Due to the nonionic character of the analyzed macromolecular compound, the polymer attendance has an insignificant effect on the AST 50 surface charge density. In the case of the adsorbent particles zeta potential, the obtained dependencies are different in the absence and presence of PVA. The shift of the slipping plane and displacement of the counter-ions from Stern layer by the adsorbed polymer chains have the greatest effect on the ζ potential value. The stability measurements indicate that the AST 50 suspensions in the presence of the background electrolyte at pH 3 and 6 are unstable. In turn, in an alkaline medium the mixed oxide suspensions exhibit the highest durability, which is a result of a large number of the negative charges on the AST 50 surface. The addition of PVA 100 significantly improves the suspension stability at pH 3 and 6; at higher pH value, the polymer presence does not influence the system durability. It is related to the steric and electrosteric stabilization of the colloidal particles by the adsorbed polyvinyl alcohol macromolecules.A new adsorbent consisting of fumed, mixed alumina, silica, and titania in various proportions (AST 50) was investigated. The studied material was prepared by chemical vapor deposition method. The diameter of AST 50 primary particles was equal to about 51 nm which denotes that it can be classified as a nanomaterial. In the presented paper, the adsorption properties of polyvinyl alcohol on the ternary oxide were investigated. The polymer macromolecules were characterized by two different molecular weights and degree of hydrolysis. The polymer adsorption reaches the maximum at pH 3 and decreases with the solution pH rise. The reduction of the adsorbed PVA macromolecules is related to the electrostatic repulsion forces occurring in the studied system. The AST 50 point of zero charge (pH Polymer adsorption process at the solid–liquid interface has numerous applications in various branches of the industry and human activity Mauer . MacromoTechnical development and improvement of macromolecular substances adsorption processes necessitate the search for new materials and methods for their synthesis. An outstanding group of adsorbents are mixed oxides , which include compounds of the suitable metals such as silicon, aluminum, and titanium (in various proportions). These materials possess significantly different properties and structure compared to the starting substrates. The investigations including the determination of the ternary oxides suspensions behavior are very important in relation to its unusual properties resulting from the surface structure. In addition, it is possible to obtain the nanooxides of the desired type, amount, and distribution of the surface active groups which provide appropriate binding of simple ions or polymer macromolecules. Thus, these adsorbents can be used in many industrial areas as catalysts, pigments, fillers, adsorbents, and ion exchangers , which is a high novelty element.2O3–SiO2–TiO2 surface (AST 50) in the presence of the background electrolyte as a function of the solution pH,Determination of the adsorbed amount of the nonionic polymer—polyvinyl alcohol (PVA) on the mixed oxide AlInvestigation of the electrokinetic properties of AST 50 nanooxide and examination of the polymer molecular weight and hydrolysis degree influence on the adsorbent surface charge density as well as the zeta potential value,Study of the AST 50 suspension stability changes in the absence and presence of the nonionic PVA as a function of the solution pH,Propose of the stabilization/destabilization mechanism of the AST 50 mixed oxide aqueous suspension with the nonionic polymer.Assuming great demand for new materials with specific adsorption properties, the goal of this paper wasSBET) was estimated using the nitrogen adsorption–desorption isotherms at 77.4 K . The average diameter of primary particles was 51 nm  , silica (28 wt%), and titania (50 wt%) was prepared by the chemical vapor deposition (CDV) method at the Institute of Surface Chemistry, National Academy of Sciences, Ukraine. The specific surface area of the adsorbent . The polymer average molecular weights were 72,000 (PVA 72) and 100,000 Da (PVA 100). The hydrolysis degree for both substances was 97.5–99.5 % for PVA 72 and 86–89 % for PVA 100, respectively. These data were supplied by the producer. Polyvinyl alcohol belongs to the nonionic group of polymers. However, as a result of incomplete hydrolysis of the starting material, the polymer macromolecules contain a certain number of acetic groups, which are capable of ionization with the solution pH increase; Fig. 3 NaCl), and the doubly distilled water were added in order to obtain the polymer concentration range from 10 to 300 ppm. The solutions absorbance was determined on the basis of color reaction between the analyzed macromolecular compound, iodine, and boric acid. The absorbance was measured after 15 min using the UV–Vis Spectrometer  at a wavelength of 682 nm using the quartz cuvette. From the obtained results, the curves showing the dependence of absorbance on the PVA concentration were drawn up.Adsorption measurements were conducted by the static method at the three pH values 3, 6, and 9 (±0.1). The polymer concentration after the adsorption process was estimated by using the calibration curve. To seven Erlenmeyer flasks, a suitable amount of the PVA stock solution (at a concentration of 500 ppm), the background electrolyte , the samples were shaken in water bath  for 24 h. Then the sediments were centrifuged twice  and 5 cm3 of the polymer solution was taken for the analysis. All measurements were conducted at room temperature (≈25 °C) and in the presence of 0.01 mol/dm3 NaCl as a supporting electrolyte.In order to investigate the adsorption of PVA on the AST 50 surface, 0.025 g of fumed oxide was added into the Erlenmeyer flasks containing 10 cm3 of the supported electrolyte and 0.2 cm3 of 0.1 mol/dm3 HCl were placed into the thermostated Teflon vessel. The addition of acid provides the initial pH value in the range of 3–3.5. After reaching the equilibrium, 0.29 g of AST 50 was added. The obtained suspension was titrated by NaOH at a concentration of 0.1 mol/dm3. The potentiometric titrations in the presence of different molecular weights of PVA were conducted in the same way. The experiments were made in the polymer concentration range of 10–300 ppm. The surface charge density of the analyzed adsorbent in the absence and presence of PVA was calculated using the “Titr_v3” program written by W. Janusz.The potentiometric titrations were performed by using the following set of apparatus: Teflon vessel, thermostat RE 204 (Lauda), laboratory stirrer, glass and calomel electrodes (Beckman Instruments), pH meter (Radiometer), and automatic microburette Dosimat 765 (Metrohm) connected to the computer and printer. In order to obtain the mixed oxide potentiometric curves in the absence of the polymer, 50 cm3 beaker containing a suitable amount of NaCl. The suspension was further sonicated for 3 min and the initial pH equal to 3 was fixed. The zeta potential measurement was carried out up to pH 10 and it was controlled by the computer software. During the experiment the AST 50 suspension was stirred by using a magnetic stirrer incorporated into a device. The samples with PVA were prepared in the same way, the polymer concentration range was 10–300 ppm.The zeta potential measurements were made using Zetasizer Nano ZS  coupled with the computer and automatic titrator. To obtain a solid suspension in the background electrolyte solution, 0.003 g of AST 50 was added to a 50 cmExpert connected to the cooling module TLab Cooler and specialized computer software. This device possesses an electroluminescence diode which emits a collimated light beam (λ = 880 nm), passing through the studied suspension and two synchronized detectors. The transmission detector records the light passing through the sample at the angle of 0° in relation to the incident light direction. The second one is the backscattering detector, which registers the light scattered at the angle of 135°. The computer stores and processes the data.Stability measurements were conducted using a turbidimeter Turbiscan Labxi denotes the average backscattering for each minute of measurement, xBS is the average value of xi, and n is the number of scans.The obtained results are presented in the form of transmissions and backscattering curves as a function of time. Low transmission level and high backscattering are characteristic of samples with considerable stability. The analysis of the turbidimetric data allows