Peculiarities of modern technology

PECULIARITIES OF MODERN TECHNOLOGY.
Modern technology exploits and controls organic materials with a precision that was inconceivable only few decades ago.[1] This is a consequence of the fact that the performance and property of a material can be related to the structure and behaviour of the constituting molecules. For instance, considering the topic of the present book, the photostability of polymers, sunscreens, drugs, paints, etc. depends on the ability to waste, at the molecular level, the absorbed photon energy either via non-radiative (e.g. internal conversion) or radiative (e.g. fosforescence) channels. In general, the more control we have on molecular properties, the more a material will fit our needs and, for this reason, in the past chemists have learned to synthesize, for instance, photostable and luminescent molecules.
A novel research target in the area of the control of molecular properties is represented by the design of molecular machines:molecules that react to a certain external signal by displacing, usually reversibly, one or more of their parts. Modern chemistry and technology are rapidly moving along this way: molecular devices and machines are, nowadays, under investigation, giving rise to the so-called molecular technology, or else termed nanotechnology.[1] Between other molecular devices, those based on reversible photochemical reactions have a great interest. These devices are operated irradiating the molecule at the wavelength required to trigger the photochemical process. In principle, the design of the right reagent, allows for a direct control of the reaction rate, efficiency and photostability even when the interconversion between the two (or more) “states” of the device need to be repeated over a large number of cycles.[2–5] It is apparent that the elucidation of the factors controlling photochemical reactions is imperative for the rational design of such a material. In particular, it is apparent that, in order to achieve this goal, it is mandatory to elucidate the details of the photochemical reaction mechanism. Among others, this requirement provides a timely and solid motivation for the topic developed in the present chapter.
In recent years computational chemistry has gained increasing consideration as a valid tool for the detailed investigation of photochemical reaction mechanisms. Below we will outline the strategy and operational approach to the practical computational investigation of reaction mechanisms in organic photochemistry. The aim is to show how this task can be achieved through high-level ab initio quantum chemical computations and ad hoc optimization tools, using either real or model (i.e. simplified) systems. Another purpose of the present Chapter is to show that, nowadays, a computational chemist can adapt his/her “instruments” (the method, the approach and the level of accuracy) to the problem under investigation, as every other scientist does when there is a problem to study and a methodology to be chosen. In particular, different and often complementary computational tools may be used as “virtual spectrometers” to characterize the molecular reactivity of a given chromophore.
The general approach used to follow the course of the photochemical reaction involves the construction and characterization of the so called “photochemical reaction path”. This is a minimum energy paths MEP[6] starting at the reactant structure and developing along the potential energy surfaces (PES) of the photochemically relevant states. Such interstate path usually originates at the Franck Condon (FC) point on the spectroscopic state and ends at the ground state photoproduct valley. Such an approach has been named the photochemical reaction path (see also Chapter 1) or, more briefly, pathway approach[7, 8]. Within this approach one pays attention to local properties of the potential energy surfaces such as slopes, minima, saddle points, barriers and crossings between states. The information accessible with this method is structural: i.e. the calculated path describes, strictly, the motion of a vibrationally cold molecule moving with infinitesimal momentum. While the path does not represent any “real” trajectory, it allows for a rationalization of different experimental data such as the excite-state lifetimes, the nature of the photoproducts and, more qualitatively, the quantum yields and transient absorption and emission spectra. As we will see in Section 2 this approach can be related to the common way of describing photochemical processes with the motion of the centre of a wave packet along the potential energy surfaces. [9] Notice also that the analysis of the photochemical reaction path is currently receiving new attention as a consequence of the recent advances in femtosecond spectroscopy and ultrafast techniques. [8]
In most past work, the energy surface structural features and, ultimately, the entire reaction path have been computed by determining the molecular wavefunction with state-of-the-art ab initio methods. In particular, a combined ab initio CASPT2[10, 11]//CASSCF[12–15] methodology has been extensively used since it has been proven to reproduce data with nearly experimental accuracy.[8, 16] This approach will be described in detail in Sections 3 together with a number of commonly used potential energy surface mapping tools. The operational procedure for approaching a photochemical problem will then be described and discussed in Section 4. The applications of such a procedure to the intriguing problems of determining the mechanism of the photoinduced cis-transisomerization of a retinal protonated Schiff base (RPSBs) model and of azobenzene (Ab) will be discussed in Sections 5 and 6 respectively. Both these chromophores have an extended conjugated π-system and are characterized by ultrafast and efficient cis-transisomerizations taking place upon photoexcitation. Thus, these systems can potentially be employed in nanotechnology for the design and construction of molecular devices such as random access memories, photon counters, picosecond photo detectors, neural-type logic gates, optical computing, light-switchable receptors and sensors, light addressable memories and molecular motors just to mention a few.[2, 17, 18] Finally, the complex network of reaction paths underlying the photochemical reactivity of cyclooctatetraene (taken as a representative of cyclic conjugated hydrocarbons) will be discussed in Section 7 to illustrate both general and subtle aspects of photochemical organic reaction mechanisms. Consumers facing threats from modern technology is nothing new. Although not entirely the same, consumers have faced similar threats from using personal computers, cellular phones, and the current electrical grid. As the dependency on technology grows, so does the dependency on electricity to power the technology. When you take away a single piece of technology from a consumer, such as a laptop or cell phone, the consumer might get angry but will easily adapt. Consumers can easily replace cell phones, but when access to electricity is taken away, consumers would find themselves without access to all of the technology they had grown reliant on. As such, smart grid threats will impact consumers in a variety of
With the advance of modern technology, big sensing data is commonly encountered in every aspect of industrial activity, scientific research and people's lives. In order to process that big sensing data with the computational power of the cloud effectively, different compression strategies have been proposed including data trend-based approaches and linear regression-based approaches. However, in lots of real-world applications, the incoming big sensing data can be extremely bumpy and discrete. Thus, at the big data processing steps of data collection and data preparation, the above compression techniques may lose effect in terms of scalability and compression due to the inner constraints of their predicting models. To improve the effectiveness and efficiency for processing those real-world big sensing data, in this chapter, a novel nonlinear regression prediction model is introduced. The related details, including regression design, least squares, and triangular transform, are also discussed. To explore fully the power and resource offered by the cloud, the proposed nonlinear compression is implemented with MapReduce for achieving scalability. Through our experiment based on real-world earthquake big sensing data, we demonstrate that the compression based on the proposed nonlinear regression model can achieve significant storage and time performance gains compared to previous compression models when processing similar big sensing data on the cloud.
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Aphorism and Truism in Knowledge Domain
Syed V. Ahamed, in Evolution of Knowledge Science, 2017
14.3.4.3 The insidious Role of Device and communication technologies
Three important influences of modern technologies become evident. First, the storage of personal preferences in handheld devices makes them ready for use at anytime and at any place. Second, the network access via the wireless cell phones and devices make it feasible to reach anyone at anytime and at anyplace. Third, the Internet access provides a lookup for the type of POC3 functions on the RC3emotional objects of the human self! Thus, the reaction time is reduced and Internet dialog is as feasible as “social interaction meeting.