Learning objectives
Knowledge and understanding:
a) Become aware of how wide and various are the areas in which Materials Science develops.
b) Acquire the basic concepts of probability calculus, the main statistical distributions and their properties and the main statistical methods for data processing.
c) Discuss basic physics topics in order to design and manage their experimental verification.
Applied knowledge and understanding:
a) Acquire the conviction that it is possible to invent new materials to respond to the increasingly pressing demands of innovative technology.
b) Plan simple physics experiments, evaluate and deal with statistical and systematic measurement errors.
c) Have a good familiarity with the different measurement methods and the ability to statistically process and analyze the results of the measurements, also by means of appropriate IT tools that allow for example their graphical representation.
d) Summarize with reports methods, measurements, analysis of the results and conclusions of the experiments.
Communication skills:
a) Knowing how to describe, to present and critically to discuss the experiment results obtained by objective measurement in the technical-scientific language.
b) Knowing how to communicate the acquired knowledge even with simple and understandable language to a non-specialist public.
Ability to learn:
a) To interpret and to understand basic texts on simple subjects of experimental physics.
b) To use the methodological tools of experimental physics as a prerequisite for understanding, planning, conducting and analyzing the experiments that will be addressed in subsequent laboratory courses.
c) Start to modify the own actions according to a practical and flexible mentality, capable of knowing how to operate even in new contexts.
Prerequisites
Knowledge of some fundamentals of elementary mathematics: algebra, trigonometry, elements of analytical geometry, elements of differential and integral calculus.
Please note that for all newly enrolled students it is compulsory to attend a training course on safety in the workplace. Details on the on-line training course are communicated by the Didactic Secretariat at the beginning of the academic year: in the absence of attendance certificates, access to the teaching laboratories may be denied.
Course unit content
Materials science was born as a fusion between physics, chemistry, mineralogy and engineering, with the aim of developing new materials required by advanced technologies. The first part of the course, which takes place in the first semester, (3 CFU), is aimed at the student who wishes to make contact with this discipline.
Starting from some notable examples, which historically have led to technological successes, we go deeper and deeper into the innovative characteristics of the materials, designed to meet the specific requests of technology. This part, purely descriptive, helps to show the potential and the role that materials science has and has had in modern society and in our everyday life.
The second part of the course, which takes place in the second semester, (6 CFU) concerns an introduction to experimental physics. Starting from the evaluation of experimental errors, instrumental and not, the knowledge of the measurement methodologies is built, allowing to manage with autonomy simple laboratory experiments.
The experiences will be proposed in such a way that it is the student to design the best method to carry out the measurements of physical quantities, taking into account the type of measurement and the instrumentation. Particular attention will be given to the design of the measure, the laboratory notebook and the compilation of the final scientific report.
Full programme
I semester
1. Materials: what they are and why study them.
Difference between Science and Technology of materials.
Some examples out of the ordinary: the compass of the Vikings, the katana, the vase of Lycurgus, the gunpowder.
2. Properties of natural and synthetic materials.
Physico-chemical, mechanical and technological.
3. Crystalline and amorphous materials.
Metals, superconductivity, shape memory alloys and self-assembling.
Semiconductors, electronics and optoelectronics, energy.
Insulators, sensors (light and gas), ceramics (thermal), glasses (amorphous, intelligent).
4. Smart and functional materials.
Polymers, natural, artificial, synthetic (linear and branched).
Composite materials, design, phases and properties.
5. Biomaterials.
Physico-chemical properties and biocompatibility.
II semester
1. The measure.
Direct and indirect measurements, physical quantities, units of measurement, characteristics and criteria for choosing measurement instruments, sensitivity, precision, promptness, dynamics; systematic and random errors, confidence intervals; orders of magnitude and significant figures.
2. Study of uncertainties in measurements.
Error propagation (sum, difference, product, quotient, quadrature sum, function of one and two variables); error as a differential. Measurement errors and their representation: confidence interval, significant figures, consistency / discrepancy between measurements, verification of physical laws.