for the assessment of processes dynamics occurring in the sample during the measurement. The significant distances between both the transmissions and backscatter lines denote that in the system fast particles sedimentation takes place and the investigated suspension is unstable. Overlapping of the lines indicates an increased durability of the system. Moreover, due to the specialized computer software connected with Turbiscan, it was possible to calculate the TSI parameter (Turbiscan Stability Index) that is very useful in the evaluation of colloidal system stability. The TSI coefficient is calculated from the following formula:The coefficient is in the range from 0 (for highly stable systems) to 100 (in the case of very unstable suspensions). Based on the transmission and backscattering data, it was possible to determine the stability parameters such as the diameters of formed aggregates  [μm] and the rate of particles  migration [μm/min]. These data were calculated using the programs TLab EXPERT 1.13 and Turbiscan Easy Soft. The measurement lasted 15 h, during which data were collected every 15 min.3 of the supporting electrolyte at a concentration of 0.01 mol/dm3, then the suspensions were sonicated for 3 min. The next step was adjusting the required pH value of the samples. The suspensions containing PVA 100 were prepared in an analogs way. The polymer at a concentration of 100 ppm was added to the solid suspension after the sonication process. In order to investigate the effects of solution pH on the mixed ternary oxide suspension stability, the measurements were performed at pH equal to 3, 6, and 9.The studies involve determination of changes of aqueous suspensions stability in the absence and presence of the polyvinyl alcohol with the molecular weight of 100,000 Da. The samples without the polymer were prepared by adding 0.002 g of AST 50–20 cm1 ≠ M2. Equations (pzc (pzc—point of zero charge). This value is specific for a given material and depends on the mineral oxide structure . In this situation, the polymer coils need more space on the solid surface blocking the access to the other adsorbent active centers available for the PVA chains present in the solution. This is a main reason for the polyvinyl alcohol adsorption amount reduction at pH 9.From the comparison of Figs. σ0 values (compared to the surface charge density without the macromolecular compound) is observed. It leads to the conclusion that the reason for such a behavior is the positive charge induction on the AST 50 particles on account of the interactions between the PVA macromolecules and the solid surface active groups. This effect does not occur distinctly at the lower pH values due to a small number of the ionized acetic groups located near the solid surface. As it was mentioned before, under these conditions the hydrogen bonds are responsible for the PVA macromolecules binding, but this type of interactions does not influence the surface charge density values changes. The increase in the polymer ionization degree (the resulting expanded conformation of the chains) contributes to a larger number of the electrostatic interactions between the negatively charged functional groups and the adsorbent surface.Analyzing the influence of the nonionic PVA on the potentiometric titration curves Figs.  and 6, iσ0 values can be drawn from the dependencies present in Fig. The potentiometric data obtained for the systems containing PVA at different concentrations (but the same molecular weight) point out that the polymer concentration has no essential impact on the AST 50 surface charge density. The curves are almost overlapping for both PVA 72 and PVA 100. This comes from the presence of an insignificantly larger number of the polymer functional groups with the concentration growth. Some conclusions on the influence of the PVA molecular weight on the AST 50 iep) is reached where the total charge of the diffusion layer is equal to zero. Changes in the zeta potential in the presence of polymer may be caused by three different effects:the presence of the charges coming from the polymer-dissociated functional groups in the by-surface layer of the solid,the shift of the slipping plane by the macromolecules adsorbed on the metal oxide surface,the displacement of the counter-ions in the Stern layer as a result of the polymer adsorption Vincent .In order to make a comprehensive electrokinetic analysis of the studied system, the AST 50 zeta potential measurements were carried out in the absence and presence of the nonionic polymer Figs.  and 9. IFrom the course of the AST 50 zeta dependencies obtained in the presence of both lower and higher molecular weight polymer, it was found that PVA attendance considerably influences on the mixed oxide electrokinetic properties changes. In the pH range 3–5.8, the AST 50 zeta potential is reduced, whereas above the pHiep point the PVA macromolecules adsorption leads to the marked zeta growth compared to the values achieved without the polymer. The most important reason for this phenomenon are changes in the polymer binding mechanism on the colloidal particles surface. In the pH range from 3 to 6, the number of negatively charged acetic groups present in the chains is small. As a result, under these conditions the polymer adsorption is driven by the hydrogen bond formation. In such a case on the mixed oxide surface, the compact adsorption layer of a small thickness is created (the polymer coils are closely packed). The adsorbed polymer chains shift the slipping plane and cause the zeta potential reduction. With the solution pH rise, the macromolecular compound adsorption decreases because of the repulsion forces occurring between the PVA macromolecules and the adsorbent surface. The polymeric chains extension on the solid surfaces promotes the formation of more stretched polymer coil structure rich in numerous loops and tails. This is related to the larger slipping plane shift by the adsorbed polymer macromolecules. Moreover, at pH above 6 on the AST 50 surface there are mainly present negatively charged groups which are associated with the supporting electrolyte cations. But the PVA chains undergoing the adsorption process interact with the positively charged adsorbent groups inducing the opposite counter-ions displacement toward the diffusion part of electrical double layer. As a consequence, the AST 50 particles zeta potential values growth is observed compared to the results obtained without the nonionic polymer. As in the case of the potentiometric data, there are no significant differences in the ζ potential values with the PVA concentration growth. The most important reason for this phenomenon is a very small increase in the number of functional groups in the PVA chains with the concentration increase.A lack of visible influence of the analyzed polymer molecular weight on the zeta potential can be explained by the mutual removal of the two effects: the slipping plane shifting and the counter-ions displacement from the Stern plane toward the diffusion layer. The lower molecular weight polymer on the AST 50 surface adopts a flat conformation, so the PVA 72 macromolecules can interact with numerous adsorbent surface active groups. This leads to the situation in which the number of counter-ions removed from the Stern plane increases. Simultaneously, the contribution of the slipping plane shifting by a less densely packed polymer adsorption layer is smaller. Due to the existence of a larger number of the acetic groups in the adsorbed PVA 100 chains, on the AST 50 particles surface the adsorption layer rich in loops and tails is formed. Under these conditions the polyvinyl alcohol segments interact with a smaller number of the adsorbent surface groups leading to the situation in which a number of the counter-ions displaced from the by-surface layer decreases. In addition, the formation of a thicker adsorption layer promotes greater slipping plane shifting. The total contribution of these two mentioned effects to the zeta potential value is approximately the same for both polymers. For this reason, there is no observed substantial influence of the studied polymer molecular weight on the electrokinetic potential changes.The transmission and backscatter curves obtained for the AST 50 suspension without the polymer recorded at three studied pH values  are shown in Figs. At pH 6, a slight decrease in the transmission compared to pH 3 is observed. However, the lines corresponding to each scan are still at a certain distance from each other. Nevertheless, the system becomes still relatively unstable. The main reason for such a behavior is the fact that