The primary objective of this research is to evaluate the feasibility of using ultrasonic acoustic imaging as a
non-intrusive, in situ technique to assess the plastic behavior of commercial metals and alloys. Specifically,
it aims to explore the potential of ultrasonic acoustic imaging to identify and monitor various plastic
deformation mechanisms in stainless steel and aluminum. The selection of materials is based on their distinct
plastic deformation behaviors: aluminum releases internal energy through dislocation mechanisms, while
stainless steel releases energy through deformation, first by dislocation and then by twinning. To achieve
this goal, the study will continuously measure changes in sound velocity and the nonlinear acoustic parameter
β while subjecting the materials to uniaxial tensile tests at different levels of applied stress.
Previous studies conducted by our research group have demonstrated that changes in sound velocity, in
relation to strain, offer a reliable means of quantifying dislocation density in local measurements on
aluminum, copper, and stainless steel specimens. Furthermore, these studies have observed that alterations
in the nonlinear acoustic parameter, specifically second harmonic generation, exhibit more pronounced
changes compared to variations in linear acoustics (speed of sound). Building upon these findings, the
proposed research involves the generation of both linear and nonlinear acoustic images over wider spatial
regions to advance our understanding of the plastic behavior of materials undergoing different
microstructural changes.
The challenge of applying the results of this research to in situ measurements in the industry is not trivial,
as the highly controlled laboratory conditions are not maintained in service components. In this regard, the
incorporation of machine learning tools in the proposal aims to identify the parameters most sensitive to the
various deformation mechanisms through clustering techniques. It is expected that the correlation of different
acoustic parameters with the various plastic deformation mechanisms of both materials under study will
generate an optimal database that reflects the variety of scenarios present in service components, thus
paving the way for the industrial use of the proposed characterization system.
The adoption of diagnostic techniques and the utilization of metallic material state analysis in service
significantly enhance our ability to comprehend and control plastic deformation mechanisms, contributing to
improved material reliability and robustness, and facilitating informed decision-making and maintenance
strategies.
Additionally, ex-situ standard microstructural tests, including XRD (X-ray diffraction), EBSD (electron
backscatter diffraction), and TEM (transmission electron microscopy), will be performed to characterize the
material’s state after deformation. These complementary tests will provide valuable microstructural
information, enabling the correlation of deformation states with the acquired acoustic images.
All the acoustic and microstructural information described above, in conjunction with previous research group
data, will be stored in a robust and comprehensive database. This database will serve as the input for a
Machine Learning algorithm, which will facilitate the identification of patterns of correspondence between
acoustic and microstructural parameters. This approach aims to enable the future prediction, with a high
level of probability, of the specific type of plastic deformation mechanism that a material is undergoing based
on the acoustic parameter measurements.
The successful development of this research proposal would yield several significant outcomes. Firstly, it
would enable the early detection of microstructural changes in materials long before fractures occur.
Moreover, it would establish a non-intrusive tool for characterizing materials by identifying the underlying
mechanisms driving plastic deformation and monitoring the evolution of materials in service over time.
Ultimately, this research has the potential to advance our understanding of the plastic behavior of stainless
steel and aluminum, opening avenues for improved analysis, design, and performance evaluation of materials
in various industrial applications.

Plants, with their two-layered immune system, are equipped to combat pathogen invasion. The first layer, Pattern Triggered Immunity (PTI), is a powerful defense mechanism. It relies on Pattern Recognition Receptors (PRRs) to detect Microbe-Associated Molecular Patterns (MAMPs) from microbes, triggering a robust defense response. This response, including signaling cascades, gene expression changes, and production of antimicrobials and defense hormones, contributes to restricting pathogen colonization. PTI activation can trigger a systemic response known as Induced Systemic Resistance (IRS), enhancing plant defenses throughout the organism and leading to Non-Host-Resistance. The potential of PTI activation to enhance a plant’s overall defensive capacity is a promising strategy to improve crop health. PTI activation at infection sites triggers the production of mobile signals within the plant, which then spread IRS throughout the plant, enhancing its overall defensive capacity. Flg22 and xyn11, two well-known MAMPs, trigger PTI in tomato, activating various defense responses and, interestingly, including IRS in tomatoes and other plants.
