1,721,097 research outputs found
Modelling Molecular Self-Organisation: computational techniques for the study of self-organising chemical systems
No full textInfluence of linker flexibility on the binding affinity of bidentate binders
No full textThe design of responsive nanosensors typically relies on the availability of probes capable of capturing their target with high affinity and specificity. This can be achieved by coupling two or more binding units through a linker. In this work, we study the dependence on the binder architecture of the binding affinity between a target molecule and a semirigid bidentate binder. Using two different binder architectures, central-rigid and extreme-rigid, and modifying the length and the flexibility degree of the linker we generated 153 different architectures. We computed their dissociation free energies by means of Monte Carlo simulations and thermodynamic integration. We found that central-rigid bidentate binders are a poor choice, as they dissociate more easily than analogous fully flexible bidentate binders. On the other hand, molecular architectures presenting extreme-rigid units were shown effective for a wide range of set-ups
Oxalic acid adsorption states on the clean Cu(110) surface
No full textCarboxylic acids are known to assume a variety of configurations on metallic surfaces. In particular oxalic acid on the Cu(110) surface has been proposed to assume a number of upright configurations. Here we explore with DFT calculations the possible structures that oxalic acid can form on copper 110 at different protonation states, with particular attention at the possibility of forming structures composed of vertically standing molecules. In its fully protonated form it is capable of anchoring itself on the surface thanks to one of its hydrogen-free oxygens. We show the monodeprotonated upright molecule with two oxygens anchoring it on the surface to be the lowest energy conformation of a single oxalic molecules on the Cu(110) surface. We further show that it is possible for this configuration to form dense hexagonally arranged patterns in the unlikely scenario in which adatoms are not involved
Diagnosi, abduzione e metafora del testo: aspetti storici e metodologici
No full texta cura di G. Gall
Nonlinear output feedback control and simulations for launch vehicles
Full text linkThe launch vehicle is an autonomous system and thus the control design plays a crucial role for the mission success.
The flight Software, which runs on the On Board Computer, cyclically executes the GNC (Guidance Navigation and Control) algorithms to control the launcher from lift-off up to payloads release.
The control design for a launcher vehicle presents several difficulties due to the complexity of the system. Some of the reasons that discourage from performing and implementing complicated control laws are:
• The intrinsic instability of the vehicle
• The presence of several disturbances (e.g. aerodynamic loads and elastic modes)
• The high variation of the structure (time varying properties)
• The high number of states derived from the equations of motion
• The algorithms computational time
These are ones of the main reasons that justify the use of "gain scheduling" approach for this kind of control problems, that consists in linearizing the model around specific trajectory instants, designing the respective controller and checking the system properties by means
of the well-known LTI tools. Even If this approach has been proven to work in the industry, it leaves always some amount of uncertainty due to the approximation forced on the original system (the nonlinear and time varying nature of the launcher are not properly considered).
In order to be able to design a control algorithm for this kind of system it is fundamental to derive reduced orders models, depending on the particular phase under analysis (so the modeling aspect is always crucial for the control engineer). Differently from the standard LTI methods, the nonlinear control theory allows to take into account more representative models. Nonlinear control feedback linearization requires however full state feedback information and an accurate plant modeling in order to work. To overcome the usual lack of knowledge
on all the system states an observer is added in the control architecture resulting in a Nonlinear Output feedback control scheme for the attitude regulation problem. This controller has better performances under specific conditions since it allows to work with a nonlinear model instead of a linearized one. However, a real improvement for launcher control design would be to focus on design methods and stability metrics that are capable of handling without restrictions the time varying nature of this system
Agent-based modeling for the 2D molecular self-organization of realistic molecules
No full textWe extend our previously developed agent-based (AB) algorithm to the study of the self-assembly of a fully atomistic model of experimental interest. We study the 2D self-assembly of a rigid organic molecule (1,4-benzene-dicarboxylic acid or TPA), comparing the AB results with Monte Carlo (MC) and MC simulated annealing, a technique traditionally used to solve the global minimization problem. The AB algorithm gives a lower energy configuration in the same simulation time than both of the MC simulation techniques. We also show how the AB algorithm can be used as a part of the protocol to calculate the phase diagram with less computational effort than standard techniques
An artificial intelligence approach for modeling molecular self-assembly : agent-based simulations of rigid molecules
No full textAgent-based simulations are rule-based models traditionally used for the simulations of complex systems. In this paper, an algorithm based on the concept of agent-based simulations is developed to predict the lowest energy packing of a set of identical rigid molecules. The agents are identified with rigid portions of the system under investigation, and they evolve following a set of rules designed to drive the system toward the lowest energy minimum. The algorithm is compared with a conventional Metropolis Monte Carlo algorithm, and it is applied on a large set of representative models of molecules. For all the systems studied, the agent-based method consistently finds a significantly lower energy minima than the Monte Carlo algorithm because the system evolution includes elements of adaptation (new configurations induce new types of moves) and learning (past successful choices are repeated)
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