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The huge market for Internet of Things (IoT) devices brings unprecedented opportunities for indoor photovoltaics (PV). Perovskite solar cells (PSCs) are one of the best candidates owing to their outstanding...

Assuring the required quality of service, despite the growing volume and variety of Internet of Things (IoT) traffic, has remained an immense challenge in the popular WiFi networks. The IoT devices using short TCP flows often attain very different levels of service due to the complicated interactions between the transport layer protocol and the shared dynamic wireless medium. We develop in this paper a novel queue management policy by using a transient model to capture the interactions of IoT (short TCP) flows over traditional traffic in WiFi networks. Based on the Markov regenerative processes coupled with fluid model, we discover that the adaptive admission control (AAC) mechanism at the WiFi access point (AP) improves the response time and fairness of IoT traffic over the lossy WiFi links. To this end, under the proposed AAC, packets are admitted into the AP in such a way that the fractions of packets in the AP buffer belonging to the different IoT devices are balanced resulting in a comparable level of service. We furthermore prove the stability of the system dynamics under AAC which provides important practical insights in designing home WiFi IoT system.

The power load demand of a data centre is in many ways different from that of a residential area. There are differences in the load profiles, the impact of the environmental parameters, the number of peak load demand occurrences in a day as well as peak and off-peak demand. The residential load demand is a complex function of human behaviour which varies based on the geographical, economical, and social characteristics of the consumers. In contrast, estimating the power load of a data centre is rather straightforward. Due to the specific nature of the power load demand of a data centre, finding the optimal configuration of the supplying microgrids can be achieved using off-the-shelf optimisers once an optimisation model has been established. This paper presents a model for finding high-quality microgrid configurations for an enterprise-size green data centres. We present an optimisation model that considers the costs and greenhouse gas emissions associated with all components of the microgrid system, as well as their interactions. The capital, operational, and degradation costs are calculated based on a lifetime of 20 years for the system. Our application of this model to a real scenario of a data centre with a given load demand in a specified environment demonstrates that the model can compose good-quality microgrid configurations for different tradeoffs of cost and sustainability.

Recently, there has been an increasing focus on cellular morphology and mechanical behavior in order to gain a better understanding of the modulation of cell malignancy. This study used uniaxial-stretching technology to select a mechanical regimen able to elevate SAOS-2 cell migration, which is crucial in osteosarcoma cell pathology. Using confocal and atomic force microscopy, we demonstrated that a 24 h 0.5% cyclic elongation applied at 1 Hz induces morphological changes in cells. Following mechanical stimulation, the cell area enlarged, developing a more elongated shape, which disrupted the initial nuclear-to-cytoplasm ratio. The peripheral cell surface also increased its roughness. Cell-based biochemical assays and real-time PCR quantification showed that these morphologically induced changes are unrelated to the osteoblastic differentiative grade. Interestingly, two essential cell-motility properties in the modulation of the metastatic process changed following the 24 h 1 Hz mechanical stimulation. These were cell adhesion and cell migration, which, in fact, were dampened and enhanced, respectively. Notably, our results showed that the stretch-induced up-regulation of cell motility occurs through a mechanism that does not depend on matrix metalloproteinase (MMP) activity, while the inhibition of ion–stretch channels could counteract it. Overall, our results suggest that further research on mechanobiology could represent an alternative approach for the identification of novel molecular targets of osteosarcoma cell malignancy.

Scattering scanning near-field optical microscopes (s-SNOMs) based on pseudoheterodyne detection and operating at ambient conditions typically suffer from instabilities related to the variable optical path length of the interferometer arms. These cause strong oscillations in the measured optical amplitude and phase comparable with those of the signal and, thus, resulting in dramatic artifacts. Besides hampering the comparison between the topography and the optical measurements, such oscillations may lead to misinterpretations of the physical phenomena occurring at the sample surface, especially for nanostructured materials. Here, we propose a stabilizing method based on interferometer phase control, which improves substantially the image quality and allows the correct extraction of optical phase and amplitude for both micro- and nanostructures. This stabilization method expands the measurement capabilities of s-SNOM to any slowly time-dependent phenomena that require long-term stability of the system. We envisage that active stabilization will increase the technological significance of s-SNOMs and will have far-reaching applications in the field of heat transfer and nanoelectronics.

