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The characterization of chemical microheterogeneity is compelling due to its relevant role in soft materials, high-entropy materials, and systems chemistry, to cite just a few instances. This work investigates the microheterogeneity of photochromic samples and metal oxide solid solutions by fitting time-resolved absorption and luminesce signals recorded after photoexcitation. The transient spectroscopic signals have been analyzed using polyexponential functions determined through the Maximum Entropy Method (MEM) and discrete exponential, Kohlrausch, and Becquerel functions through the Levenberg-Marquardt algorithm. The outputs of the different fitting functions and algorithms are compared and exploited to characterize chemical microheterogeneity quantitatively. The practical relevance of chemical microheterogeneity is supported by the demonstration that photochromic samples are transformed from binary to multistate systems, capable of encoding much more information, and that microheterogeneous photocatalysts are provided with several structural defects that guarantee the coexistence of many active sites and higher catalytic activity.

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Artificial Intelligence (AI) is having a revolutionary impact on our societies. It is helping humans in facing the global challenges of this century. Traditionally, AI is developed in software or through neuromorphic engineering in hardware. More recently, a brand-new strategy has been proposed. It is the so-called Chemical AI (CAI), which exploits molecular, supramolecular, and systems chemistry in wetware to mimic human intelligence. In this work, two promising approaches for boosting CAI are described. One regards designing and implementing neural surrogates that can communicate through optical or chemical signals and give rise to networks for computational purposes and to develop micro/nanorobotics. The other approach concerns "bottom-up synthetic cells" that can be exploited for applications in various scenarios, including future nano-medicine. Both topics are presented at a basic level, mainly to inform the broader audience of non-specialists, and so favour the rise of interest in these frontier subjects.

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This work highlights the relevant contribution of conformational stereoisomers to the complexity and functions of any molecular compound. Conformers have the same molecular and structural formulas but different orientations of the atoms in the three-dimensional space. Moving from one conformer to another is possible without breaking covalent bonds. The interconversion is usually feasible through the thermal energy available in ordinary conditions. The behavior of most biopolymers, such as enzymes, antibodies, RNA, and DNA, is understandable if we consider that each exists as an ensemble of conformers. Each conformational collection confers multi-functionality and adaptability to the single biopolymers. The conformational distribution of any biopolymer has the features of a fuzzy set. Hence, every compound that exists as an ensemble of conformers allows the molecular implementation of a fuzzy set. Since proteins, DNA, and RNA work as fuzzy sets, it is fair to say that life's logic is fuzzy. The power of processing fuzzy logic makes living beings capable of swift decisions in environments dominated by uncertainty and vagueness. These performances can be implemented in chemical robots, which are confined molecular assemblies mimicking unicellular organisms: they are supposed to help humans "colonise" the molecular world to defeat diseases in living beings and fight pollution in the environment.

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Some features of the human nervous system can be mimicked not only through software or hardware but also through liquid solutions of chemical systems maintained under out-of-equilibrium conditions. We describe the possibility of exploiting a thin layer of the Belousov-Zhabotinsky (BZ) reaction as a surrogate for the cochlea for sensing acoustic frequencies. Experiments and simulations demonstrate that, as in the human ear where the cochlea transduces the mechanical energy of the acoustic frequencies into the electrochemical energy of neural action potentials and the basilar membrane originates topographic representations of sounds, our bioinspired chemoacoustic system, based on the BZ reaction, gives rise to spatiotemporal patterns as the representation of distinct acoustic bands through transduction of mechanical energy into chemical energy. Acoustic frequencies in the range 10-2000 Hz are partitioned into seven distinct bands based on three attributes of the emerging spatiotemporal patterns: (1) the types and frequencies of the chemical waves, (2) their velocities, and (3) the Faraday waves' wavelengths.

