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.

It is proposed to assess the feasibility of using ultrasound as a nonintrusive, in-situ, probe of plastic behavior
in high-entropy alloys (HEAs). More specifically, whether it is possible to use ultrasound to reliably
characterize the plasticity deformation mechanism—slip, TWIP, TRIP—of Fe80-xCo10Cr10Mnx. To this end,
the speed of sound will be measured, continuously, as a function of applied stress in uniaxial tensile tests.
In previous work, proposers have shown that the speed of sound as a function of stress provides a reliable
tool to measure dislocation density in aluminum, copper, and stainless steel. In the latter case, it has also
been shown to reliably discriminate between slip and twinning as a deformation mechanism. It is now
proposed to study the possibility of extending this capability not only to new materials, HEAs, but also to a
new mechanism, phase transformation.
We will start with the materials whose plastic deformation is slip-dominated, since we have robust experience
in this case. We shall then move to the TWIP material, where our more recent experience will be brought to
bear, to end up with the unexplored, from the point of view of ultrasound, TRIP material. Samples for tensile
loading will be prepared. They will be tested using a universal testing machine and ultrasound measurements
of longitudinal wave velocity will be carried out in-situ. A decrease in the wave velocity as a function of
applied stress will indicate a proliferation of dislocations; the dislocation density will be determined as a
function of stress as will the parameters of Taylor’s rule. An increase in wave velocity as a function of stress
will indicate a decrease in average grain size. Modeling will be applied to determine whether this is due to
twinning or phase transformation. These results will also be validated with post-mortem XRD, TEM, and
metallography measurements, as well as ex-situ acoustic measurements.
The success of the proposed research would have short-term and long-term benefits: In the short term it
would provide a non-intrusive tool—ultrasound—to assist in the search for HEAs with pre-determined
properties, as needed for specific applications. In the long-term, it would pave the way for the development
of a practical, non-intrusive, tool for the evaluation of HEA pieces in service.

This project aims to investigate how structural modifications of a dicationic derivative of azobenzene can
affect the drug release and load capacity of its photoactive molecular aggregate.
To evaluate this, three types of structural modifications are proposed. First, the introduction of functional
groups on the photoactive nucleus of dicationic azobenzene is expected to shift the absorption band of the
molecular photoswitch.
Second, the replacement of the fluorescent organic cations over the structure of the molecular
photoswitch, which confer luminescent and amphipathic properties to the system.
And third, the modification of the length of the chains over the molecular photoswitch could change the
aggregate size.
To determine whether these potential modifications can modulate the light-induced release activity of the
photoswitchable aggregate, an enzyme inhibitor will be loaded and released by illumination in the presence
of the enzyme.
Under this scenario, any modification of the enzymatic activity will be correlated with the drug’s
photorelease.

Combinatorial objects frequently appear in various areas of computer science and discrete
mathematics. These objects are central to questions in algorithmic design, where we aim to program a
computer to efficiently perform tasks involving them. These tasks may include counting objects based on
certain parameters, sampling an object uniformly at random, optimizing with respect to an objective
function, searching for objects that satisfy specific properties, or generating all objects exactly once. This
project focuses on two of these problems: combinatorial generation and the search for highly distinct
combinatorial objects.
While many of the aforementioned tasks have general-purpose techniques that allow them to tackle
multiple problems simultaneously, the situation becomes less clear when dealing with combinatorial
generation or the search for distant objects. Much of the effort in these areas has been devoted to
developing ad hoc methods. Despite this, these last two problems can be naturally phrased in the language
of flip graphs, which encode the similarity between combinatorial objects. In this context, the problem
transforms into the traditional graph problems of Hamiltonicity (finding a path that traverses all the
vertices exactly once) and diameter (finding two vertices that are farthest apart). Recent research has
highlighted the significant value of exploiting polytopal properties and symmetry of flip graphs, leading to
unified frameworks that can address many problems simultaneously. The main objective of this project is
to contribute to this perspective. Specifically, it aims to enhance our understanding of the polytopal and
symmetric properties of flip graphs and use this knowledge to develop efficient algorithms for tackling
Hamiltonicity and diameter problems

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).

Métodos basados en representaciones neuronales implícitas están empezando a usarse ampliamente en distintos ámbitos de visión computacional, robótica y sensado remoto. Este tipo de representaciones están permitiendo abordar múltiples problemas en ambientes no controlados como la agricultura, de manera robusta. Por ejemplo, métodos basados en Neural Radiance Fields (NeRF) se están explorando de manera amplia tanto con imágenes satelitales como en problemas de robótica de campo. En este contexto, el proyecto busca aunar competencias en visión computacional y aprendizaje de máquinas, usadas en la detección remota y en robótica, para abordar nuevas técnicas basadas en representaciones neuronales implícitas, para aplicaciones de la agricultura de precisión. Para lograr este objetivo, los investigadores convocados tienen un profundo conocimiento en estas áreas complementarias.
Es importante destacar que las áreas de sensado remoto (satelital y drones) y sensado próximo (robots y redes de sensores) están experimentando una aceleración sin precedentes. En el caso de sensado remoto, además de los grandes programas públicos como Sentinel, los actores privados están creando flotas de microsatélites capaces de vigilar la Tierra con revisitas diarias. Estos datos abundantes, baratos y de alta resolución están creando oportunidades para desarrollar aplicaciones novedosas para la supervisión de la actividad agrícola. En el caso del sensado próximo, las redes de sensores, junto con el uso de robots para monitoreo, está permitiendo un seguimiento regular de los procesos agrícolas, con una alta resolución temporal y espacial, por lo que cada vez hay una mayor disponibilidad de datos, que complementan los datos obtenidos mediante sensado remoto.
A nivel de uso, estas tecnologías se complementan, y a nivel de investigación, las técnicas utilizadas están empezando a converger, mediante el uso de métodos basados en redes neuronales, y más específicamente por métodos basados en representaciones neuronales implícitas, tales como Neural Radiance Fields (NeRF). Por todo esto, el estudio del sensado remoto y próximo de manera conjunta, y mediante marcos de trabajo con técnicas similares como las representaciones neuronales implícitas, tiene un gran potencial para en un futuro próximo generar una visión integrada de los procesos agrícolas mejorando la sostenibilidad y eficiencia en la agricultura.
Durante su ejecución, el proyecto llevará a cabo actividades de investigación conjunta, incluyendo seminarios online regulares, la toma de datos en terreno, y un workshop de cierre en el contexto de una conferencia internacional, que junto con el intercambio de investigadores en formación (magíster, doctorado y/o postdoctorado), así como visitas de investigadores senior, buscan articular una de red de trabajo que aborde de manera interdisciplinar y con técnicas modernas, problemáticas de sensado remoto y próximo en agricultura de precisión mediante representaciones neuronales implícitas, tales como Neural Radiance Fields (NeRF), entre otras.

Evaluación del potencial de captura de carbono en relaves mineros de la Mina El Teniente, Chile