Task offloading plays a critical role in cloud networked multi-robot systems for leveraging computation support from cloud infrastructure and benefiting greatly from the well-developed cloud network facilities. However, considering the delay constraint, the extra costs of data transmission and remote computation, it is not trivial to make optimized offloading decisions. In particular, task offloading for robots is more complex due to their on-demand mobility and network connectivity that significantly influence the robot–cloud communication links. Moreover, for multi-robot systems, a suitable balance of workload between local network (robot–robot) and global cloud (robot–cloud) is also required, so as to attain proper utilization of resources. Therefore, it is essential to establish more comprehensive offloading schemes for modeling systems that can handle these higher level of complications. With that view, this paper aims to develop a novel multi-layer decision-making scheme for task offloading which jointly considers the following four aspects: (i) selection of task for offloading, (ii) selection of robot to offload a task, (iii) selection of location to offload/perform task, (iv) selection of access point for offloaded task. An integrated framework for cloud networked multi-robot systems is presented to enable our task offloading scheme where the primary robot can aid from additional local robots to improve the offloading process. In particular, we consider a warehouse scenario with 36 cell workspace where a 40 node taskflow is motivated from a “parcel sorting and distribution” application, to be completed by the primary robot. The offloading decision for each task is formulated as part of a joint optimization problem and it is solved by developing a multi-layer genetic algorithm scheme that takes into account motion, network connectivity and local sharing for its offloading decisions. We evaluate the results of the scheme via comparison with two validated benchmarks. The outcome highlights a significant improvement in overall system performance due to joint involvement of motion (path planning), connectivity (bandwidth estimation) and robot–robot communication (local offloading) that facilitates energy-efficient offloading to cloud, faster completion of tasks and better utilization of available resources.

Vision-based measurement (VBM) systems are powerful tool to extract quantitative information by acquiring video sequence or static images. When a VBM is applied to the evaluation of nominal properties, such as image characteristics, the term VBM is substituted with vision-based evaluation (VBE) by extending the framework of operation unit to a new concept of evaluation unit (EU) for the image analysis and machine learning phases. To this regard, deep learning (DL) approaches have gained an exponential interest in the research and industrial community, thanks to incredible flexibility toward visual words and the possibility to abandon subjective feature extraction procedures. From such an explosiveness of applications, it emerges the need to conduct studies on the capability of DL strategies to deal with uncertainty contributions, i.e., definitional uncertainty related to the measurand and reference uncertainty that may occur during the calibration process. In order to present a benchmark platform to analyze the effect of the major sources of uncertainties estimated, we use here an atomic force microscopy (AFM) imaging scenario for the evaluation of the effect of nanoparticles exposure on human cells in the laboratory. These studies are nowadays fundamental in toxicity analysis for monitoring the health conditions of workers and for protecting people from atherosclerosis disease. The performance of the proposed VBE-DL system to recognize cell alterations from the AFM images is related to three different sources of uncertainty and a critical analysis of the results achieved is provided.

The fourth industrial revolution introduces an ideal opportunity for inclusion of cloud-enabled robots in a factory environment to improve productivity and reduce human intervention. For this novel paradigm, task offloading plays a critical role in leveraging computation support from resourceful cloud infrastructure. In particular, network connectivity and on-demand mobility of robot significantly influence the task offloading decision-making and vice-versa. While current studies in the literature separately consider mobility or communication aspects to accommodate offloading, ours is the first approach to integrate these three interdependent factors together in order to formulate a joint optimization problem for the proposed oil factory maintenance application. A modified genetic algorithm scheme is then developed to solve the problem with a novel 3-layer decision: task offloading, path planning, and access point selection. Simulation results and comparison with existing techniques suggest that communication-aware and mobility-driven offloading in industrial scenario leads to superior system performance and minimum consumption of resources.

