B2066 - SPETTROSCOPIA AD ALTA RISOLUZIONE

Learning outcomes

At the end of the course, the student is familiar with the experimental techniques used to record high-resolution molecular spectra. They are able to interpret the spectra based on quantum mechanical models that describe the investigated molecular system. Finally, they have an understanding of the application areas of high-resolution spectroscopy.

Course contents

Regular attendance in both lectures and laboratory sessions is essential for achieving the learning outcomes of the course.

• Elements of Python

1. Introductory tutorial on the Python programming language
2. Development of algorithms and Python scripts for implementing the course topics

• Molecular vibrations

1. Diatomic molecules
2. Normal modes
3. Anharmonicity
4. Inversion mode in Ammonia

• Molecular rotations

1. Diatomic molecules
2. Angular momentum
3. Poliatomic rigid rotor
4. Rotational spectrum

• Rotation a vibration

1. Diatomic molecules
2. Rotational spectrum of simmetric-top molecules
3. Inversion spectrum of Ammonia
4. Laboratory exercise: FTIR spectrum of HCN, FTIR inversion spectrum of ammonia and estimate of its inversion barrier

Readings/Bibliography

The main reference material consists of lecture notes provided by the instructor, which include the key bibliographic references supporting the topics covered. The lecture notes are made available in PDF format on the Virtuale platform.

Teaching methods

The course comprises 5 ECTS (40 hours) of face‑to‑face instruction in lecture halls and computer labs, covering the theoretical principles of high-resolution molecular spectroscopy, with a focus on rotational and vibrational transitions and the quantum-mechanical models that describe them. These lectures are complemented by guided practical sessions in which students implement the theoretical models using Python scripts.
In addition, 1 ECTS (12 hours) of laboratory exercises is planned, aimed at the direct acquisition of spectroscopic data and their subsequent processing.
The teaching approach is designed to foster an integrated development of theoretical and practical skills, alongside analytical thinking and operational autonomy.

Regarding Health and Safety Training, students are required to attend e-Learning Modules 1 and 2. The additional Module 3 is mandatory for this learning activity.

Assessment methods

The final assessment consists of an oral exam, approximately one hour in length, aimed at verifying the acquisition of the expected knowledge and skills. The exam includes:

1. discussion of the laboratory report;

2. in-depth exploration of a topic, either through the development of a Python script or based on materials from the lecture notes appendices;

3. response to a main question on topics covered in the course.

Evaluation is based on content mastery, clarity of presentation, the ability to link theory and practice, and autonomy in discussion. The grading criteria are as follows:

• Sufficient (18–20): basic knowledge; mostly correct expression, but uncertain and not well structured.

• Good (21–24): solid understanding; overall adequate explanation, though mostly rote and with limited ability to connect topics.

• Very good (25–27): strong command of content; well-structured presentation with analytical ability.

• Excellent (28–30L): complete and in-depth knowledge; rigorous, independent and critical discussion. Honors are awarded in cases of outstanding performance.

Students with learning disorders and/or temporary or permanent disabilities: please, contact the office responsible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

Teaching tools

Teaching materials (lecture notes, Python tutorials, lab instructions) are made available on the Virtuale platform. Practical sessions use Colab Notebooks for model implementation and data analysis. In-person activities make use of projectors, whiteboards, and computer. Laboratory exercises include the use of an FTIR spectrometer and involve the preparation and handling of gas-phase samples for data acquisition.

Office hours

See the website of Luca Dore