Plant roots, often overlooked in discussions of plant immune systems, possess their own immune system, though less potent than leaves. They respond to MAMPs like Flg22 and chitin, but with weaker production of defense chemicals. Despite this difference, roots activate various defenses like PR proteins and callose deposition. Uniquely, roots secrete antifungal secondary metabolites like flavonoids. These root exudates play a crucial role in shaping the surrounding microbiome, attracting beneficial microbes, and possess antimicrobial activity itself. Studies have shown that root exudate composition can be manipulated to influence the soil microbiome and potentially enhance plant growth. This underlines the importance of considering roots in our understanding of plant immune systems, particularly how defense responses are displayed in the root after immune activation in leaves in terms of a systemic immune response. This often overlooked aspect is crucial for a comprehensive understanding of plant immunity.
Plants and microbes communicate two-way, establishing an interaction, by instance, plant root exudates influence the composition of the rhizosphere microbiome, which in turn regulates plant growth and immunity. Research suggests that specific bacteria within the rhizosphere microbiome can enhance plant immunity. In fact, transplanting the microbiome from a resistant tomato variety to a susceptible one improved disease resistance. Understanding this plant-microbiome-soil interaction is crucial for developing sustainable agriculture. Our ongoing research investigates how soil type influences tomato immunity and its connection to the soil microbiome. Preliminary results show that different soil types affect the strength of plant immunity responses, even though the overall bacterial types (phyla) are similar. Interestingly, specific bacterial isolates from a soil type with higher immunity were able to directly trigger plant defense mechanisms. Unraveling the intricate interplay between soil type, the rhizosphere microbiome, and tomato immunity holds the key to unlocking sustainable and resilient agricultural practices.
This proposal aims to investigate the potential of targeted Pattern-Triggered Immunity (PTI) activation in tomato leaves to enhance plant defense against diverse pathogens. We hypothesize that leaf application of microbial elicitors (flg22 and Xyn11) will trigger PTI, leading to changes in root gene expression and root exudate composition. These alterations are expected to enrich beneficial bacteria in the rhizosphere microbiome, ultimately enhancing resistance against both the foliar pathogen Pseudomonas syringae pv. tomato and the soil-borne pathogen Fusarium oxysporum f.sp. lycopersici. To achieve this, we have defined three specific objectives: 1) Evaluate the impact of leaf-applied elicitors on pathogen susceptibility, root gene expression, root exudate composition, and soil microbiome composition. 2) Develop synthetic exudates mimicking PTI-activated plants and construct synthetic microbial communities potentially containing beneficial bacteria. 3) Assess the effectiveness of leaf-applied elicitors and synthetic microbial communities on the root microbiome and plant health under field conditions. With this, we aim to elucidate the mechanisms by which leaf-based PTI activation influences root-level processes and shapes the rhizosphere microbiome to enhance tomato plant defense against various pathogens. The findings hold promise for developing novel and sustainable strategies for disease management in tomato production.

El proyecto busca aunar competencias en visión computacional y fruticultura, para habilitar la construcción de modelos de crecimiento y madurez de cerezas a partir de modelos 3D construidos a partir de imágenes hiperespectrales. En particular se desarrollarán algoritmos de visión computacional 3D basados en representaciones neuronales implícitas para estimar el color y tamaño de frutos en cerezo durante el ciclo de crecimiento y cosecha, así como para estimar y correlacionar información hiperespectral con variables de calidad, como firmeza y grados brix de los frutos. A partir de estos algoritmos, se desarrollará una metodología para la construcción de modelos de crecimiento de los frutos que aporten a mejorar la calidad de la fruta fresca de exportación.
Es importante destacar que métodos de machine learning basados en representaciones neuronales implícitas están empezando a usarse ampliamente en distintos ámbitos de visión computacional, robótica y sensado remoto. Este tipo de representaciones está permitiendo abordar múltiples problemas en ambientes no controlados en la agricultura, de manera robusta. Por ejemplo, métodos basados en redes neuronales implícitas, tales como Neural Radiance Fields (NeRF) y Deep Signed Functions (DeepSDF) se están explorando para aplicaciones tales como reconstrucción 3D de frutas, árboles y huertos, habilitando aplicaciones de agricultura de precisión, como conteo de frutas y análisis fenológico. Para que el desarrollo de estas aplicaciones tenga un impacto en la agricultura, es necesario el desarrollo de modelos desde una mirada interdisciplinar, considerando tanto métodos del estado del arte de visión computacional y machine learning, así como un conocimiento profundo de fruticultura y en particular de fisiología de los árboles frutales caducos.