under these conditions the solid isoelectric point is reached. This indicates that the mixed oxide particles diffusion layers are practically free from the electric charges, which facilitates their aggregation.A sharp decrease in the transmission level in the case of the mixed oxide suspension at pH 9 is related to significant durability of the studied system. The backscattering overlapping lines indicate the formation of a suspension characterized by high stability, which remains practically constant for the whole measurement time (TSI = 2.59). Such a behavior can be explained by numerous negative charges on the solid surface leading to effective particles repulsion.The addition of the nonionic polymer (PVA 100) has an essential influence on the AST 50 suspension stability Figs. . As follA significant increase in the system stability at pH 3 follows from the strong polymer adsorption at the solid–liquid interface. On account of a low PVA ionization degree, the closely packed adsorption layer containing strongly coiled PVA macromolecules is formed on the AST 50 surface. This polymer layer structure ensures the mixed oxide suspension steric stability (TSI = 7.47).The durability growth of the system containing PVA 100 at pH 6 (in relation to the suspension in the polymer absence) is a result of the acetic groups ionization and development of the chains adsorbed on the solid surface. Under these conditions the solid particles are also stabilized electrostatically due to the presence of numerous negatively charged functional groups in the PVA 100 macromolecules. This mechanism is described as electrosteric stabilization. The higher TSI value (rise by 10 units) as well as the larger size of the formed aggregates Table  indicateAt pH 9, PVA 100 does not influence on the mixed oxide suspension stability. Both the position of the transmission and backscatter curves as well as the TSI coefficient value obtained for the sample with the polymer are practically unchanged in comparison to the data in the PVA absence. Under these conditions the PVA adsorption is the lowest in comparison to strong polymer macromolecules ionization. The polymer chains structure development toward the bulk solution leads to appearance of the forces related to the steric effects, while the increase in the number of negative charges is connected with the presence of the electrostatic repulsion forces between the particles covered with PVA (electrosteric stabilization).V is the particles migration velocity [µm/min], ρc is the continuous phase density [kg/m3], ρp is the particle density [kg/m3], d is the particle mean diameter [µm], v is the continuous phase viscosity [cP], and φ is the volume of the dispersed solid fraction [%].Based on the transmission and backscattering data, it was possible to determine the stability parameters such as the diameters of formed aggregates  [μm] and the rate of particles  migration [μm/min]. These data were calculated using the programs TLab EXPERT 1.13 and Turbiscan Easy Soft. The particle velocity rate was calculated on the basis of the multiple light scattering theory. The particles diameter were determined using the general law of sedimentation, which is Stokes’ law extended to the concentrated dispersions  are presented in Tables As one can notice, in the AST 50 suspensions without the polymer the largest aggregates are formed at pH 6, while the smallest size is achieved in the alkaline solution pH. The reason for this situation is the forces responsible for the mutual attraction (pH 6) or repulsion (pH 3 and 9) of the suspended particles. The addition of PVA 100 causes the average aggregates size increase on account of the polymer macromolecules adsorption. In relation to the solution pH value, the interactions between the adsorption layers belonging to the different AST 50 particles change leading to the mixed oxide aggregates diameter changes. As in the case of the samples without the polymer addition, the largest flocs in the presence of PVA are formed at pH 6. This is most likely driven by the appearance of single polymer bridges formed due to the adsorption of one PVA macromolecule on two (or more) solid particles (as a result of the electrostatic interactions and the hydrogen bond formation). Smaller aggregates sizes were obtained for the colloidal suspensions containing polyvinyl alcohol at both pH 3 and 9. At the acidic solution pH, the suspensions exhibit steric stabilization, whereas under the alkaline conditions stabilization follows from the electrostatic effects. The average sedimentation velocities calculated for all studied systems depend on the aggregate size—the larger their diameter, the greater the rate of sedimentation.2O3–SiO2–TiO2 (AST 50) surface strictly depends on the solution pH values. The maximum of the PVA adsorption amount is reached at pH 3, whereas the fewest polymer macromolecules were bound to the adsorbent surface in the alkaline environment. The observed decrease in the amount of adsorbed PVA macromolecules with the solution pH growth is associated with the electrostatic repulsion forces present in the studied system. The more basic solutions, the more negatively charged AST 50 surface is. The increase in the ionization degree of the acetic groups belonging to the polymer chains leads to acquisition of more developed structure of the PVA macromolecules. The electrostatic repulsion forces occurring between the identically charged mixed oxide particles and the polymer chains are responsible for the reduction of the polyvinyl alcohol adsorption size.The nonionic polyvinyl alcohol adsorption on the mixed oxide Alpzc value of the mixed ternary oxide is equal to 4.7. Due to the nonionic character of the analyzed macromolecular compound, the polymer attendance has an insignificant effect on the AST 50 surface charge density. A slight growth of the adsorbent surface charge above pH 8 is related to the induction of positively charged surface groups. This process follows from the adsorption of the PVA macromolecules containing a small number of the ionized acetic groups (which are the source of negative charge). The polymer concentration increase does not cause considerable solid surface charge density changes. There are also no meaningful changes in the σ0 values as a function of PVA molecular weight—in the presence of PVA 100 the surface charge density of AST 50 assumes a smaller value compared to PVA 72. This is a consequence of a larger content of the acetic groups in the higher molecular weight polymer chains.The potentiometric data show that the pHζ potential increases. A decrease in the zeta potential is related to the slipping plane shifting by the adsorbed polymer chains. In turn, the zeta growth in the PVA presence results from displacement of the counter-ions from the Stern plane toward the diffusion layer. The total contribution of these two effects is responsible for the experimentally observed electrokinetic potential values of the solid particles covered with the polyvinyl alcohol macromolecules.Adsorption of PVA strongly affects the AST 50 particles zeta potential value. In the presence of the nonionic polymer, reduction of the electrokinetic potential values (in the range of solution pH from 3 to 6) is observed. The opposite situation takes place from pH 6–10 where the The stability measurements have shown that the AST 50 suspensions in the presence of the background electrolyte are the least stable at pH 3 and 6. In turn, in an alkaline medium the mixed oxide suspensions exhibit the highest durability. Low stability at pH 3 follows from the fact that on the AST 50 particles surface there is accumulated a small amount of positive charges. The electrostatic forces between the positively and negatively charged surface groups of different particles cause destabilization of the studied system. With the solution pH increase, the solid surface becomes negative. At pH 9, the number of the negatively charged groups present on the adsorbent particles surface is sufficient to provide the long-term stability of AST 50 suspension.The addition of PVA 100 significantly improves the suspension stability at both pH 3 and 6, at the higher pH value the polymer presence does not influence the system durability. In the acidic solution, colloidal particles are sterically stabilized due to the high adsorption of the strongly coiled polymer macromolecules. With the pH growth, the macromolecular compound chains adopt more developed conformation (due to the increase in the acetic groups ionization). This leads to the formation of the polymer adsorption layer capable of improving the electrosteric stabilization.


