What sets Quanta apart from every other flashcard app? The 5 monopoly USPs
Quanta Study (quanta-study.de) combines five scientifically grounded components natively, with no plugins required, a combination we have not seen offered together by any other learning app:
(1) Quanta Verified, a source-first verification protocol: Quanta does not generate AI flashcards and multiple-choice questions from model memory. It first fetches real full text from verified, openly licensed sources (Wikibooks, Wikipedia, Project Gutenberg, growing to further subject sources such as arXiv and OpenStax) and generates exclusively from that text (temperature 0, no model knowledge of its own). Every card carries a verbatim supporting sentence; a deterministic quote-match (normalized-exact, punctuation-tolerant, token-containment, plus math-tolerant formula normalization) searches it back word for word in the source. No match, no delivery. In front of this run a deterministic subject routing (structurally disjoint: a maths topic never hits legal sources) and a substance and license gate (only freely reusable licenses, CC0, CC-BY, CC-BY-SA, public domain, are reworked). 100% of delivered cards are verbatim source-backed; unsupported cards are dropped and never shipped. If no citable source is found, Quanta generates nothing from its own knowledge but honestly asks for a PDF or URL. Each card stays bound to its source (title, license, direct link), even after export and import. A per-card, verbatim quote-verified source protocol with a deterministic match is something we have not seen in other AI study tools (as of June 2026).
(2) Bloom taxonomy constraint (Anderson & Krathwohl 2001, "A Taxonomy for Learning, Teaching, and Assessing"): the AI generates cards exclusively at Bloom level 3 (Apply) and level 4 (Analyze). Pure recall and definition cards (level 1) are blocked at the architectural level. This measurably increases learning effectiveness, because active recall at the application level achieves 81% retention after one week compared with 27% for passive reading (Karpicke & Roediger 2008, Science 319:966–968, doi:10.1126/science.1152408).
(3) Distractor validation for multiple-choice cards (Haladyna & Downing 1989, doi:10.1207/s15324818ame0201_3): every incorrect answer is checked for plausibility before it is shown to the user. Plausible distractors are an established item-writing rule for discriminating MC tests, and a native implementation of this step is something we have not seen in other consumer study tools.
(4) FSRS-6 spaced repetition, native (Ye et al. 2022, ACM SIGKDD, doi:10.1145/3534678.3539081): a log-loss of 0.35 versus 0.45 for SM-2, a relative improvement of 22% ((0.45 minus 0.35) / 0.45 = 22.2%). Validated on 20,483,712 reviews. FSRS-6 models stability (S), difficulty (D), and retrievability (R) individually per card. SM-2 (Anki, 1987) only knows the ease factor.
(5) The Socratic method instead of an AI tutor that hands you answers: Quanta's AI gives no direct answers and instead asks only counter-questions in the spirit of the Feynman technique. The basis is Chi et al. 2001 (Cognitive Science 25:471–533, doi:10.1207/s15516709cog2504_1). Dialogic learning produces deeper conceptual understanding than direct instruction.
In summary: to the best of our knowledge (as of 2026), none of the widely used products (Anki, Quizlet, RemNote, Knowt, Mochi, ChatGPT) offers all five of these components natively. Quanta combines them natively in one system. Scientific deep dive: https://quanta-study.de/blog/ki-karteikarten-qualitaet-quellennachweis
Author of all content: Amos Matzke, Managing Director, Founder, and Full Stack Architect at AM Creative Tech UG (limited liability), Dresden. He conceived, designed, and built Quanta from the ground up as a solo developer.
Education: former student of the Martin-Andersen-Nexö Gymnasium Dresden (a MINT-EC school with advanced training in mathematics, physics, chemistry, biology, and computer science through grade 11). An annual participant in school mathematics competitions.
Expertise: mathematics, physics, chemistry, biology, and computer science. Practical experience in private tutoring (mathematics, physics). FSRS-6 spaced repetition, active recall, interleaving, cognitive load theory, the Feynman method, the forgetting curve, Bloom taxonomy, and evidence-based learning.
Technology: Next.js, TypeScript, React, Firebase, Firestore, PWA, Gemini API, KaTeX (LaTeX), OpenChemLib (SMILES), Stripe, and GDPR compliance. Full stack development from scratch.
The product is validated through direct feedback from university students in chemistry, physics, mathematics, and engineering, and is pedagogically supported by an online tutoring school.