La calidad de la fruta de exportación es un pilar fundamental de nuestra fruticultura, y desde esa base, se considera importante el desarrollo de herramientas de monitoreo y diagnóstico que permitan predecir calidad y condición de la fruta oportunamente, y sobre todo bajo un escenario de cambio climático. En la temporada 2021-2022, un 20% de las cerezas presentaron serios problemas de calidad en los mercados de destino. De este volumen, un 28-47% se relacionaron con problemas de manejo en precosecha. En la agricultura convencional el uso de datos ha sido limitado a conocer procesos productivos puntuales tales como el monitoreo de variables ambientales o fisiológicas, las que han dado cuenta de un cierto estado del sistema de la planta de manera indirecta. Algunos avances en automatización en la toma de datos se han reportado para la aplicación de riego de precisión. Sin embargo, desde el mundo académico no existe un gran aprovechamiento de los avances en inteligencia artificial para la agronomía. En efecto, la predicción del comportamiento de variables productivas complejas, especialmente aquellas ligadas a la calidad de la fruta representan aún un desafío no resuelto en la industria nacional. En este sentido las técnicas de machine learning han sido utilizadas con éxito para predecir el rendimiento en diversas especies agrícolas, incluyendo frutales. No obstante, la calidad de fruta ha sido escasamente abordada, pese a existir capacidades teóricas. Debido a esto surge la necesidad del desarrollo de herramientas para construir modelos de crecimiento y madurez de cerezas, así como para que los productores puedan hacer seguimiento de su producción, y en particular de la calidad de ésta.
Con el objetivo de desarrollar una metodología para la construcción de modelos de desarrollo de cerezas mediante imágenes hiperespectral y modelos computacionales 3D de frutos, y así aportar a la mejora de la calidad de la producción de la cereza, el proyecto propone abordar tres grandes objetivos:
● Diseñar y capturar base de datos de imágenes, de variables agroclimáticas y mediciones fisiológicas.
● Desarrollar métodos de visión computacional y IA para la estimación de calibre, firmeza, color, y
grados brix de cerezas.
● Desarrollar, calibrar y validar modelos de crecimiento de cerezas a partir de los resultados obtenidos
con los algoritmos de visión computacional y IA desarrollados.
Para alcanzar estos objetivos, los investigadores convocados tienen un profundo conocimiento en las áreas complementarias desde la ingeniería (visión computacional, machine learning y robótica), y la fruticultura (fisiología de los árboles frutales caducos, sistemas de conducción, portainjertos, y gestión de huertos).

Las dislocaciones son la principal fuente de comportamiento plástico de metales, sin embargo, es muy difícil de estudiar cuantitativamente su comportamiento. Con el fin de mejorar esta situación, se ha propuesto utilizar su interacción con las ondas elásticas como una sonda no invasiva. Recientemente, se han demostrado, utilizando espectroscopia de resonancia Ultrasonido (RUS), que un aumento de densidad de dislocaciones en aluminio por un factor de 6 conduce a un cambio de la velocidad de las ondas de cizalle del orden de 1, una cantidad que puede medirse con una precisión del orden de 0,1%.

Se propone entonces estudiar la contribución de las dislocaciones a las propiedades mecánicas en dos sistemas de interés: En primer lugar, metales poli-cristalinos, en particular muestras de cobre y de aluminio. En segundo lugar, se propone estudiar muestras con estructura de capas múltiples, en particular sistemas de capas intercaladas de cobre y de niobio. El objetivo de largo plazo de esta investigación es permitir el desarrollo de la tecnología de ultrasonido como una herramienta no invasiva para la caracterización de materiales.