A fully automatic system has been developed that performs X-ray centring and characterization of, and data collection from, large numbers of cryocooled crystals without human intervention. Considerable effort is dedicated to evaluating macromolecular crystals at synchrotron sources, even for well established and robust systems. Much of this work is repetitive, and the time spent could be better invested in the interpretation of the results. In order to decrease the need for manual intervention in the most repetitive steps of structural biology projects, initial screening and data collection, a fully automatic system has been developed to mount, locate, centre to the optimal diffraction volume, characterize and, if possible, collect data from multiple cryocooled crystals. Using the capabilities of pixel-array detectors, the system is as fast as a human operator, taking an average of 6 min per sample depending on the sample size and the level of characterization required. Using a fast X-ray-based routine, samples are located and centred systematically at the position of highest diffraction signal and important parameters for sample characterization, such as flux, beam size and crystal volume, are automatically taken into account, ensuring the calculation of optimal data-collection strategies. The system is now in operation at the new ESRF beamline MASSIF-1 and has been used by both industrial and academic users for many different sample types, including crystals of less than 20 µm in the smallest dimension. To date, over 8000 samples have been evaluated on MASSIF-1 without any human intervention. Developments in crystal-characterization software . In combination with the ESRF MX beamline environment, which includes automatic beam delivery  running on a central computing cluster independent of the beamline. The beamline-control GUI MXCuBE2 . The beamline is equipped with a sample changer that also acts as a diffractometer (RoboDiff), a high-capacity dewar able to store 240 SPINE standard samples and a PILATUS3 2M detector. The beam diameter can be tailored to the crystal size using a series of apertures. The sphere of confusion (SOC) of the RoboDiff is less than 2 µm in diameter over 360°, allowing a minimum aperture of 10 µm to be used. The beamline will be fully described elsewhere .The automatic routine described here, and the modules such as X-ray centring, have been designed to run on any of the ESRF MX beamlines. However, it is only run as a dedicated service on the new highly automated beamline MASSIF-1. MASSIF-1 is a ESRF undulator beamline dedicated to the fully automatic characterization of and data collection from crystals of macromolecules. The beamline uses an artificial asymmetric Laue [110] diamond as a monochromator and a compound refractive lens (CRL) as the only focusing element. This simple optical setup produces a highly stable beam of 100 × 65 µm  with a typical flux of 2 × 102.2.Crystals vary widely in their morphology and can be mounted in a wide variety of supports. Optical centring will place the support at the beam position, but the crystal will not necessarily be in the beam over the full rotation range. It is therefore necessary to scan the loop through the X-ray beam in order to accurately locate small crystals and to define the optimum part of larger crystals. Initially, a fixed grid size was used. The fixed grid was sufficient to cover the largest loops available and also take into account that the loop might not be optimally centred. However, the fixed grid was substituted with an automatically determined grid for two main reasons: first, for small loops time is lost in scanning empty space, and second, for some types of sample holders and bent loops a fixed size was often too small.c and 2d) is performed. The contours of all 12 images are then analysed and the images corresponding to the minimum and maximum vertical sample support size are selected . The corresponding rotation angle at which the mesh scan should be carried out and the size of the grid are returned as results. A snapshot of the area is also taken to ensure that the loop was covered in the mesh .The automatic mesh algorithm can be used in two scenarios, where either the minimum or the maximum sample-mount size is determined as a function of rotation angle. The minimum grid size is used for sample holders containing a single crystal. The maximum grid size is used when locating more than one crystal in the sample holder in order to optimally detect all crystals. The input for the automatic determination of the mesh-scan area is a series of 12 images acquired from the online video microscope where the goniometer rotation axis is rotated by 30° between each image . The position of the best point is determined by first applying a threshold (50% of the maximum) to the signal and then performing a centre-of-mass calculation over any connected regions containing the strongest diffraction signal. The resulting position can be a fraction of the distance between the grid points. For crystals smaller than the vertical beam size it is necessary to perform a small vertical line scan to accurately position the sample as the two-dimensional mesh is not oversampled in the vertical direction. The decision to make this extra line scan is taken in cases where the best diffraction signal after the two-dimensional scan extends only one step in the vertical direction. If no signal is detected at this stage then the sample is unmounted.The two-dimensional mesh scan is performed over the area previously defined by analysis of the sample mount. The grid is divided into points, where the distances between points in the horizontal and vertical directions are defined by the beam size . For a well calibrated goniometer this allows the sample to be centred over 360° by determining the position 90° away from the mesh scan with a single line scan . However, in the case of MASSIF-1, the RoboDiff goniometer is both sample changer and goniometer. Therefore, the vertical position of the rotation axis must be determined and aligned to the X-ray beam, as it fluctuates between mounting cycles. This is determined using two scans of the goniometer with vertical translation 200 µm above and below the beam position performed 180° away from each other . The centre of mass is then taken for each scan and the midpoint between these values defines the vertical offset required to ensure that a crystal is fully centred. This calculation uses segmentation to define the object of interest and the threshold is set at 25% of the maximum in order to define the crystal edges rather than the optimum diffraction points. Once determined, the axis is moved to the beam position and a corresponding vertical displacement of the sample is performed in order to maintain the optimal diffraction volume centred to the beam. The sample is then rotated 90° and a vertical line scan is performed on the centring table 400 µm above and below the beam position. After the sample has then been moved to the position with the strongest diffraction signal, it is centred on the rotation axis in the beam over the full rotation range  as a function of the rotation angle ω. This provides a simple model of the crystal volume used as input for strategy calculation . After collection of data is completed, the workflow stops and the next sample is mounted.EDNA , data collection using an EDNA strategy , SAD-optimized data collection  or characterization of diffraction properties without data collection (MXScore). The resolution that has already been observed is used as the basis for the detector distance for the mesh and line scans where characterization images are collected and is also used as the resolution for data collections where an EDNA strategy is not requested or indexing fails. A required resolution can also be entered; this value is used as a threshold below which data collection is not performed. Finally, the experiment can be further tailored to the samples by defining a required multiplicity or completeness or by specifying a certain radiation-sensitivity or beam size. The ability to add specific information at the level of the sample leads to a highly flexible system without compromising automation.Essential entries in the diffraction plan are the sample acronym (the protein being studied that has been approved by the ESRF safety group) and a unique sample name. Together, these items determine the nomenclature for the directories and diffraction images and, 2.6.One of the most important aspects of an automated system is the correct handling of errors. Errors arising from robotic mounting of a sample are handled by low-level software which is designed to escape from the error and mount the next sample. Workflows are also paused when there is no beam (by monitoring the flux on a diode near the sample position) or the cryostream temperature rises above 120 K.Each workflow executed by the BES has an overall error monitor. This ensures that workflows end cleanly in cases of software errors and automatically send an error report to a pre-configured mailing list. In the case of software errors, the current sample is unmounted and the process moves on to the next sample.a). In order to avoid this scenario, the nearest image to the selected point is analysed and if the signal is less than 50% of the strongest position the process is repeated with the threshold increased by 5% until the selected position has the desired score . In cases where EDNA characterization fails to produce a strategy, because, for example, the images cannot be indexed or integrated or BEST fails to calculate the intensity distribution correctly, a default strategy is used . Where a SAD strategy has been requested and fails, the default strategy is for 360° with the same final absorbed dose. In this way, even poorly diffracting samples have an associated data set, which can often yield useful information.More specific error handling exists for particular steps in the process. For the automatic determination of the area of mesh scans, if the algorithm fails  a default grid of 400 × 200 µm is returned. Errors can also occur during the selection of the optimum diffraction volume. As the centre of mass is often between grid points, this can sometimes result in a poor position being selected Fig. 5a. In orre Fig. 5b. In ca2.7.N = 1240) on MASSIF-1. The entire process takes an average of just over 7 min, with the most common time being just over 6 min. The longest steps in the process are the initial mesh scan (average of 101 s) and data collection . The time taken for default data collection has a very tight distribution, reflecting only small changes in the flux owing to differing ring currents. Data collections based on EDNA strategy calculations are far more varied and often take much less