Background: The blood–brain barrier (BBB) bypass of dopamine (DA) is still a challenge for supplying it to the neurons of Substantia Nigra mainly affected by Parkinson disease. DA prodrugs have been studied to cross the BBB, overcoming the limitations of DA hydrophilicity. Therefore, the aim of this work is the synthesis and preliminary characterization of an oxidized alginate-dopamine (AlgOX-DA) conjugate conceived for DA nose-to-brain delivery. Methods: A Schiff base was designed to connect oxidized polymeric backbone to DA and both AlgOX and AlgOX-DA were characterized in terms of Raman, XPS, FT-IR, and 1H- NMR spectroscopies, as well as in vitro mucoadhesive and release tests. Results: Data demonstrated that AlgOX-DA was the most mucoadhesive material among the tested ones and it released the neurotransmitter in simulated nasal fluid and in low amounts in phosphate buffer saline. Results also demonstrated the capability of scanning near-field optical microscopy to study the structural and fluorescence properties of AlgOX, fluorescently labeled with fluorescein isothiocyanate microstructures. Interestingly, in SH-SY5Y neuroblastoma cell line up to 100 μg/mL, no toxic effect was derived from AlgOX and AlgOX-DA in 24 h. Conclusions: Overall, the in vitro performances of AlgOX and AlgOX-DA conjugates seem to encourage further ex vivo and in vivo studies in view of nose-to-brain administration.

In this paper, we propose a novel mathematical framework to formulate a unified Bus Rapid Transit (BRT) with Transit Signal Priority (TSP) system for single destination networks, namely, Bus Priority System Optimal Dynamic Traffic Assignment with Signal Control (BP SODTA-SC). This framework considers dedicated bus lanes, bus routes, and priority for public bus transport in mixed bus-car scenarios. Furthermore, this approach assures fairness to all road users. It is linear and can be applied to analyze city-size BRT-TSP systems. Our numerical results show that bus priority significantly reduces Total System-wide Passenger Travel Time (TSPT).

Citrate-capped gold nanoparticles (AuNPs) were functionalized with three distinct antitumor gold(III) complexes, e.g., [Au(N,N)(OH)2][PF6], where (N,N)=2,2&prime, bipyridine, [Au(C,N)(AcO)2], where (C,N)=deprotonated 6-(1,1-dimethylbenzyl)-pyridine, [Au(C,N,N)(OH)][PF6], where (C,N,N)=deprotonated 6-(1,1-dimethylbenzyl)-2,2&prime, bipyridine, to assess the chance of tracking their subcellular distribution by atomic force microscopy (AFM), and surface enhanced Raman spectroscopy (SERS) techniques. An extensive physicochemical characterization of the formed conjugates was, thus, carried out by applying a variety of methods (density functional theory&mdash, DFT, UV/Vis spectrophotometry, AFM, Raman spectroscopy, and SERS). The resulting gold(III) complexes/AuNPs conjugates turned out to be pretty stable. Interestingly, they exhibited a dramatically increased resonance intensity in the Raman spectra induced by AuNPs. For testing the use of the functionalized AuNPs for biosensing, their distribution in the nuclear, cytosolic, and membrane cell fractions obtained from human lymphocytes was investigated by AFM and SERS. The conjugates were detected in the membrane and nuclear cell fractions but not in the cytosol. The AFM method confirmed that conjugates induced changes in the morphology and nanostructure of the membrane and nuclear fractions. The obtained results point out that the conjugates formed between AuNPs and gold(III) complexes may be used as a tool for tracking metallodrug distribution in the different cell fractions.

With the dramatic worldwide growth of information technology sectors, the traffic of data centres is projected to experience an increase of about 80% by 2020. Green data centres are proposed as a solution towards sustainable platform providing IT services. This paper presents an optimal microgrid configuration for a green data centre. The mathematical models for the costs and the green gas emissions associated with all components of the microgrid system as well as their interactions are taken into consideration in the optimisation model. The capital, operational, and degradation costs during a life-time of 20 years of the system are also considered. The optimum microgrid configuration is composed of 38% renewable energy generation that reduces the total cost by 37% and greenhouse gas emissions by 16%.

Smart grids are predicted to play an important role in the future power system that will be deployed in smart cities. The integration of renewable power generation, energy storage, and demand response algorithms has led to the development of complex structures for managing the increasing energy demands. This paper proposes a decentralised multi-agent based demand response algorithm designed for a smart grid that relies on photovoltaic generation while feeding both a green data centre and supplying the residential load demand. The main objective of the proposed methodology is to minimise the total daily costs incurred due to shortfalls in the solar energy generation. The cost optimisation is performed by taking the user inconvenience of the residential load and the energy cost into account. The simulation results show that participation of green data centres in demand response can significantly reduce the costs incurred across the system due to shortages in renewable power as well as reducing CO2 emissions.