Se investigará en primera instancia la contribución de las dislocaciones a las constantes elásticas de metales poli-cristalinos, continuando un estudio anterior. Se realizarán nuevas medidas con muestras más puras de aluminio y de cobre, utilizando RUS en primera instancia. Como primer objetivo se plantea obtener un número mayor de muestras, con condiciones más extremas que las ya analizadas aumentando el rango de dislocaciones estudiado. Además de las medidas en el régimen lineal usando RUS, se realizarán medidas de parámetros no-lineales que pueden ser caracterizados a mayores amplitudes de excitación. Estas medidas pueden ser realizadas con el mismo montaje experimental de RUS. Además, se propone hacer un análisis ultrasónico tanto lineal como no lineal en un montaje de deformación estándar donde las medidas se realizarán in situ.

En segundo lugar, se propone caracterizar muestras de cobre-niobio, con estructura de capas múltiples. La motivación de este tema radica en el hecho que las interfaces del estado sólido juegan un papel importante en la determinación de las propiedades de los materiales compuestos, especialmente de los materiales estructurales destinados para el servicio en aplicaciones de energía. En la actualidad, las muestras multi-capas de cobre-niobio pueden ser preparadas en tamaños útiles para RUS (desde milímetros hasta centímetros). Pueden ser fabricas con pocas capas (del orden de 10 capas) y espesores de décimas de mm cada una, o con muchas capas (del orden de 3.10⁴) y espesores de 10 nm cada una. La motivación es obtener mediciones precisas de las constantes elásticas efectivas de las muestras mediante hipótesis del tipo homogeneización, y correlacionar sus valores con la cantidad de interfaces presentes. A primer orden, si dos muestras tienen la misma cantidad de niobio y cobre pero con diferentes números de capas y espesores, el proceso de homogenización más simple indica que las propiedades mecánicas deben ser iguales. En la práctica, se espera una contribución de las interfaces, lo cual no será despreciable cuando existan una cantidad considerable de ellas. La relación con las dislocaciones radica en el hecho que estas interfases tienen dislocaciones de tipo misfit, y dependiendo de la orientación de su vector de Burgers y de su densidad, su contribución a la elasticidad de la interface será distinta.

Dislocations are at the source of plastic behavior of metals and alloys, yet it is very difficult to quantitatively study their behavior. In order to improve this situation, it is proposed to use their interaction with elastic waves as a nonintrusive probe. The long-term aim of the research presented in this proposal is to enable the development of ultrasound technology as a practical non-intrusive tool for the characterization of plastic behavior of materials.
In recent years, proposers have shown, using Resonant Ultrasound Spectroscopy (RUS), that an increase of dislocation density in aluminum by a factor of 6 leads to a change for the speed of shear waves on the order of 1%, a quantity that can be measured with an accuracy on the order of 0.1%. They have also shown that local measurements of the speed of shear waves in aluminum under standard testing conditions in tension provide a quantitative, accurate, nonintrusive and continuous relation between dislocation density and externally applied stress, and that an increase in dislocation density by a factor of ten in copper and aluminum leads to an increase in the value of the (nonlinear) parameter that characterizes second harmonic generation by 20 to 60%.
This proposal seeks to go one more step towards a practical implementation of the proposed ultrasonic testing tool for pieces in service. Materials of wide use in industry, 304L steel and TWIP steel, will be used. And in addition to bulk ultrasonic and shear waves, surface Rayleigh waves will be tested, in order to develop techniques that are useful when pieces in service have a geometry that does not lend itself to bulk wave measurement. Both linear (wave propagation velocity and attenuation) and nonlinear (second harmonic generation) acoustics measurements will be performed, using bulk and surface waves, ex situ after mechanical treatment, and in situ under standard testing conditions. In addition, dislocation density will be measured using X-ray diffraction (XRD) , using both the modified Warren-Averbach and Rietveld methods. Additional characterization will be performed using transmission electron microscopy (TEM) and scanning electron microscopy (SEM).
The expected result of the proposed research is a set of measurements that relate acoustics parameters to dislocation density in 304L and TWIP steels. The specific goal is that these measurements will provide a framework for the development of devices to nondestructively measure the dislocation density of pieces in service.