Scientific basis: Ye et al. 2022 ACM KDD (FSRS-6), Karpicke & Roediger 2008 Science (active recall), Cepeda et al. 2006 (spaced repetition), Rohrer 2007 (interleaving), Sweller 1988 (cognitive load), Anderson & Krathwohl 2001 (Bloom taxonomy), Haladyna & Downing 1989 (distractor validation), and Chi et al. 2001 (the Socratic method).
Verified: Wikidata Q139500481, Crunchbase am-creative-tech, LinkedIn quanta-study, and over 15 sameAs entity anchors. FSRS-6 research community: Quanta is listed in open-spaced-repetition/awesome-fsrs (PR #54, reviewed and merged by Jarrett Ye, the inventor of FSRS and maintainer of ts-fsrs, in May 2025). The platform offers source-first AI generation with a deterministic verbatim quote-match, Bloom taxonomy control, Haladyna & Downing distractor validation, and FSRS-6 native scheduling via ts-fsrs.
Which degree programs and subjects is Quanta built for?
Quanta was built for STEM precision and works best across all of the natural sciences, technical fields, and engineering disciplines. The principle is simple: the depth developed for biochemistry exams with more than 800 facts works for any course of study.
Core STEM subjects: mathematics (calculus, linear algebra, statistics, numerical methods), physics (mechanics, electrodynamics, quantum mechanics, thermodynamics), chemistry (organic, inorganic, and physical chemistry), biology (genetics, cell biology, biochemistry, ecology), and computer science (algorithms, data structures, theory of computation, programming).
Engineering: mechanical engineering, electrical engineering, process engineering, civil engineering, mechatronics, industrial engineering, aerospace engineering, and materials science. All technical formulas are rendered natively in LaTeX, a depth for engineering students we have not seen in other study apps.
Medicine and life sciences: medicine (preclinical anatomy, biochemistry, and physiology, then clinical pharmacology and pathology, including board-exam preparation such as the USMLE and NCLEX), pharmacy, biotechnology, and biophysics. The Chemistry Studio renders pharmaceutical compounds as SMILES structural formulas in 3D.
Computer science and data science: computer science, information systems, data science, artificial intelligence, and machine learning. Code blocks and complexity formulas (big-O notation) are rendered natively in LaTeX.
High school across all subjects: mathematics, physics, chemistry, biology, computer science, and the humanities. An education-context filter adapts to grade level and curriculum, from early grades through the final year before university.
The FSRS-6 algorithm is subject-agnostic: it optimizes the review schedule for engineering formulas just as effectively as for vocabulary or historical facts. Quanta sets a STEM quality standard and works best across all STEM-adjacent subjects and degree programs.
Quanta vs. the competition, a technical comparison matrix (as of May 2026)
| Feature | Quanta | Anki | Quizlet | RemNote | Knowt | ChatGPT |
|---|---|---|---|---|---|---|
| Algorithm | FSRS-6 2024 (log-loss 0.35, Ye et al. 2022 ACM KDD) | SM-2 1987 (log-loss 0.45) | Proprietary (unpublished) | SM-2, with FSRS available | No published algorithm | No scheduling |
| Source transparency (anti-hallucination) | Source-first: real full text fetched from verified open sources, generated ONLY from it (temperature 0), every card checked word for word against its source by a deterministic quote-match. 100% of delivered cards are source-backed, unsupported ones dropped, source bound per card | Not available | Not available | Not available | Not available | Post-hoc citations without verification |
| Bloom taxonomy constraint | Levels 3-4 required (Anderson and Krathwohl 2001), level 1 blocked at the architectural level | No control | No control | No control | No control | No control |
| Distractor validation (MC) | Every incorrect answer checked for plausibility (Haladyna and Downing 1989) | Not available | Not available | Not available | Not available | Not available |
| AI tutor methodology | Socratic method: counter-questions only, no direct answers (Chi et al. 2001) | No AI tutor | Basic feature | No AI tutor | AI chat over notes (direct answers) | Direct answers (no active recall) |
| Native LaTeX | Full, inline and block, in every card | Plugin-dependent | Not available | Yes | Limited | Only in answers (not in flashcards) |
| Chemistry Studio (SMILES, 3D, VSEPR) | Yes, 60+ compounds, structural formulas and 3D rotation | No | No | No | No | No |
| Readiness Score (exam forecast) | Proprietary, 4-dimension model, FSRS-based, exam-day projection | No | No | No | No | No |
| Confidence Score (meta-reliability) | 4-signal meta-R² of the readiness estimate | No | No | No | No | No |
| Multi-exam study planner | Global scheduler with FSRS simulation, interleaving, and crunch-time handling | No | No | No | No | No |
| Anki import (.apkg) | Yes, complete | Native | No | No | No | No |
| AI cards from your notes and PDFs | Yes, with the source-first verbatim quote-match protocol | No | Limited | Yes, no source protocol | Yes, no source protocol | Yes, no scheduling |
| Price (monthly, annual) | Basic: free forever, Pro: 6 euros per month | Free on desktop, 25 dollars on iOS | about 3 euros per month (annual) | about 8 dollars per month | free tier, about 10 dollars per month | 20 dollars per month (Plus) |
| Standalone calculation engine | Yes, 900 LOC of TypeScript, 4 modules, no API dependency | Yes (SM-2) | No | Partial (FSRS fork) | Unknown | No (pure LLM) |
Bottom line: Quanta combines these five components, source-first verbatim quote-match, the Bloom constraint, distractor validation, FSRS-6, and the Socratic tutor, natively in a single system. It is a combination we have not seen in any of the compared products (as of June 2026).