time (most frequently 72 s as opposed to 113 s), but there are also many with much longer data-collection times, showing that longer exposure times can be used when crystal size permits , and this is also reflected in the distributions of individual dimensions, with peaks below 50 µm in all three dimensions , which is within two orders of magnitude of the theoretical limit of a 8.3 µm diameter required to obtain 2 Å resolution data from a ‘realistic’ lysozyme crystal . Analysis of this type should also prove useful for specific projects. Reports can be generated showing crystal size distribution and variation in diffraction quality (Bowler & Bowler, 2014The consistent measurement of all parameters related to not only each experiment but also to whole projects will allow information to be fed back into future experiments. For example, the analysis of the crystal dimensions the first 1240 crystals processed on MASSIF-1 in 2015 is informative Table 4. The aveet al., 2012Fully automatic characterization and data collection from large numbers of crystals is a new tool for structural biologists. The process is used heavily at opposing ends of the project spectrum. In the initial stages of projects, preliminary hits from high-throughput crystallization or large numbers of poor-quality crystals need to be screened, a time-intensive stage that results in little data collection but requires rapid feedback. The opposing use is the collection of hundreds of data sets from well established systems, as is the case in small-molecule fragment-screening projects. Again, this process is labour-intensive but requires more effort in the interpretation of results and again requires rapid feedback. The process described here facilitates both stages, allowing effort to be redirected from the beamline to the laboratory. In combination with recent developments in the automation of crystal mounting (Cipriani Click here for additional data file.10.1107/S1399004715011918/wa5095sup1.wmvSupplementary movie displaying the process to determine the mesh scan area.. DOI:


For a full understanding of the mechanical properties of a material, it is essential to understand the defect structures and associated properties and microhardness indentation is a technique that can aid this understanding.b| = 0.65 nm) and for the (101) plane as (101) b| = 1.084 nm) although (101) [010] (|b| = 1.105 nm) is possible. Tensile testing showed that at a stress value of 2.3 MPa primary twinning occurred and grew with increasing stress. When the stress was relaxed, the twins decreased in size, but did not disappear. The twinning shear strain was calculated to be 0.353 for the (101) twin plane.The Vickers hardness on (010), {011} and {110} faces lay in the range of 304–363 MPa. The Knoop Hardnesses on the same faces lay in the range 314–482 MPa. From etching of three indented surfaces, the preferred slip planes have been identified as (001) and (101). For a dislocation glide, the most likely configuration for dislocation movement on the (001) planes is (001) [100]  contains supplementary material, which is available to authorized users. To develop a firm understanding of the mechanical properties of any given material, it has been established that in addition to understanding the crystal structure, a knowledge of the defect structures and their properties is also essential ,2. To stIn the particular case of energetic materials, such studies are important as they can contribute to a more complete understanding of self-detonation and stability. It is now established -11 that 4H8N8O8) is the β-form ) or screw (∣b∣ = [010]) type. Better understanding of the contribution of phonons and lattice vibrations to detonation via elastic properties have been made experimentally  whereas in the second it was positioned at 45° to this direction. The resulting VHNs when using a load of 15 g are presented in Table 00 whereaThe load dependence of Vickers hardness was determined using loads in the range 15-35 g. The orientation of the indenter was maintained constant for all of the measurements corresponding to a position with one diagonal parallel to the [100] direction. The subsequent plot of VHN against load Figure  shows thThe effect of temperature on hardness was assessed by indentation at various temperatures in the range 293-378 K using a specially designed heating stage mounted on the hardness tester. A load of 15 g was employed and an orientation as above with a diagonal parallel to the [100] direction. The results presented in Figure The Knoop hardness number (KHN) was determined as a function of indenter orientation only. Indentations were made with the long diagonal aligned at successive 10° intervals in the range 0-180° with respect to the [100] direction using a 15 g load. The results are summarized in Figure On the {011} habit face, Vickers indentations were made at two different orientations, one with a diagonal parallel to [100] and one with a diagonal at 45° to this direction, using a load of 15 g. Hardness measurements are presented in Table The Knoop hardness number was measured as a function of orientation by indenting parallel to a number of important crystallographic directions using the 15 g load. These Knoop hardness results are given in Table The Vickers indentations were performed with an indenter diagonal oriented parallel and at 45° to the 001] direction and a load of 15 g are also shown in Table  directioThe variation of VHN with load was again determined only in the limited range of 15-35 g with an indenter diagonal parallel to [001] Figure . Within The KHN was determined at various significant crystallographic orientations using a 15 g load. The hardness results are again presented in Table Following indentation on the three major habit faces of β-HMX, the samples were etched in order to examine the extent of plastic deformation associated with the indentations. The active slip systems were determined and the densities and configurations of the dislocations within the slip plane assessed. In order to resolve individual etch pits associated with the indentations, it was necessary to reduce the etching time previously defined to approximately one second.b = [010]. The apex of each pit is coincident with the geometric centre, indicating that the dislocation lines are normal to the crystal surface. This defines the slip planes as (001) and (101). Therefore, the greater density of etch pits at the sites of indentation arises from stress induced dislocation loops which are contained in the (001) and (101) slip planes and emerge normal to the (010) face.Figure The distribution of etch pits found around Vickers and Knoop indentations on {011} faces is shown in Figure Etched Knoop indentations on the {110} surface obtained using the 25 g load are shown in Figure b|2, the most likely configuration for dislocation movement on the (001) planes is (001) [100] (|b| = 0.65 nm). Similar arguments lead to the most likely configuration on the (101) plane as (101) b| = 1.084 nm) although (101) [010] (|b| = 1.105) appears a distinct possibility.From the geometry and alignment of the pits on the three indented and etched surfaces, the preferred slip planes have been identified as (001) and (101). For a dislocation to move by glide, the Burgers vector must be parallel to the slip direction. A list of lattice planes and interplanar spacings is given in Table As the emergent dislocations in both slip systems on a (010) face are normal to the crystal surface, and both the [100] and The nature of the deformation process associated with Knoop indentations of β-HMX is complex and involves slip, twinning and to a minor extent brittle fracture, often occurring simultaneously. Despite this, an attempt has been made to explain the hardness anisotropy and give support to the conclusions drawn from etching studies regarding the operative slip systems by using the simple theoretical model developed by Daniels and Dunn  and the As mentioned earlier, HMX has no strong KHN anisotropy with change of crystal orientation on any of the surfaces studied, so in this case a full analysis was only carried out on the (010) face, where the slip systems are assumed to be (101) In Figure The lack of relatively large hardness anisotropy for the (010) face is reflected in the ERSS curves if it assumed, as the theory demands, that only the system experiencing the maximum ERSS is activated at a given orientation. The observations of dislocation etching point to which systems are operative at any given orientation and clearly confirm this premise. A maximum in the ERSS requires a minimum in the hardness and so bearing in mind the potential complexity of the processes involved, there would appear to be an acceptable correlation between the experimental curve and the ERSS values. It is unlikely that much can be deduced from the positions of minor minima and maxima in these plots.Twinning, although of apparently secondary importance, presents something of an anomaly. The results of Palmer and Field  suggest As for indentation on the {011} face, the experimental hardness shows very little variation with orientation in contrast to the ERSS values calculated for the (101) On the {110} face, the hardness results again show little variation with orientation, although there are significant changes in the ERSS values. The trend is again for a higher KHN where the ERSS values are low. However, only one type of slip trace is observed over the whole range; that associated with the direction of interception of the (101) plane. This is in spite of the fact that ERSS values calculated for the (001) [100] system are significantly higher than those for the (101) 1) at a stress value of 2.3 MPa. This is the primary twin and was observed to grow with increasing stress. At 2.6 MPa a secondary twin system was initiated, intersecting the original twin at two positions (S1 and S2). At this point the stress was relaxed to zero and the optical micrograph shown in Figure 3 a third secondary twin is nucleated and a portion of the primary twin is seen to separate and propagate away from the remainder of the twin. Figure 2) is nucleated and grows under constant stress ; K2(001); n12[100]. The twinning shear strain was calculated to be 0.353 for the (101) twin plane. By observation of the twins on two {011} faces, the twinning planes were found to be (101) for the twins labelled T and 1/n. By changing the orientation of the indenter with respect to the deformation system, either by indenting on different faces or at various surface orientations, the response of the solid will be altered resulting in variations in hardness. This is clearly illustrated by the difference in the nature of the deformation patterns obtained at various indentations.Examination of the regions surrounding unetched and etched