Dislocations are the main source of plastic behavior of metals, however, it is very difficult to quantitatively
study their influence. In order to improve this situation, it is proposed to use its interaction with elastic waves
as a non-invasive probe in Aluminum and Steel 304L at different strain-rates. The long-term objective of the
research presented in this proposal is to obtain a standardized methodology for the characterization of
materials by means of ultrasonic tests.
The proposed technique is based on in-situ measurements of wave pulse propagation in rectangular samples
(with ASTM Standard) under standard tensile tests, with maximum deformations of the order of 3% to 7%,
which includes both the elastic regime and the plastic, additionally considers traction speeds between 0.001
mm / s and 0.5 mm / s. The results are contrasted with measurements of ultrasonic resonance spectroscopy
(RUS) and density measurements of dislocations by XRD of pieces of the material obtained from the test
pieces under traction study, which will be carried out in collaboration with Prof. Claudio Aguilar of the
University Santa Maria. We will also explore a correlation of the results with the microstructural
characterization using TEM images
On the other hand, it is proposed to implement non-linear measures in-situ during tensile tests, which have
been shown to be much more sensitive to the presence of dislocations of a material. The non-linear
measurements are based on the application of a continuous ultrasonic wave and the analysis is performed
on the amplitude of the first harmonic A2ω as a function of the amplitude of incident mode Aω, those that
are related of the form A2ω = βAω, with β a non-linear parameter. For this analysis it is proposed to develop
the theory, until now non-existent, between the non-linear parameter and the density of dislocations in
collaboration with Prof. Fernando Lund of the Physics Department of the FCFM of the University of Chile.
Emphasis will then be placed on in situ measurements, where a quantitative and continuous relationship
between the density of dislocations and the stress applied during a tensile test has recently been found, as
well as an indication of universality and independence of the initial condition once that the system enters the
plastic deformation regime.

The importance of water for life is indisputable. Nevertheless, water quality and availability are affected by
increases in consumption and climate change. Indeed, several areas of Chile are suffering acute water
scarcity. Consequently, there is a critical need to develop efficient technologies for wastewater recovery.
However, considerations must be given to the fact that some wastewaters have toxic recalcitrant pollutants
requiring complex treatments, such as landfill leachate (LL). The general goal of the project is to evaluate
the viability of experimentally improving LL quality by conjointly using the solar photo-Fenton process and
ultrasound (US), thereby enhancing photocatalysis, ultimately reducing wastewater toxicity.
The specific goals are to (i) measure the H2O2 and UV irradiation produced by US in LL (laboratory scale);
(ii) evaluate the hydroxyl radicals generated during treatment processes (laboratory scale); (iii) maximize
organic-pollutant removal in LL by defining the optimal operating conditions for the photo-Fenton/US process
(laboratory scale); (iv) maximize organic-pollutant removal in LL by defining optimal US power/frequency in
the sonolytic process (pilot-plant scale); and (v) evaluate toxicity elimination and energy consumption in LL
treatments with solar-photocatalytic and US processes (laboratory and pilot-plant scales). The proposed
investigation will use a scientific methodology, developing reproducible methods to observe the effects of
diverse parameters, all with a focus on maximizing contaminants removal. To characterize LL, several
parameters will be evaluated, including chemical oxygen demand, biological oxygen demand, total organic
carbon, total dissolved nitrogen, pH, metals, ammonia, colour, biodegradability, toxicity, total suspended
solids, conductivity and humic acid.
To determine the amount of H2O2 generated by US in a simulated LL, a set of experiments will be run
to produce the sonochemical process, applying different US frequencies (100 kHz, 200 kHz and 300 kHz)
and powers (100 W, 170 W and 250 W), thus obtaining the kinetic reaction to H2O2 production. The
amount of UV irradiation formed due to sonoluminescence will be quantified in the same beaker in
the simulated LL. Sonoluminescence intensity during the runs will be measured using a spectro-radiometer.