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Inspired by some traits of human intelligence, it is proposed that wetware approaches based on molecular, supramolecular, and systems chemistry can provide valuable models and tools for novel forms of robotics and AI, being constituted by soft matter and fluid states as the human nervous system and, more generally, life, is. Bottom-up mimicries of intelligence range from the molecular world to the multicellular level, i.e., from the Ångström (10-10 meters) to the micrometer scales (10-6 meters), and allows the development of unconventional chemical robotics. Whereas conventional robotics lets humans explore and colonise otherwise inaccessible environments, such as the deep oceanic abysses and other solar system planets, chemical robots will permit us to inspect and control the microscopic molecular and cellular worlds. This article suggests that systems made of properly chosen molecular compounds can implement all those modules that are the fundamental ingredients of every living being: sensory, processing, actuating, and metabolic networks. Autonomous chemical robotics will be within reach when such modules are compartmentalised and assembled. The design of a strongly intertwined web of chemical robots, with or without the involvement of living matter, will give rise to collective forms of intelligence that will probably reproduce, on a minimal scale, some sophisticated performances of the human intellect and will implement forms of "general AI." These remarkable achievements will require a productive interdisciplinary collaboration among chemists, biotechnologists, computer scientists, engineers, physicists, neuroscientists, cognitive scientists, and philosophers to be achieved. The principal purpose of this paper is to spark this revolutionary collaborative scientific endeavour.

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Chemical Artificial Intelligence (CAI) is a brand-new research line that exploits molecular, supramolecular, and systems chemistry in wetware (i.e., in fluid solutions) to imitate some performances of human intelligence and promote unconventional robotics based on molecular assemblies, which act in the microscopic world, otherwise tough to be accessed by humans. It is undoubtedly worth spreading the news that AI researchers can rely on the help of chemists and biotechnologists to reach the ambitious goals of building intelligent systems from scratch. This article reports the first attempt at building a Chemical Artificial Intelligence knowledge map and describes the basic intelligent functions that can be implemented through molecular and supramolecular chemistry. Chemical Artificial Intelligence provides new tools and concepts to mimic human intelligence because it shares, with biological intelligence, the same principles and materials. It enables peculiar dynamics, possibly not accessible in software and hardware domains. Moreover, the development of Chemical Artificial Intelligence will contribute to a deeper understanding of the strict link between intelligence and life, which are two of the most remarkable emergent properties shown by the Complex Systems we call biological organisms.

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The potential role of bottom-up Synthetic Cells (SCs) in the Internet of Bio-Nano Things (IoBNT) is discussed. In particular, this perspective paper focuses on the growing interest in networks of biological and/or artificial objects at the micro- and nanoscale (cells and subcellular parts, microelectrodes, microvessels, etc.), whereby communication takes place in an unconventional manner, i.e., via chemical signaling. The resulting "molecular communication" (MC) scenario paves the way to the development of innovative technologies that have the potential to impact biotechnology, nanomedicine, and related fields. The scenario that relies on the interconnection of natural and artificial entities is briefly introduced, highlighting how Synthetic Biology (SB) plays a central role. SB allows the construction of various types of SCs that can be designed, tailored, and programmed according to specific predefined requirements. In particular, "bottom-up" SCs are briefly described by commenting on the principles of their design and fabrication and their features (in particular, the capacity to exchange chemicals with other SCs or with natural biological cells). Although bottom-up SCs still have low complexity and thus basic functionalities, here, we introduce their potential role in the IoBNT. This perspective paper aims to stimulate interest in and discussion on the presented topics. The article also includes commentaries on MC, semantic information, minimal cognition, wetware neuromorphic engineering, and chemical social robotics, with the specific potential they can bring to the IoBNT.

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Photochromic compounds are employed in implementing neuron surrogates. They will boost the development of neuromorphic engineering in wetware. In this work, the photochromic behaviours of (E)-3,4,6-trichloro-2-(p-diazenil)-phenol (t-DZH) and its conjugated phenoxide base (t-DZ) have been investigated experimentally in three different media: (1) pure acetonitrile, (2) in water and acetonitrile mixed in a 1/1 volume ratio, and (3) in an aqueous micellar solution of 3-(N,N-Dimethylmyristylammonio)propanesulfonate (SB3-14). The analysis of the spectral and kinetic features of t-DZH and t-DZ has been supported by quantum-mechanical DFT calculations, the maximum entropy method, and the determination of their colourability (C). The versatility of t-DZH and t-DZ makes them promising molecular probes of micro-environments and potential ingredients of photochemical oscillators required for implementing pacemaker neurons capable of communicating through optical signals in wetware.

