists and AI practitioners. Our findings shed light on what is at stake when we anthropomorphize AI systems and offer an empirical lens to reason about how to allocate credit and responsibility to human stakeholders.

Studies in the field of medicine have started to apply Artificial Intelligence's (AI) and Intelligent Computing Technique skills for processing and analyzing data to telemedicine, as the technology's use in other disciplines and businesses has grown in popularity. As healthcare professionals work to increase virtual care options along the continuum, they must leverage artificial intelligence (AI) and Intelligent Computing Techniques in telehealth to enable clinicians to make data-rich, real-time decisions that will enhance patient outcomes. Given the broad use of AI in other industries, research in the medical field has begun to leverage AI's advantages in data processing and analysis in telehealth. The convergence of Artificial Intelligence (AI) and intelligent computing techniques has significantly transformed the landscape of telemedicine and healthcare. This chapter aims to explore the applications, benefits, challenges, and future prospects of employing AI and intelligent computing in telemedicine and healthcare. The integration of these technologies has paved the way for more efficient diagnosis, treatment, remote patient monitoring, and personalized healthcare, revolutionizing the industry's approach to patient care. The chapter provides an in-depth analysis of the various AI-driven applications and their impacts on healthcare delivery, while also addressing the ethical and privacy concerns associated with these advancements.