In this paper, a structured learning based sinusoidal modelling approach is developed for the prognostics of gear tooth cracking process. According to the vibration signal properties, a learning structure is firstly proposed for gear mesh vibration signal modelling. The learning model is deigned to be a representation of the vibration signal, including all harmonic components generated by the normal gear operation, the residual periodic signal affected by the system load and equipment wear, and the residual non-periodic part induced by gear tooth crack and system disturbances. Motivated by neural network learning, the signal model parameters are optimized by the error back-propagation process, using stochastic gradient descent. The proposed structured learning approach is applied to model the gear tooth cracking process using a set of rig test data. The excellent performance of this approach shows that different parts of the vibration signal are decomposed, and the gear tooth cracking development can be successfully monitored.

A single human oesophageal adenocarcinoma cell (OE33) has been imaged using aperture infrared scanning near-field optical microscopy (IR-SNOM) in transmission and reflection and also by Fourier-transform infrared (FTIR) microspectroscopy in transmission only. This work presents the first images obtained in both transmission and reflection of the same specimen using the aperture IR SNOM technique. The results have been used to compare the two SNOM modes and also the two techniques, which have complementary capabilities. The SNOM technique necessitates a very stable source and a careful choice of wavelengths, since it is too slow to yield images at the thousands ofwavelengths obtained with FTIR. However the SNOM technique is not diffraction limited and with careful fabrication of tips can yield images with high spatial resolution. There is no significant correlation between the SNOM images obtained in transmission and reflection and the correlations between images obtained at different wavelengths vary with the different imaging modes. These results are attributed to the strong dependence of the evanescent wave on both the wavelength and the distance between the tip and the source of the signal within the sample. While both transmission and reflection SNOM images show some correlation with topography this is not a dominant effect. These results indicate that with suitable calibration a combination of reflection and transmission aperture IR-SNOM measurements has the potential to reveal information on the depth distribution of the chemical structure of a specimen.

Engineered nanomaterials play an even more relevant role in nanotechnology advances. However, care must be taken due to their suspected detrimental effects on human cells. Such alterations can be monitored through Atomic Force Microscopy (AFM) equipment and image digitalization. With the purpose to depict a metrological compliant scenario, a novel vision-based evaluation system is proposed with an evaluation unit based on a deep learning architecture. Inspired by the recent trends in trying to extend the standard concept of quantities to nominal properties and measurement to evaluation, we proposed here a platform for the evaluation of morphological alterations in AFM images of human cells exposed to different concentrations of carbon nanotubes. Results reveal the feasibility to automatically investigate such alterations with the aim to improve occupational medicine protocols and cells cataloguing procedures.

Nowadays, visual-based measurement (VBM) systems offer new possibilities of investigation for researchers and clinicians, using noninvasive–nondestructive approaches. In this paper, we present an atomic force microscopy (AFM)-based VBM for the study of cell-cerium oxide nanoparticle interactions. To provide a metrological characterization of the results obtained and with the aim to compare different strategies, we modeled four artifacts effects occurring in AFM acquisition within the random process theory and implemented a Monte Carlo simulation to repeatedly inject such variability in the original image. Empirical cumulative distribution function, confidence intervals, and average representative values, following Supplement 1 guidelines, were estimated for the final scores assigned by the operations unit to each cell. Area under the roc curve and accuracy of classification for two different machine learning approaches were compared in a metrological compliant methodology. Results clearly demonstrate the robustness of the presented VBM system and quantify the uncertainty expected for such kind of results.

The development of more accurate and sensitive diagnostic techniques is a key factor in efforts to improve cancer survival rates. The technique of infrared aperture fibre scanning near-field optical microscopy (IR-SNOM), together with radiation from the infrared free-electron laser (IR-FEL) on ALICE at Daresbury Laboratory (UK), has been used to obtain IR images of a crypt-like feature and the surrounding tissue; the tissue was taken from a patient with oesophageal adenocarcinoma and with a history of Barrett's oesophagus. We have shown that the DNA signal is enhanced relative to other contributions in the region of the crypt, and the glycoprotein signal shows a less pronounced increase in the region of the crypt. The Amide II signal is found to be anti-correlated with the DNA and glycoprotein profiles. The absorbance of the Amide II signal is found to differ for three different types of cancer tissue. High-resolution IR images of the crypt reveal additional structure that would not be resolved in diffraction-limited techniques.