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Gluten related-disorders have a prevalence of 1-5 % worldwide triggered by the ingestion of gluten proteins in wheat, rye, barley, and some oats. In wheat gluten, the most studied protein is gliadin, whose immunodominant 33-mer amino acid fragment remains after digestive proteolysis and accumulates in the gut mucosa. Here, we report the formation of 33-mer thin-plate superstructures using intrinsic tyrosine (Tyr) steady-state fluorescence anisotropy and cryo-TEM in combination with water tension measurements. Furthermore, we showed that fluorescence decay measurements of 33-mer intrinsic fluorophore Tyr provided information on the early stages of the formation of the thin-plate structures. Finally, conformational analysis of Tyr residues using minimalist models by molecular dynamic simulations (MD) demonstrated that changes in Tyr rotamer states depend on the oligomerization stage. Our findings further advance the understanding of the formation of the 33-mer gliadin peptide superstructures and their relation to health and disease.

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Chemical micro-heterogeneity is an attribute of all living systems and most of the soft and crystalline materials. Its characterization requires a plethora of techniques. This work proposes a strategy for quantifying the degree of chemical micro-heterogeneity. First of all, our approach needs the collection of time-evolving signals that can be fitted through poly-exponential functions. The best fit is determined through the Maximum Entropy Method. The pre-exponential terms of the poly-exponential fitting function are used to estimate Fuzzy Entropy. Related to the possibility of implementing Fuzzy sets through the micro-heterogeneity of chemical systems. Fuzzy Entropy becomes a quantitative estimation of the Fuzzy Information that can be processed through micro-heterogeneous chemical systems. We conclude that our definition of Fuzzy Entropy can be extended to other kinds of data, such as morphological and structural distributions, spectroscopic bands and chromatographic peaks. The chemical implementation of Fuzzy sets and Fuzzy logic will promote the development of Chemical Artificial Intelligence.

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Human interaction with the world is dominated by uncertainty. Probability theory is a valuable tool to face such uncertainty. According to the Bayesian definition, probabilities are personal beliefs. Experimental evidence supports the notion that human behavior is highly consistent with Bayesian probabilistic inference in both the sensory and motor and cognitive domain. All the higher-level psychophysical functions of our brain are believed to take the activities of interconnected and distributed networks of neurons in the neocortex as their physiological substrate. Neurons in the neocortex are organized in cortical columns that behave as fuzzy sets. Fuzzy sets theory has embraced uncertainty modeling when membership functions have been reinterpreted as possibility distributions. The terms of Bayes' formula are conceivable as fuzzy sets and Bayes' inference becomes a fuzzy inference. According to the QBism, quantum probabilities are also Bayesian. They are logical constructs rather than physical realities. It derives that the Born rule is nothing but a kind of Quantum Law of Total Probability. Wavefunctions and measurement operators are viewed epistemically. Both of them are similar to fuzzy sets. The new link that is established between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability could spark new ideas for the development of artificial intelligence and unconventional computing.

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The goals and targets included in the 2030 Agenda compiled by the United Nations want to stimulate action in areas of critical importance for humanity and the Earth. These goals and targets regard everyone on Earth from both the health and economic and social perspectives. Reaching these goals means to deal with Complex Systems. Therefore, Complexity Science is undoubtedly valuable. However, it needs to extend its scope and focus on some specific objectives. This article proposes a development of Complexity Science that will bring benefits for achieving the United Nations' aims. It presents a list of the features shared by all the Complex Systems involved in the 2030 Agenda. It shows the reasons why there are certain limitations in the prediction of Complex Systems' behaviors. It highlights that such limitations raise ethical issues whenever new technologies interfere with the dynamics of Complex Systems, such as human beings and the environment. Finally, new methodological approaches and promising research lines to face Complexity Challenges included in the 2030 Agenda are put forward.