3. Study of uncertainties in measurements.
Statistical data processing and their representation; statistical analysis of random errors: mean and measure of deviation, variance and standard deviation; histograms and frequency distributions. Cumulative frequency. Outline of the treatment of systematic errors.
4. Study of uncertainties in measurements.
Frequency and probability, limit distribution, probability density; normalization, mean value and standard deviation. Normal distribution: confidence and standard deviation, normal integral of errors; comparison of results. Average for best estimate. Population distributions.
5. Study of the uncertainties in physical measurements.
Weighted averages, data rejection (Chauvenet criterion); outline of the least squares method and regressions.
6. Introduction to probability theory.
Statistics and probability, discrete and continuous variables, the concept of event; favorable cases and possible cases, classical and frequentistic definition of probability.
Laboratory experiences
1) Measurement instruments, analog and digital
2) Density of solids
3) Calibration of a thermocouple
Bibliography
1. J.R. Taylor, Introduzione all'Analisi degli Errori, Ed. Zanichelli, Bologna, 2° ed., 2000.
2. M. Loreti, Teoria degli errori e fondamenti di statistica.
3. Additional material provided by the teacher.
Teaching methods
The teaching activities are divided into classroom lessons and practical laboratory activities. The course is 9 CFU (3 in the first semester and 6 in the second semester). Classroom lessons are 3 credits which correspond to a total of 24 hours of activity. The practical laboratory activity is of 6 credits which correspond to a total of 72 hours of activity in the laboratory. The slides used to support classroom lessons will be uploaded on a weekly basis on the Elly platform. To download the slides, registration to the online course is needed.
Assessment methods and criteria
The final evaluation consists of an on-going assessment and a final oral examination.
- Ongoing: at the end of the first semester, a written examination will be carried out on the topics covered in the first part of the course and will be evaluated by means of a grade starting from 15 up to 30.
- the final examination will include the evaluation of group reports on the activity carried out in the laboratory (1 for each experiment) which will be evaluated through a 0-30 scale judgment.
If, at the end of the course, the overall evaluation of the reports is not sufficient, practical laboratory tests may be proposed, as appropriate, to complement the oral examination.
- final oral interview (evaluation on a 0-30 scale) in which the student will have to discuss some of the experiences and answer questions on the theoretical part.
The final evaluation will result from the weighted average of the judgments acquired in the ongoing test, in the reports carried out by the group and the outcome of the exam interview (25%, 25%, 50%).
Learning evaluation will be focused on the assessing expected outcomes, in accordance with the Dublin descriptors. The final grade will be given out in thirtieths and will vary from 18/30 to 30/30 with honors. The objective of the exam is to verify the level of achievement of the knowledge, skills and abilities indicated. The vote will be expressed, according to the following evaluation scheme:
- Excellent (30-30 cum laude): Excellent knowledge and understanding of the treated topics. Excellent ability to apply the knowledge acquired to solve the proposed exercises and in addressing new problems. Excellent presentation capabilities.
- Very good (27-29): Good knowledge and understanding of the topics covered. Good ability to apply the acquired knowledge to solve the proposed exercises and to face new problems. Excellent presentation capacity.
- Good (24-26): Good knowledge and understanding of the topics covered. Discreet ability to apply the acquired knowledge to solve the proposed exercises and to face new problems. Good presentation capacity.
- Discreet (21-23): Discreet knowledge and understanding of the topics covered. Limited ability to apply the acquired knowledge to solve the proposed exercises and to face new problems.
- Sufficient (18-20): Minimum knowledge of the topics covered and limited ability to apply the acquired knowledge to solve the proposed exercises.
- Insufficient (<18): Lack of an acceptable knowledge of the treated topics and / or the student does not demonstrate sufficient ability to apply the acquired knowledge to solve the exercises.
Other information
The active participation of the student in the laboratory experiments is an essential and indispensable part of the course, as well as of the evaluation process. In presence of particular conditions (for example, in case of working students), the opportunity to create personalized courses can be evaluated. In order to access the final evaluation, participation in 70% of laboratory activities is required. If absences completely preclude participation in one or more experiments, remedial activities can be evaluated.
2030 agenda goals for sustainable development
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