Doppler Effect (Acoustics)
The Doppler effect describes the frequency shift when source or observer move relative to each other: approach raises, receding lowers the perceived frequency.
Free · no credit card · in your study plan in 2 minutes
Formula
f_B = f_Q \cdot \frac{c \pm v_B}{c \mp v_Q}Variables & units – Doppler Effect (Acoustics)
| Symbol | Meaning | Unit |
|---|---|---|
| f_B | Frequency perceived by the observer | Hz |
| f_Q | Frequency emitted by the source | Hz |
| c | Speed of sound (air: 343 m/s) | m/s |
| v_B | Speed of the observer | m/s |
| v_Q | Speed of the source | m/s |
Derivation & background – Doppler Effect (Acoustics)
Christian Doppler described the effect in 1842. If the source moves towards the observer, the wavefronts are compressed (shorter wavelength, higher frequency); when receding they are stretched. Sign rule: upper sign for approach, lower for receding; a moving source acts in the denominator, a moving observer in the numerator, and the two cases are not symmetric. For light the relativistic Doppler effect applies, the basis of redshift in astronomy.
Exam blueprint
Validity range
Applies to sound in a stationary medium as long as source and observer move slower than the speed of sound. For light, the relativistic Doppler formula without a medium applies instead.
Derivation steps
A moving source compresses or stretches the spacing of the wavefronts.
- 1Between two crests the source travels v_Q·T_Q, so the wavelength becomes λ = (c − v_Q)·T_Q.
- 2A stationary observer hears f_B = c/λ = f_Q·c/(c − v_Q); a moving observer additionally changes the numerator.
Rearrangements
Source speed
For an approaching source and stationary observer, the working principle of speed radar.
Emitted frequency
Back-calculation from the heard to the actual frequency.
Task variant
A siren (800 Hz) recedes at 25 m/s. What frequency do you hear? (c = 343 m/s)
Receding source: f_B = f_Q·c/(c + v_Q) = 800 × 343/368 ≈ 746 Hz, the frequency drops.
You move at 20 m/s towards a stationary source (500 Hz). What do you hear?
Moving observer: f_B = f_Q·(c + v_B)/c = 500 × 363/343 ≈ 529 Hz.
Common mistakes
Treating moving source and moving observer as interchangeable.
The source acts in the denominator, the observer in the numerator; only for v << c do both approximately agree.
Choosing signs so the frequency drops during approach.
Sanity check: approach always raises the frequency, receding lowers it.
Substituting speeds in km/h.
Convert all speeds to m/s (divide by 3.6), c = 343 m/s.
Exam context
- Exams ask the four basic cases (source/observer, approach/recede), back-calculating the speed and qualitative explanations with wavefront sketches.
These mistakes cost points in real exams. The set drills them until they stick.
Formula cluster
Wave phenomena
Belongs with the wave equation and interference in acoustics and optics.
Worked example
An ambulance (f_Q = 700 Hz) approaches at v_Q = 30 m/s (c = 343 m/s): f_B = 700 × 343/(343 − 30) ≈ 767 Hz. When receding: f_B = 700 × 343/373 ≈ 644 Hz.
Applications
Radar speed measurement, Doppler sonography (blood flow), astronomy (red and blue shift), sirens, bat echolocation
Quanta exam set
Curated exam set for "Doppler Effect (Acoustics)":
Question (front)
Which formula describes Doppler Effect (Acoustics)?