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Materials based on ordered protein aggregates have recently received a lot of attention for their application as drug carriers, due to their biocompatibility and their ability to sequester many biological fluids. Bovine serum albumin (BSA) is a good candidate for this use due to its high availability and tendency to aggregate and gel under acidic conditions. In the present work, we employ spectroscopic techniques to investigate the heat-induced BSA aggregation at the molecular scale, in the 12-84 °C temperature range, at pH = 5 where two different isoforms of the protein are stable. Samples at low and high protein concentration are examined. With the advantage of the combined use of FTIR and CD, we recognize the aggregation-prone species and the different distribution of secondary structures, conformational rearrangements and types of aggregates, of millimolar compared to micromolar BSA solutions. Further, as a new tool, we use the Maximum Entropy Method to fit the kinetic curves to investigate the distribution of kinetic constants of the complex hierarchical aggregation process. Finally, we characterize the activation energy of the initial self-assembling step to observe that the formation of both small and large aggregates is driven by the same interactions.

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The global challenges of the XXI century require a more in-depth analysis and investigation of complex systems [...].

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The comprehension and control of the interactions between nanoparticles and proteins at a molecular level are crucial to improve biomedical applications of nanomaterials and to develop nanosystems able to influence and regulate the conformational changes in proteins. In this work, we explore the interactions between Gramicidin A peptide (GramA) and dodecanethiol-stabilized small silver nanoparticles (D-AgNPs), paying particular attention to the effect on GramA conformation in POPC bilayers. D-AgNPs have been prepared to have dimensions (5 nm) and a hydrophobic nature compatible with the POPC lipid bilayer. Fluorescence, Raman and IR spectroscopies have been used to investigate both peptide conformation and its position inside the phospholipid bilayer. Results are discussed in terms of solvent exposure and conformation of GramA peptide.

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Scientists want to comprehend and control complex systems. Their success depends on the ability to face also the challenges of the corresponding computational complexity. A promising research line is artificial intelligence (AI). In AI, fuzzy logic plays a significant role because it is a suitable model of the human capability to compute with words, which is relevant when we make decisions in complex situations. The concept of fuzzy set pervades the natural information systems (NISs), such as living cells, the immune and the nervous systems. This paper describes the fuzziness of the NISs, in particular of the human nervous system. Moreover, it traces three pathways to process fuzzy logic by molecules and their assemblies. The fuzziness of the molecular world is useful for the development of the chemical artificial intelligence (CAI). CAI will help to face the challenges that regard both the natural and the computational complexity.

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Two chloromethyl phenylcarbamate-based chiral stationary phases, one containing an amylose-type chiral selector (Lux Amylose 2, from Phenomenex) and the other a cellulose-type one (Lux Cellulose-4, from Phenomenex), were successfully used for the chiral resolution of three helical chromenes featuring a helicene-like structure. The compound bearing a phenyl substituent on the helicene-like structure was enantioresolved at 25°C with Lux Cellulose-4 and a n-hexane/1-propanol 99:1 v/v eluent. With a n-hexane/2-propanol 99.8:0.2 v/v mobile phase, the same column (operated at 35°C) provided the separation of the four isomers of the compound having a hexyl residue on the helicene-like motif and an additional asymmetric carbon. Lux Amylose-2 was necessary for the enantioseparation of the compound having the sole hexyl residue on the helical scaffold. For the last compound a n-hexane/2-propanol 99.8:0.2 v/v eluent was used, and the column temperature was fixed at 5°C. The enantiomer elution order was appraised by using electronic circular dichroism and theoretical calculations. Notably, different thermodynamics of retention and enantioseparation were observed for molecules with pronounced structural similarity, that is, the enantiomer pairs of the compound containing the additional asymmetric carbon atom. Indeed, both entropically and enthalpically controlled adsorption and separation processes were observed.