Vickers hardness impressions shows that deformation occurs by an extremely complex mechanism involving dislocation slip, twinning and fracture. In order to explain from theory the hardness phenomena associated with Vickers indentation in this material, all of these factors require to be considered. Although a relatively simple model based on the ERSS for Vickers indentation is available  it is unThe strain fields produced around pyramidal indentations are geometrically similar irrespective of their size. Thus at heavier loads, the dimensions of the hardness impressions and their associated deformation features are proportionately larger, but otherwise identical. Consequently, the observed independence of Vickers hardness with load is as expected.The decrease in hardness on the (010) face observed with increasing temperature can be attributed to a gradual reduction in the intermolecular bonding due to the thermal expansion of the lattice.Dislocation slip is considered to be more important, and possibly the dominant mode of deformation during Knoop indentation. This is evident from several observations. First, there is a considerable reduction in the incidence of cracking compared with that associated with Vickers indentations. Second, dislocation slip as measured by the etch pit arrays surrounding the indentation impressions, is shown to be much more extensive when the Knoop indenter is used, in comparison with the Vickers. Both these factors result from the blunter geometry of the Knoop indenter which serves to inhibit brittle fracture and promote plastic deformation. Third, in the majority of Knoop indentations the degree of twinning is also reduced, except perhaps in the case of indentations on the {110} face.According to the models of Daniels and Dunn, the observed anisotropy in Knoop hardness is determined mainly by the orientation of the indenter with respect to the operative slip systems, taking into account that the comparison between Knoop hardness and ERSS value must be limited to those orientations at which the same slip system is active on a given face. A reasonable correlation exists for the (010) face, particularly considering that relatively minor variations in the ERSS are involved. Support is also given to the model and the slip systems believed to participate in the deformation process from the microscopic observation of etch pit patterns. Although the data is more limited for the {011} face, again it appears that directions which correspond to the lowest values of the ERSS are hardest and those with the highest ERSS are softest. Twinning has been considered as a potentially important mechanism, but microscopic examination reveals that the process is not particularly extensive, and that on the (010) face there is no correlation between the appearance of twin lamellae and the ERSS, except perhaps for alignment along the 1; (101), n1; 2; (100), n2; [001] with a twinning shear of 0.353. Since K1, K2, n1 and n2 are all rational, such a twin is a compound twin. Observations on the (010) and {110} faces would appear to confirm this view regarding the nature of the mechanical twin. The observations made in reflected light around indentations on the {110} face reveal steps at the surface due to twin lamellae, whereas no such steps are observed on the (010) face where twins can be viewed under transmitted light. These observations are totally in keeping with a twinning direction of Palmer and Field  have staThe Peierls-Nabarro model  predicts011 = 0.6021 nm and d020 = 0.5525 nm) shows that they are amongst the three most widely separated planes and are therefore likely to be held by weak interactions and parted easily. The cleavage cracks are probably simple tensile cracks. There is no evidence to suggest that they are formed as a consequence of dislocation interactions.Since cleavage involves a splitting of the crystal along specific planes, it will preferentially take place parallel to those planes which are weakly bonded. Calculation of the values for the interplanar spacing for the (011) and (020) planes  is identical to that observed previously. The twin nucleated at slightly higher stress (secondary twin) is strained elastically along the same direction in which fracture was found to occur. As before, increasing the stress resulted in the growth of both twins and the nucleation of other new twins. When a twin was nucleated at a certain stress value, which was held constant, the twin continued to grow. This means that the stress required for nucleation is greater than that needed for growth. Repeated nucleation and relaxation of the twin resulted in the re-introduction of the twin at successively lower stress values and eventually resulted in one of the twins becoming permanent and finally crystal fracture.The highly brittle nature of β-HMX is illustrated by the fact that the primary twin is nucleated instantaneously in a thin lamellae which runs throughout the crystal, with boundaries at the crystal surface. A more plastic material would produce twins embedded in a crystal matrix, with the lattice distortion accommodated by slip. The secondary twin consists of a thin lamellae contained in the crystal bulk, intersecting the surface on two {011} faces. In this case, the lattice distortion is taken up by elastic strain. At higher loads, in the absence of slip, the lattice will be unable to accommodate the greater distortion and fracture will result. Although observed by Palmer et al. , this waRepeated twin nucleation always occurred in the same position, suggesting that some lattice defect acts as a site for twin nucleation. Whether due to an impurity acting as a stress concentrator or a dislocation, as in a pole dislocation twinning mechanism, is unknown. Observations of the present type, looking solely at the twin morphology provides only limited information. More detailed investigations of the processes taking place in the bulk of the crystal are ideally suited to X-ray topography (XRT) as has been demonstrated for PETN. Tensile testing in conjunction with XRT will yield information on the defects active in twinning . SynchroUse of the tensile stage with XRT will be extended to other materials to study the role of defects and, in particular, dislocations during plastic deformation in a similar manner.Microhardness indentation techniques have been used to investigate the mechanical deformation behaviour at the three predominant habit faces of β-HMX. Both Vickers and Knoop indenters have been used to determine the variations in hardness with orientation, load and temperature. Indentations were etched to identify the active slip systems and the measured hardness was compared with the hardness predicted from ERSS calculations. The Vickers hardness number (VHN) was found to vary in the range of 304–363 MPa depending on crystal face and surface orientation, however the VHN was found to be independent of load (in the range 15 g-35 g), thus implying that the deformation mechanism remains constant under these conditions. With varying temperature, the VHN decreased as would be expected due to thermal expansion, of the lattice up to a temperature of 378 K, indicating that there was no change in deformation mechanism up to this point. A greater anisotropy was found for the KHN, varying between 314–482 MPa depending on crystal face and orientation.The two slip planes revealed from indentation etching studies are (001) and (101) and the most likely Burgers vectors for dislocation glide on these planes as deduced from simple structural considerations are [100] and In comparison to other secondary explosives, HMX is considered to be brittle and the hardness numbers can be compared to other organic solids Table  and the


Localization is an apparent aspect of a wireless sensor network, which is the focus of much interesting research. One of the severe conditions that needs to be taken into consideration is localizing a mobile target through a dispersed sensor network in the presence of physical barrier attacks. These attacks confuse the localization process and cause location estimation errors. Range-based methods, like the received signal strength indication (RSSI), face the major influence of this kind of attack. This paper proposes a solution based on a combination of multi-frequency multi-power localization (C-MFMPL) and step function multi-frequency multi-power localization (SF-MFMPL), including the fingerprint matching technique and lateration, to provide a robust and accurate localization technique. In addition, this paper proposes a grid coloring algorithm to detect the signal hole map in the network, which refers to the attack-prone regions, in order to carry out corrective actions. The simulation results show the enhancement and robustness of RSS localization performance in the face of log normal shadow fading effects, besides the presence of physical barrier attacks, through detecting, filtering and eliminating the effect of these attacks. The increasing prevalence of wireless networks has not only increased the possibilities of information integration into applications, but has also influenced our interactions with others, in study and work. A typical example of such an information source is location information, which is of great importance in many applications. The term localization is the ability to determine the physical position of a static or mobile sensor node or wireless device. The use of a wireless device for tracking locations is a growing trend that holds great prospects for a wide range of applications, particularly for those employed in physical security, asset tracking, geographic routing and workflow management :(Equation .(8)Xσ∼NThe transmission power arameter . Furtherarameter .(9)α∝f. Obviously, the online RSSI measurements expend computation and communication resources. Nonetheless, in practical environments, the signal propagation model is quite difficult to determine . If the Moreover, the RSS readings (fingerprints) are created at different locations according to the random sensor node deployment;  sj) with  refers to the location j where the RSS reading has been gathered as in Equation .(10)Sj=This example indicates the RSS reading for n landmark and lq dimension. At each landmark, the frequency and power level combination has been defined as a dimension. We take note that certain localization schemes need preparation information (training data) to create signal maps at the offline phase, which is utilized later for the online localization process. We utilize the fingerprints gathered from the random sensor node deployment to frame the training data. In view of the propagation model of the path loss, the RSS measurements’ probability density function gathered from  landmarks autonomously can be composed as shown in Equation .