Task offloading opens a gateway for robotic applications to leverage computation support from the cloud infrastructure. It exploits a trade-off between the robot's and cloud's processing capabilities, and the communication between the two entities plays a critical role in making these decisions. Two major factors that significantly influence communication are network connectivity (bandwidth) and mobility of the robot. We integrate these two factors with the offloading decisions to formulate an optimization problem. Our objective in this paper is to improve the quality of service (QoS) for a 25 node application taskflow, known as direct acyclic graph. We propose a genetic algorithm based approach to solve the optimization problem which performs a novel three-layer decision making: (i) whether to offload a task or not, (ii) path planning to reach a desired location for offloading/local execution, (iii) select access point to associate with for offloading. We simulate for a smart city scenario consisting of 36-cell workspace with obstacles and compare the offloading results with a well- established fixed movement offloading method. The outcomes of our study suggest that motion and connectivity aware offloading leads to more efficient performance in terms of improved QoS and minimum consumption of resources, i.e., energy, time or distance.

In this paper, lateral vehicle dynamics is further studied, and a sliding mode (SM) control for vehicle lane-keeping system with system uncertain dynamics is developed. In order to achieve smooth vehicle lane-keeping performance, a specified motion trajectory of the vehicle is considered during the process of modelling lateral vehicle dynamics. An SM controller can then be developed based on the bound information of system uncertainties. With the proposed SM controller, lateral offset will asymptotically converge to zero first in a smooth manner. Then vehicle head direction will asymptotically converge to the tangent of the road curve with the equivalent control input signal. Simulation results demonstrate the excellent vehicle lane-keeping performance with the proposed SM control.

Using a scanning near-field optical microscope coupled to an infrared free electron laser (SNOM-IR-FEL) in low-resolution transmission mode, we collected chemical data from whole cervical cells obtained from 5 pre-menopausal, non-pregnant women of reproductive age, and cytologically classified as normal or with different grades of cervical cell dyskaryosis. Imaging data are complemented by demography. All samples were collected before any treatment. Spectra were also collected using attenuated total reflection, Fourier-transform (ATR-FTIR) spectroscopy, to investigate the differences between the two techniques. Results of this pilot study suggests SNOM-IR-FEL may be able to distinguish cervical abnormalities based upon changes in the chemical profiles for each grade of dyskaryosis at designated wavelengths associated with DNA, Amide I/II, and lipids. The novel data sets are the first collected using SNOM-IR-FEL in transmission mode at the ALICE facility (UK), and obtained using whole cells as opposed to tissue sections, thus providing an ‘intact’ chemical profile. These data sets are suited to complementing future work on image analysis, and/or applying the newly developed algorithm to other datasets collected using the SNOM-IR-FEL approach.

Atomic force microscopy is gaining interest as a technique to quantitatively study biological samples in native environment. However, the measuring principles behind may cause the presence of different sources of artifacts and image degradations. In this work, we present an AFM image analysis tool able to recognize morphological alterations in human leukemia cells after 3 h incubation with the antioxidant cerium oxide nanoparticles. To demonstrate the robustness of the approach to a particular artifact called contrast reversal (CR) effect, consisting in an unexpected switching between repulsive and attractive tip regime during scanning, we present a technique to artificially inject the artefact on the image and then apply the tool. Maximum area under the ROC (AUC) curve results of 0.91 (0.11) in the discrimination between exposed and unexposed cells confirm the validity of the approach and its applicability in AFM-based cell studies.

The morphological changes induced by engineered nanomaterials (ENMs) in cellular samples are a key clue to evaluate the impact of these materials on human health. The investigation of the complexity of the interaction among nanoparticles and cellular molecules requires cutting-edge instrumentation and dedicated procedures. To this regard, atomic force microscopy (AFM) is a leading imaging technique that has the peculiarities of high resolution and direct relationship with 3D cellular morphology. Expert human operators, however, are still required to manage most of the AFM-based analysis, thus introducing subjective bias and allowing a limited number of trials. The modality of interaction and the resulting topographic differences are often not a priori known. Also, the presence of artefacts in AFM images may affect the derived conclusions. In this work, we propose a robust and flexible strategy to analyse AFM topography images with single click-select actions by the human operator. The proposed system allows for not only morphological studies and quantification of the changes occurring in cellular samples in the presence of nanomaterials, but also for the investigation of diversified experiments in more flexible application domains. As a proof of concepts, samples of human EA. hy926 endothelial cells exposed to carbon nanotubes are used to demonstrate the effectiveness of the proposed solution. The system is also tested against various AFM-artefact and noise scenarios and the robustness of its discrimination capability is verified. (C) 2017 Elsevier B.V. All rights reserved.