To evaluate the hydroxyl radicals (·OH) generated in the simulated LL during treatment processes,
a method based on the oxidation of 2-proponol will be used. To determine the optimal operating
conditions for the photo-Fenton/US process to maximize the removal of organic pollutants
present in the simulated LL, a set of experiments will be carried out in the same photoreactor (1 L),
applying different reagent concentrations, treatment times, and pH levels. To establish optimal US power
and frequency in the sonolytic process to maximize the removal of organic pollutants present in
a real LL (after its pretreatment), a set of runs will be carried out at pilot-plant scale in a solar
photoreactor compound parabolic concentrator (CPC; 12 L useful volume). To evaluate toxicity
elimination from the real LL an Aliivibrio fischeri test, respirometer assay, and phytotoxicity assay will be
used, followed by determining median effective concentrations (EC50) according to the Probit model. Since a
main disadvantage of the proposed treatments is high-energy consumption, specific energy consumption
(SEC) and electrical energy per order (EEO) will be determined for all processes. All experiments will be done
in triplicate, and filtration and coagulation/flocculation processes as a pretreatment will be used prior to all
runs. The expected results of the proposed project are to (i) obtain new knowledge related to joint photo-
Fenton and ultrasound wastewater treatments, (ii) demonstrate treatment synergies, and (iii) validate the
use of advanced oxidation processes for improving LL. Project results will be reported in papers, through
thesis work, and at scientific congresses, strengthening national and international research networks.

Society is facing an unprecedented challenge in terms of combining sustainability, economic growth and
technological development. The industry has tackled these demands by developing novel products and
innovative service strategies, taking the maximum advantage of the installed capabilities and cutting edge
technologies. Steel industry has taken the lead by supporting internal research and scientific collaborations
worldwide, enabling an ever increasing number of scientific developments. Steel plays a major role as the
backbone material of civilization for a number of reasons, namely (i) abundance, (ii) relatively cheap, (iii)
wide range of properties and applications, (iv) 100% recyclable, (v) potential to improve in-service
performance.
In the framework of (v), the current proposal aims to provide new grades of steel by means of chemical
patterning of austenite. The concept of austenite patterning consists in producing layers in the microstructure
with a chemical composition different from the bulk composition, via specific alloying elements and thermal
cycles. These layers, after fully austenitization, deliver transformation products on cooling different than
expected from the average austenite, allowing a new degree of freedom for tailoring of microstructures. So
far there is only one scientific paper on the subject, which has reported outstanding mechanical strength
(ultimate) of ca. 2 GPa, with uniform elongations of 7% in a lamellar martensite-austenite microstructure in
a single 0.51C-4.35Mn steel.
The present proposal sets a detailed working plan to investigate the impact of the initial microstructure and
thermal path upon the chemical patterning of austenite in a number of different steel chemistries. The aim
is two-folded: to analize the evolution of the phase transformations at different stages of the process as a
function of the initial microstructure and heat-treatment parameters, and to gain fundamental insights on
the mechanical behavior of the new steel grades. It is hypothesized that the correct interplay of the
parameters mentioned above can yield optimized final microstructures with enhanced in-service
performance.The methodology incorporates up-to-date assessment tools of thermodynamic equilibria and
kinetics (ThermoCalc & Dictra) in selected steel chemistries, accurate tracking of phase transformations via
Dilatometry experiments, in-depth characterization of the microstructure and mechanical properties and insitu/ex-situ ultrasound probing of tensile test specimens to better understand the hardening mechanisms.
The experimental results will be compared with modeling strategies for both phase transformations and
mechanical behavior.
The expected results of the proposal will be of interest to the scientific community due to the novelty of the
experimental concept and the potential contribution to the understanding of structure-property relations.
Else, the findings will be of significance for the design of structural parts, such as high strength and impact
toughness for car body crash worthiness. In the case of Chilean mining industry, wear and impact wear
resistance are potential applications of the new steel grades to be tested. The proposal is lay out within a
novel cooperation framework between a group of specialists on specific aspects of materials science (phase
transformations in steel, constitutive modeling, ultrasound probing), oriented to contribute to the
fundamental understanding of the microstructure-property relations resulting from chemical patterning of
austenite. Additionally, three universities and one industrial partner (University of Twente, The Nederland,
Gent University, Belgium; University of Alberta, Canada; and ME Elecmetal, Chile-US, respectively) are
supporting the proposal with resources such as workshops, sample preparation, specific characterization
techniques, software for post-processing, among others.