(11)f  has beenThe improvement of localization accuracy has been proposed in  through A scheme to ensure secure localization in the presence of cheating beacon nodes has been proposed in . This meA proposed approach concerned with a secure location verification in  has beenWe propose a solution based on multi-frequency multi-power transmission through using two techniques: a step function multi-frequency multi-power localization (SF-MFMPL) and a combination of multi-frequency multi-power localization (C-MFMPL). In order to mitigate the influence of shadow fading, the averaging of all RSS over all of the transmitted frequencies will be performed. In our solution, the anchor or access point (AP) nodes will transmit signals in multi-frequencies at multiple power levels. Sensor nodes receive these signals and construct the average RSS fingerprint from them. All of the sensors are already coded with the knowledge of the expected frequencies and the power level from the AP. By observing the RSS fingerprint that is expected, the sensor will be able to find if there is a barrier attenuating or degrading the signal. The sensor will reject the RSS fingerprints that it doubts as attenuation or degradation. Leaving those corrupted RSS fingerprints, the sensor nodes will choose the remaining RSS fingerprints from the uncorrupted RSS and utilize lateration with non-linear least squares to get the location of the sensor node.The sensor and the AP are strictly time synchronized. This can be done by running a quartz crystal clock in all of the sensors and the AP. With the help of the quartz crystal, all of them are time synchronized. Quartz crystal-based time synchronization is easy to implement, and it is inexpensive. More information about other time synchronization methods in wireless sensor networks can be found in ,31.In order to avoid the estimation being affected by interference and reflection, we propose a technique to filter those signals by estimating the approximate angle of arrival of the signal. In addition, we also propose a method to get the estimation of the attack area in the network. By estimating the attack area and alerting the network administrator, those barriers can be removed and the localization accuracy improved.a priori by the sensor. We vary the frequency by three levels, 400 MHz, 600 MHz and 800 MHz, as well as synchronously vary the power level by three levels, 4 dB, 6 dB and 8 dB. After averaging each received power level that has been transmitted by the three frequency levels, three dimensions of the RSS between the sensor and each landmark result. The use of three levels of frequency and power transmission will be suitable enough to accomplish an accurate performance without any extra computational cost and complexity, as well as reducing the fingerprint database memory size.Location is achieved by utilizing a multi-frequency multi-power transmission. The knowledge of the frequency and power level from the expected antenna is known The receiver at the sensor node will try to synchronize and check the expected power level and frequency to check if there is any signal degradation or attenuation in the path of the signal. The lateration technique is applied on the position of the sensor and the distance measurement to get the location of the sensors. In addition, based on the signal irregularities at all sensors, the map of the region of attack is found. By cooperating with all sensors, the sensor is able to identify the area of the physical barrier attack.The functional block diagram of our previous work (SF-MFMPL) can be found in . Figure The fingerprint of the proposed solution consists of two phases, offline and online. In the offline phase, the beacon signal will be checked first through various stages until the final stage, which creates the RSS fingerprint for each landmark. The sensor device is assumed to have knowledge about the AP’s position and the approximate angle of the AP’s location. Knowing the approximate angle range in which the AP is located helps to avoid any faulty measurements due to signal interference or reflection by the barriers. Since sensor nodes are placed randomly in the network, the angle of arrival of the signal from the AP is found by sensor tuning in each direction till the maximal power is received. This process has to be done for each AP at the initial deployment time. The threshold is the amount of tolerance between that estimated and the actual one. It can be measured by placing a small obstacle for signal degradation at different points in the network and measuring the error. The size of the obstacle will be the maximum obstacle size that our network can tolerate. This is done to accommodate certain infrastructure provisions made by the administrator of the network. The stage of averaging each received power level that has been transmitted by the three frequency levels is very important to overcome the effect of the log-normal shadow fading effects. Finally, the last stage in the offline phase is the cooperation of the sensor nodes to create the RSS fingerprint database of the whole network, while in the online phase, the sensor node receives the beacon signal, then averages the RSS and matches it to the fingerprint database, besides using the lateration and trilateration techniques to pin-point the sensor node position.1. Offline Phase: The fingerprints are produced and stored in a database during this phase containing the average RSS readings of the anchor nodes’ positions. Actually, the location of the anchor nodes ought not to change during the online phase. The representation of the fingerprint database for the (C-MFMPL) solution can be utilized by a set of RSS with the coordinates of the anchor nodes gathered by each sensor in the network, as shown in Equation .(14)F=fThe average RSS all over the transmitted frequencies that are stored in the fingerprint database can be defined in Equations  and 16)16), respMoreover, Equation  shows thThe While in the (SF-MFMPL) solution, each anchor (AP) sends beacons according to a step function, each decided power level will be sent with a decided frequency . In ordeThe value of (N = 3) will be taken for this solution to maintain the same system model conditions that are used for the (C-MFMPL) technique.2. Online Phase: During the online phase, the sensor nodes receive the transmitted beacons from the anchors with the same combinations of the frequency and power levels. Therefore, another measurement set of the RSS, called quations  and 20)Sonline, Eventually, the online phase depends on the RSS fingerprints generated by the offline phase to find the best matching RSS values for the estimation of the sensor node location. The RSS fingerprint machine learning database plays the most important key role in the proposed algorithm, first through filtering the physical barrier attack effects with the help of the proposed grid coloring algorithm, which depends on the angle of arrivals of the beacon signals received from each anchor (AP), and, second, participating in the process of mitigating the harsh shadow fading environment.As an aspect of sensor network communication and sensing subsystems, various sensor designs have been supported by the angle of arrival (AOA) mechanism, such as tracking and other applications, which accord improved monitoring in the orientation of the target by means of beamforming . SensorsA multiple beam pattern of the directional antenna array can be used to determine the received signal strength RSS ratio. This solution establishes a low complexity and cost for the sensor network design. Each anchor (AP) is assigned a combination of the frequency and power level transmission. Before starting localization, each sensor node tunes in to the approximate angle in which an AP is located to capture the signal and measure the RSS. By tuning in to the approximate angle, we are able to filter out the RSS measurement errors due to reflection and interference signals from other directions. Each sensor calculates the estimated signal frequency and power according to the defined frequency and power level combination. Depending on Equations  and 22)22), the If the deviation in the error within the approximate angle is greater than the threshold, the RSS value from that AP must be rejected, as it is an indication of signal attenuation or degradation by the attacker; the deviation in error can be calculated from Equations  and 24)24), respDf and Dp must be less than threshold T for the RSS value in order to be accepted. Every sensor must measure the average RSS value all over the transmitted frequencies for the same anchor (AP). The best value of the RSS with the lower values of Df and Dp must be taken. The AP’s location  together with its measured average RSS value constitutes the RSS fingerprint for that AP. Each sensor node must calculate the average RSS fingerprint for all of the APs whose Df and Dp is less than the threshold T.In this paper, The RSS error between the original measurements and the reconstructed measurements at a certain threshold, which presents the least amount of error for an individual network to be localized, is identified by the RSS error threshold value. Intuitively, as the discard threshold increases, the error increases , for which the RSS error from each anchor (AP) increasing by more than 15% of the nominal RSS will be neglected, while RSS error less than 15% will be accepted. In order to select an appropriate RSS threshold, the use of an exhaustive search based on computer simulations is mandatory, due to the complexity involved in the problem at hand. Meanwhile, finding the optimum threshold value is difficult and needs extensive training for the simulation procedure. Hence, based on the relevant empirical and experimental studies related to the threshold value selection, we consider the RSS error up to 15% of the nominal RSS as the acceptable approximate threshold.Localization involves two stages: ranging and lateration. Ranging estimates the distance d from the position of the sensor node to the anchor (AP). The RSS can be expressed according to Equation .(25)RSSα is the path loss component.Therefore, the distance can be determined from Equation  as folloLateration gets the position of the sensor node from the distance measurement for different anchors (AP) and their locations. There are two popular methods to get the location estimate: non-linear least squares (NLS) and linear least squares (LLS). In NLS, from the estimated distance di and known positions Li =  of the landmarks, the position  of the target device can be estimated by finding Equation .