Cloud robotics is an emerging paradigm that enables autonomous robotic agents to communicate and collaborate with cloud computing infrastructures. It further complements Internet of Things (IoT) to improve the performance of smart city applications. By offloading heavy data- intensive computation to the ubiquitous cloud, quality of service (QoS) guarantee can be ensured. Unlike their mobile counterpart, the robots have unique characteristics of mobility, skill- learning, data collection and decision-making capabilities, which makes offloading decisions significantly complex. This paper proposes a generic cloud robotics framework to realize smart city vision while taking into consideration its various complexities. Specifically, task offloading is formulated as a constrained optimization problem capable of handling Direct Acyclic Graph (DAG) known as task flow. Given the constraints, a genetic algorithm (GA) based scheme is further developed to solve the problem. The performance of the algorithm is verified by evaluating the results via three benchmarks. To the best of our knowledge, this is one of the first attempts of task offloading approach for smart city applications of cloud robotics.

In this paper, a novel sliding mode (SM) control is developed for vehicle lane keeping. The proposed SM controller can enable the vehicle to track the reference road center-line and yaw direction with zero errors. The idea behind the SM controller are as follows: first, lateral vehicle dynamics system is divided into two subsystems. Lateral position error dynamics system is recognized as a predominant subsystem, while yaw angle error dynamics system is an affiliate subsystem. Then, an SM controller is designed for the predominant subsystem to achieve asymptotical convergence of lateral position error. Since the components related to yaw angle dynamics in the equivalent control input signal of front wheel steering angle have the pole-placement function to yaw angle dynamics, the stability of the affiliate subsystem and asymptotical convergence of yaw angle error finally can be ensured. Thus, the SM controller designed for predominant subsystem can achieve decoupled control for both lateral position error dynamics and yaw angle error dynamics. The excellent performance of the proposed SM control is demonstrated by simulation results.

This paper proposes a new dynamic neural modelling method for mechatronic system prognostics. A simulated turbofan engine system is firstly introduced. Then, so-called health index (HI) for the system is evaluated by an artificial neural network model called extreme learning machine (ELM) to recognize the health conditions. In order to track and predict the propagation process of the system based on the health index, a new dynamic neural network (DNN) with feedback from outputs to both output layer and hidden layer is developed. With the batch learning type of least square, both the feedback weights and output weights are optimized. With the turbofan engine datasets from NASA, simulations are carried out, using both the ELM model and the proposed DNN. The excellent performance of the proposed DNN shows that the DNN with hidden layer feedback has a superior capability of capturing the dynamic features of the mechatronic system.

Cervical cancer remains a major cause of morbidity and mortality among women, especially in the developing world. Increased synthesis of proteins, lipids and nucleic acids is a pre-condition for the rapid proliferation of cancer cells. We show that scanning near-field optical microscopy, in combination with an infrared free electron laser (SNOM-IR-FEL), is able to distinguish between normal and squamous low-grade and high-grade dyskaryosis, and between normal and mixed squamous/glandular pre-invasive and adenocarcinoma cervical lesions, at designated wavelengths associated with DNA, Amide I/II and lipids. These findings evidence the promise of the SNOM-IR-FEL technique in obtaining chemical information relevant to the detection of cervical cell abnormalities and cancer diagnosis at spatial resolutions below the diffraction limit (≥0.2 μm). We compare these results with analyses following attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy; although this latter approach has been demonstrated to detect underlying cervical atypia missed by conventional cytology, it is limited by a spatial resolution of ~3 μm to 30 μm due to the optical diffraction limit.