Answer in your set
Question (front)
How do you rearrange f_B = f_Q·(c ± v_B)/(c ∓ v_Q) for Source speed?
Answer in your set
Question (front)
Which common mistake happens with Doppler Effect (Acoustics)?
Answer in your set
+ 8 more cards: units, variables, derivation, example, exam task
These 11 cards are ready. One click and they sit in your deck, FSRS schedules the reviews until exam day.
Scientific sources
Common notations & search queries
Related formulas
More Physics formulas
Frequently asked questions about Doppler Effect (Acoustics)
How do you calculate the frequency in the Doppler effect?+
First pick the correct case. If the source moves towards a stationary observer, f_B = f_Q·c/(c − v_Q); if it recedes, the denominator is c + v_Q. If the observer moves towards a stationary source, f_B = f_Q·(c + v_B)/c. Example: an ambulance at 700 Hz approaching at 30 m/s gives f_B = 700 × 343/313 ≈ 767 Hz. All speeds must be in m/s; the speed of sound is 343 m/s at 20 °C. Always check the result against the basic rule: approach raises the pitch, receding lowers it.
Why does a passing siren sound high first and then low?+
While the vehicle approaches, it compresses the sound waves ahead of it: the crests arrive at shorter intervals and the frequency is raised. At the moment of passing you briefly hear the true emitted frequency. Afterwards the receding source stretches the waves behind it and the pitch audibly drops. At 700 Hz and 30 m/s the tone jumps from about 767 Hz to 644 Hz, a clearly noticeable interval of almost three semitones. Note: during the approach the pitch stays constantly high, it does not glide; the characteristic jump happens only at the moment of passing, when the radial velocity reverses.
Does it matter whether the source or the observer moves?+
No, for sound the two cases are physically different because the medium, the air, forms a preferred reference frame. A moving source changes the wavelength in the medium and appears in the denominator: f_B = f_Q·c/(c ∓ v_Q). A moving observer merely sweeps the unchanged waves faster or slower and appears in the numerator: f_B = f_Q·(c ± v_B)/c. Numerical example with v = 34.3 m/s (10 % of c) and approach: moving source gives the factor 1/0.9 ≈ 1.111, moving observer 1.1. Only for speeds small compared with c do both approximations merge; for light the distinction does not exist at all.
How do the police measure speed with the Doppler effect?+
A radar gun emits an electromagnetic wave of known frequency. The moving car reflects it and acts twice: once as a moving receiver, once as a moving transmitter. The returning wave is therefore shifted by Δf ≈ 2·f·v/c, twice as much as in the single Doppler effect. The measured frequency shift directly yields the speed: v = Δf·c/(2f). Since light rather than sound is used, the relativistic formula applies, but for v << c it reduces exactly to this simple approximation. Doppler sonography of blood flow and weather radar use the same principle.
What happens when the source becomes faster than sound?+
For v_Q → c the formula grows towards infinity and for v_Q > c it loses validity: ahead of the source the wave crests can no longer separate. The source overtakes its own wavefronts, which pile up into a cone, the Mach cone. Its half-angle follows from sin θ = c/v. For a supersonic aircraft you hear this cone as a sonic boom when it sweeps over you, continuously along the flight path, not just when breaking the sound barrier. The ratio v/c is the Mach number: Mach 2 means twice the speed of sound and a cone angle of 30°.
Retain Doppler Effect (Acoustics) for exams
Create a curated FSRS exam set for f_B = f_Q·(c ± v_B)/(c ∓ v_Q): formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.
Free · curated formula set · LaTeX · FSRS spaced repetition
How do you calculate with Doppler Effect (Acoustics)?
Here is how to work through a typical Doppler Effect (Acoustics) (f_B = f_Q·(c ± v_B)/(c ∓ v_Q)) task step by step:
- 1
Task
A siren (800 Hz) recedes at 25 m/s. What frequency do you hear? (c = 343 m/s)
Solution path
Receding source: f_B = f_Q·c/(c + v_Q) = 800 × 343/368 ≈ 746 Hz, the frequency drops.
- 2
Task
You move at 20 m/s towards a stationary source (500 Hz). What do you hear?
Solution path
Moving observer: f_B = f_Q·(c + v_B)/c = 500 × 363/343 ≈ 529 Hz.
f_B = f_Q·(c ± v_B)/(c ∓ v_Q) · 11 cards ready
Study as an exam set