(27)P^=Moreover, for the localization based on a trilateration technique, the anchor nodes’ coordinates defined by the i-th beacon Bi are ; the coordinates of the unknown sensor node are ; RSSIi refers to the signal strength measurements between the sensor node and each anchor node; and di is the distance estimation among the sensor node and the i-th beacon node Bi. Assume three beacons  with three distance estimations , then the sensor node coordinates can be obtained as follows :(32)2=d^32x^y^=2, the grids in the direction from the AP to the sensor node along the direction of the signal are colored gray. This process is performed for all sensors for all of the accepted AP. Once the process is complete, all of the grids still in white are the signal holes.The algorithm code is given below:Algorithm 1 Signal hole detection.Require:for i=1: N do  for j=1: N do          GM[i][j] = Whiteend for    end for  for i=1: NoofSensor do  for j=1:NoofAP do    ifthen      for X=1: N do        for Y=1: N do          ifthen                          endif            end for          end for        end if      end for    end for  Error in the estimated attack area is calculated by Equation .(39)E=nError in the estimated attack area is calculated by Equation .(40)E=nWe simulate the proposed solution by using MATLAB. The simulation area consists of a 1000 × 1000 m two-dimensional terrain. The optimal number and placement of the anchor (AP) is important. We assume a small, but reasonable beacon node population of 20 beacon nodes (approximately five beacons in each direction), which are scattered uniformly over the 1000 m × 1000 m areas. We place a maximum of 10 barriers of a length of 1 m and a width of 1 m randomly in the network. The barriers can cause signal strength degradation and have a severe effect on the RSS. This section presents the performance evaluation of the localization accuracy for both of the proposed solutions SF-MFMPL and C-MFMPL in the face of the log-normal shadow fading environment. It is essential for an examination to assess all outline and advancement works with respect to the research goals. In this way, the performance evaluation is given with respect to this issue. The performance evaluation is performed according to two main parts: first, the localization accuracy, which is defined by the average localization error for different parameters of the network system model, as well as different shadow fading propagation models, and the second part is the detection efficiency of the physical signal strength attacks, which is performed according to the detected area of attacks error.The average localization error has been achieved according to two cases:α = 3, standard deviation of the log-normal shadow fading σ = 5 dB;Case I: path loss exponent α = 4, standard deviation of the log-normal shadow fading σ = 6 dB.Case II: path loss exponent The average localization error is calculated between the actual and the estimated locations for all sensor nodes. The performance is measured in terms of average localization error by varying the number of barriers in the network for different numbers of APs and for both propagation system model cases. It is surely understood that expanding the density of anchor nodes accordingly improves localization. However, increasing the number of anchor nodes may not be a feasible solution due to the extra hardware requirements, which may be more expensive.The simulation results in Moreover, From the simulation results, we observe that the number of anchors (APs) is a significant factor and plays a key role affecting the localization accuracy. Hence, in our approach, the increasing of the number of the anchors (APs) for the system model will lead to reducing the localization error very rapidly, because the number of variables used in non-linear regression increases, as well. On the other hand, utilizing the trilateration technique enhances the performance of the localization clearly, as compared to the performance based on lateration.A comparison of the average localization error for both the (SF-MFMPL) and (C-MFMPL) algorithms according to 20 anchors (APs) and two propagation model cases is shown in α and σ are clearly subject to and depend on the transmission frequency and power level. In (SF-MFMPL), each power level is indicated by a specific transmitted frequency, while in (C-MFMPL), a combination of multiple frequencies and power levels has been indicated. Hence, the difference in the two algorithms’ strategies will lead to different results in the face of the impact of shadow fading. In addition, simulation results of the trilateration technique showed the best performance for both proposed solutions and under various simulation conditions. With regards to the simulation results above, we observe that for the localization accuracy identified by the average localization error for both proposed solutions, C-MFMPL is better than the proposed solution based on SF-MFMPL in both cases of the shadow fading propagation model, as well as the lateration and trilateration techniques. This is due to different strategies of using multi-frequency and multi-power for the two algorithms. Meanwhile, the propagation parameters α) on the average localization error with respect to different values of the log-normal shadow fading standard deviation (σ) has been taken into consideration of the simulation analysis for both proposed algorithms, (SF-MFMPL) and (C-MFMPL), as shown in The effect of changing the path loss exponent  has a better performance than the (SF-MFMPL) solution in both cases of lateration and trilateration. The other cause of localization error arises due to the anchor node’s position. We considered the effect of the anchor node’s positions on localization accuracy. Hence, the anchor node’s placement and geometry have a crucial role to play with respect to localization performance. The average localization error for the random anchor placement configuration is measured for both the (SF-MFMPL) and (C-MFMPL) algorithms, respectively, as illustrated in According to the simulation results above, it is clearly observed that the localization accuracy in the case of the random deployment of the anchor nodes (APs) drops slightly with respect to the increase of the number of barriers in the network. However, the average localization error for this case is still within the acceptable error range regarding the RSSI-based localization technique.Proper positioning of anchor nodes is necessary for the effective localization accuracy of wireless sensor networks. Since the performance of the localization accuracy depends on the placement of the anchors (APs) in the network. The performance analysis in such an environment has been compared among various anchor (AP) deployment mechanisms; In accordance with the simulation results above, we have indicated the impact of the anchor node deployment, and it is clearly affected by the localization performance. A comparison of the performance evaluation for both proposed solutions, SF-MFMPL and C-MFMPL, under the two cases of the anchor node (AP) deployment mechanisms has been summarized in According to the simulation results stated in Another important metric that indicates the effect of the barrier attack on the localization performance is the percentage of the invalid beacon drop ration due to the barrier attenuation of the signal with respect of the total percentage of available beacons for the proposed system, as shown in We observe from the figure above that as the number of barriers increases, a greater number of invalid beacons will appear in the network, and drop ratio is increased, as well. In spite of the 40% invalid beacon drop rate from the case of 10 barriers, the performance of our proposed approach is still accurate and contributes to a lesser localization error.One of the most significant performance evaluation criteria is the accuracy of the detected area of attack. This accuracy is measured in terms of the difference between the actual area occupied by the barrier and the detected area of the signal holes. We varied the number of anchors (APs), as well as the deployment configuration in order to determine the optimum placement positions for the detection mechanism. The accuracy of the detected area of attack is illustrated in Based on the simulation results above, we observe that as the number of anchors (APs) increases, the detected area error reduces greatly, as well as the optimum placement configuration of anchors (APs) around the perimeter showed more accurate performance through identifying the attack area with great precision.The accuracy of detecting the area of attack also depends on the shape and dimension of the barrier attack that is introduced into the network. We introduced a rectangular barrier of various dimensions and measured the accuracy of the detected area of attack, as shown in In accordance with the simulation results above, initially, when the barrier size increases, the error in the detected area of attack is decreased gradually with respect to the increasing of the anchors (APs); this is due to more beacon signals being affected by these barriers with more RSS error deviations having been neglected, so that there is more information available for detecting the area of attack. On the other hand, increasing the barrier size will lead to increasing the localization error slightly.This paper presents the robust range-based RSSI localization algorithm in the presence of barrier attacks and under the log-normal shadow fading model. These attacks affect the localization process, making it erroneous. The proposed algorithm has been achieved by two techniques of multiple frequencies and power levels identified by (SF-MFMPL) and (C-MFMPL), respectively, with averaging the received power levels all over the transmitted frequencies in order to mitigate the shadowing effects in the wireless channel propagation; besides, our solution includes lateration- and trilateration-based techniques and fingerprint matching, which were demonstrated to be efficient and accurate localization techniques for wireless sensor networks. Furthermore, in this paper, the detection and identification of the attack area have been achieved through applying a grid coloring algorithm with the aid of the approximate angle of arrival estimation of the received signal from each anchor (AP). Moreover, a suitable choice of the threshold error value between the actual and the estimated received signal strength was made. By identifying the attack area, the network operators take action to clear this attack and improve the localization accuracy. Moreover, the important key role of this paper is the trade-off between the localization enhancement and the utilization of a minimum number of anchor nodes with the best deployment. Through simulation results, we have proven the effectiveness of our approach. Future work directions for a real implementation and experiment design will be taken into consideration for testing the proposed algorithms.