Extremely brilliant infrared (IR) beams provided by synchrotron radiation sources are now routinely used in many facilities with available commercial spectrometers coupled to IR microscopes. Using these intense non-thermal sources, a brilliance two or three order of magnitude higher than a conventional source is achievable through small pinholes (

The need to image objects on increasingly finer scales and spatially localized specific molecules can be met by the combination of infrared, visible and Raman spectroscopy with scanning near-field microscopy, giving rise to a powerful nanospectroscopic tool used to perform simultaneous topographical measurements and optical/chemical characterization with subwavelength resolution, overcoming the diffraction limit of light. Conventional spectroscopy is often not enough sensitive and spatially resolved to detect specific elements or domains in a sample. Scanning near-field optical microscopy is a sensitive and flexible technique that achieves an enhanced optical resolution through the very close placement of the sensing element to the object. We present several results of near field spectroscopy with infrared radiation emitted by a free electron laser to investigate material science and biological samples. The local reflectivity revealed features, as a function of photon energy, that were not present in the corresponding shear-force (topology) images and were due to localized changes in the bulk properties of the sample. The size of the smallest detected features clearly demonstrated that near-field conditions were reached, with an optical spatial resolution well below the diffraction limit.

The capability to monitor finely the physical properties of eumelanin, an important class of biopolymers, involved in melanoma cancer pathologies, whose function and intrinsic disorder still collects the interest of many investigators, was achieved by means of electrospray deposition (ESD). By alleviating the problem of the solubility of melanin through the realization of high-quality films it was possible to spread light on the unknown biopolymer supramolecular organization. In fact, on the basis of scanning probe microscopies, electron spectroscopies, and transport properties, it was possible to delineate peculiar features of the melanin organization varying from heteropolymeric to oligomeric in character and eventually turning in a cross-linked secondary molecular structure.

We recently developed an atomic force microscopy-based protocol to use the roughness of the plasma membrane of erythrocytes (red blood cells, RBCs) as a morphological parameter, independently from the cell shape, to investigate the membrane-skeleton integrity in healthy and pathological cells. Here we apply the method to investigate a complex physiological phenomenon, the RBCs aging, that plays a major role in the regulation of the RBCs' turnover. The aging, monitored morphologically and biochemically, has been accelerated and modulated by preventing oxidative stresses as well as the effects of proteases and divalent cations, and by artificially consuming the intracellular adenosine triphosphate. The collected data evidence that the progression of aging causes a drastic decrease of the measured roughness that is diagnostic of a progressive, adenosine triphosphate-dependent alteration of the membrane-skeleton properties. Finally, the degree of reversibility of such effects has been investigated as a function of aging time, enabling the detection of irreversible transformation in the RBCs' structure and metabolism.

This work reports the use of an infrared spectroscopic version of scanning near-field optical microscopy (IR-SNOM) and shows that it is possible to reveal subcellular entities via their chemical constituents in a label-free human oesophageal adenocarcinoma cell (OE33). This work presents the first high spatial resolution (similar to 0.15 mu m) study of features imaged at various wavelengths within a cancer cell. The clear illumination of sub-micron sized moieties within a cell is a major step forward and is a key requirement for understanding cancer and for the study of other diseases and healthy tissue. The stable and tuneable light source was provided by the infrared free electron laser on the ALICE accelerator at Daresbury. The images reveal a structure with a size and wavelength absorption that are consistent with a chromosome and open the possibility of observing other localized structures, such as microvesicles, that play an important role in the development and spread of cancers.

A hybrid conventional inverted/scanning near-field optical microscope (Inverted/SNOM) has been obtained, by coupling an Olympus IX-70 optical microscope with a SNOM head to combine the versatility and ease of use of the conventional microscope with the high-resolution and three-dimensional reconstruction obtainable with the SNOM technique. The head can be run in shear or tapping mode and is optimized to characterize soft, biological samples including living cells in physiological environment by including the SNOM in a cylindrical chamber that insulates it from the external noise, while maintaining a controlled temperature and atmosphere. Applications in the fields of biology and material science are presented.

Scanning near field optical microscopy (SNOM) is a well-established technique to obtain a sub-wavelength resolution optical characterization, together with nanometer-scale topography images, on any kind of biological or non-biological sample. Recently we have modified a classical SNOM unit to work in the tapping modality, ensuring stability, versatility and good optical resolution and signal to noise ratio. Exploiting the vertical tip movement, in particular, we were able to access two different optical detection modes: light modulation, which can be obtained by a mechanical chopper or by electronically switching the laser on and off; gap modulation in which the tip's vertical oscillation is used to produce, itself, a modulation of the collected light. Several biological and non-biological samples have been investigated and the data reveal that, despite the signal collected in gap-modulation is at least one order of magnitude smaller than in laser-modulation, resolution, and signal-to-noise ratio in the gap-modulated images is preferable. On the other hand, the higher intensity of the laser-modulation signal allows to deconvolve the optical information at higher harmonics of the tip oscillation frequency. This is a well-known procedure used in the apertureless-SNOM setup to enhance the near-field contribution of the scattered light and reduce the noise content. The interesting results obtained in this Aperture setup are described and commented.

The Si/Ag(110) interface is attracting increasing interest since the recent claim that the one-dimensional (1D) nanostructures formed at this surface would be silicene nanoribbons. Lately, the synthesis of multilayer silicene nanoribbons was reported as well. In this paper, we report on the scanning tunneling microscopy, low-energy electron diffraction, and density functional theory study of the 1D nanostructures forming on Ag(110) upon Si deposition at temperatures above 460 K. Two different kinds of nanostructures are here described: nanodikes and nanotrenches, respectively protruding from and engraved on the Ag(110) substrate. The first kind of structure was recently reported to be constituted by multistacks of silicene nanoribbons. We show here that nanodikes and nanotrenches are the result of silver faceting stabilized by silicon atoms. Indeed, such nanostructures share the very same facet crystallographic features, namely inclination and reconstruction: the top and base parts are Ag(110) planes, while their sides are Ag(221) and Ag(22$\overline{1}$) facets stabilized by Si atoms. Finally, density functional theory calculations suggest that Si atoms may substitute Ag atoms in such facets, casting doubts on the usual assumption that Ag is a suitable substrate for silicene growth because it is inert against silicon.

The optical properties of metal nanoparticles play a fundamental role for their use in a wide range of applications. In hyperthermia treatment, for example, gold nanoshells (NSs, dielectric core+gold shell) pre-embedded in a cancer cell absorb energy when exposed to appropriate wavelengths of a laser beam and heat up, thereby destroying the cancer cell. In this process, nevertheless, healthy tissues (not targeted by the NSs) along the laser path are not affected; this is because most biological soft tissues have a relatively low light absorption coefficient in the near-infrared (NIR) regions—a characteristic known as the tissue optical window. Over such a window, NIR light transmits through the tissues with scattering-limited attenuation and minimal heating, thereby avoiding damage to healthy tissues. As a consequence, the identification of NSs assumed a fundamental role for the further development of such cancer treatment. Recently, we have demonstrated the possibility to identify 100–150 nm diameter gold NSs inside mouse cells using a scanning near-optical microscope (SNOM). In this paper, we provide a numerical demonstration that the SNOM is able to locate NSs inside the cell with a particle–aperture distance of about 100 nm. This result was obtained by developing an analytical approach based on the calculation of the dyadic Green function in the near-field approximation. The implications of our findings will remarkably affect further investigations on the interaction between NSs and biological systems.

The paper presents the results of the experimental and theoretical study of 3,3'-disulfopropyl-5,5'-dichlorothiacyanine sodium salt (TC) adsorption and J-aggregation on the surface of citrate-capped silver nano particles (AgNPs) with an average particle size 10 nm. Various nanospectroscopy techniques such as UV-vis, TEM, AFM, DLS, zeta potential, and fluorescence measurements were carried out in order to characterize the hybrid system. Besides this, a set of simple density functional theory calculations (DFT) was performed and this suggested that TC dye, from the thermodynamical point of view, most likely interacted with AgNPs via oxygen atom from SO3- groups. The methods, which considered AgNPs as the macromolecule with several binding sites and TC dye as the ligand, were applied for the analysis of saturation curves constructed from the fluorescence data to obtain the binding constant (K-a = 1.5 x 10(6) M-1) and the average number of TC dye molecules bonded per AgNP (similar to 330). Moreover, TC fluorescence was quenched in the presence of AgNPs on the concentration dependent manner, yielding the linear Stern-Volmer relation accounting for both static and dynamic quenching with the quenching constant K-sv = 2.5 x 10(8) M-1. Kinetic measurements of J-aggregation as the dependence of AgNPs/TC concentration ratio confirmed that the J-aggregation occurred via a two-step process, the first of them being the initial adsorption of dye on AgNPs surface, followed by dye molecules rearrangement and the consecutive growth of TC aggregate layers.